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Abu-Freha N, Afawi Z, Yousef M, Alamor W, Sanalla N, Esbit S, Yousef M. A machine learning approach to differentiate stage IV from stage I colorectal cancer. Comput Biol Med 2025; 191:110179. [PMID: 40220595 DOI: 10.1016/j.compbiomed.2025.110179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 04/04/2025] [Accepted: 04/07/2025] [Indexed: 04/14/2025]
Abstract
BACKGROUND AND AIM The stage at which Colorectal cancer (CRC) diagnosed is a crucial prognostic factor. Our study proposed a novel approach to aid in the diagnosis of stage IV CRC by utilizing supervised machine learning, analyzing clinical history, and laboratory values, comparing them with those of stage I CRC. METHODS We conducted a respective study using patients diagnosed with stage I (n = 433) and stage IV CRC (n = 457). We employed supervised machine learning using random forest. The decision tree is used to visualize the model to identify key clinical and laboratory factors that differentiate between stage IV and stage I CRC. RESULTS The decision tree classifier revealed that symptoms combined with laboratory values were critical predictors of stage IV CRC. Change in bowel habits was predictive for stage IV CRC among 14 of 22 patients (63 %). Weight loss, constipation, and abdominal pain in combination with different levels of carcinoembryonic antigen (CEA) were predictors for stage IV CRC. A CEA level higher than 260 was indicative for stage IV CRC in all observed patients (61 out of 61 patients). Additionally, a lower CEA level, in combination with hemoglobin, white blood cell count, and platelet count, also predicted stage IV CRC. CONCLUSIONS By applying a machine learning based approach, we identified symptoms and laboratory values (CEA, hemoglobin, white blood cell count, and platelet count), as crucial predictors for stage IV CRC diagnosis. This method holds potential for facilitating the diagnosis of stage IV CRC in clinical practice, even before imaging tests are conducted.
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Affiliation(s)
- Naim Abu-Freha
- Institute of Gastroenterology and Hepatology, Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Zaid Afawi
- Clalit Health Services, Southern District, Beer-Sheva, Israel
| | - Miar Yousef
- Lady Davis Carmel Medical Center, Haifa, Israel
| | - Walid Alamor
- Internal Medicine Department, Soroka University Medical Center, Beer-Sheva, Israel
| | - Noor Sanalla
- Internal Medicine Department, Soroka University Medical Center, Beer-Sheva, Israel
| | - Simon Esbit
- Medical School for International Health, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Malik Yousef
- Department of Information Systems, Zefat Academic College, Zefat, Israel; Galilee Digital Health Research Center, Zefat Academic College, Zefat, Israel
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Kanaan S, Altamimi A, Qattous H, Rbeihat H. Enhanced non-invasive machine learning approach for early colorectal cancer detection: Predictive modeling and validation in a Jordanian cohort. Comput Biol Med 2025; 191:110184. [PMID: 40249989 DOI: 10.1016/j.compbiomed.2025.110184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 01/16/2025] [Accepted: 04/08/2025] [Indexed: 04/20/2025]
Abstract
BACKGROUND Colorectal cancer (CRC) ranks as the third most prevalent cancer worldwide, posing significant public health challenges. Late-stage detection often results in poor treatment outcomes, elevating mortality rates. The economic and psychological burdens of CRC treatment underscore the need for early detection. OBJECTIVE This study aims to enhance the early detection of colorectal cancer by employing machine learning (ML) algorithms on non-invasive features. The focus is on constructing a comprehensive dataset, analyzing non-invasive features, and developing predictive models to minimize the necessity for invasive procedures such as colonoscopy. By focusing on non-invasive, easily accessible data, the study aims to develop a model that can be widely applied without the associated risks of invasive procedures. METHODS A retrospective dataset of 400 patients was sourced from the colorectal cancer unit of Royal Medical Services (2021-2022). The dataset included demographic data, imaging reports, laboratory results, and clinical evaluations. The study involved three experiments, training ML models (K-Nearest Neighbors (KNN), Super Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Naïve Bayes (NB)) on the collected dataset and a public dataset to validate generalizability. The first experiment used 35 features across the ML algorithms. The second experiment focused on the most informative features. The third experiment validated the models using a public dataset, with Phase I including all data and Phase II excluding missing values. RESULTS The Random Forest (RF) algorithm consistently outperformed other models, achieving an accuracy of 95.8 % in the first experiment, increasing to 96.5 % in the second experiment. For the public dataset, RF accuracy was 66.0 % in Phase I and 68.9 % in Phase II. Conversely, the KNN algorithm exhibited the lowest accuracy across all experiments. CONCLUSION This study highlights the effectiveness of ML in early CRC detection using non-invasive techniques. The RF model demonstrated superior accuracy, suggesting its potential application in clinical settings. The research contributes valuable insights into CRC detection within the local context and emphasizes the broader applicability of ML in improving cancer diagnosis and personalized treatment.
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Affiliation(s)
- Soha Kanaan
- Princess Sumaya University for Technology,(PSUT), Amman, Jordan.
| | - Ahmad Altamimi
- Department of Software Engineering, Princess Sumaya University for Technology (PSUT), Amman, Jordan
| | - Hazem Qattous
- Department of Software Engineering, Princess Sumaya University for Technology (PSUT), Amman, Jordan
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Zheng X, Li Y, Wu Z, Tang Y, Lai PY, Chen MS, Chen HY, Wang CD, Li J, Dai Q. Interpretable Staging Prediction of Liver Cancer Based on Joint-Knowledge Network. IEEE J Biomed Health Inform 2025; 29:2993-3006. [PMID: 40030475 DOI: 10.1109/jbhi.2024.3509858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2025]
Abstract
Clinical staging is crucial for treatment strategies and improving 5-year survival rates in hepatocellular carcinoma (HCC) patients. However, existing methods struggle to distinguish stages with highly similar textual features. Additionally, their lack of interpretability hampers their practical application in medical scenarios. Here, we introduce KnowST, a joint-knowledge network designed to leverage task relevance to explore implicit knowledge for interpretable staging prediction of liver cancer. First, the relevance of auxiliary tasks and the main task is established from two perspectives to guide the model's focus on staging-related implicit knowledge in radiology reports. Stages-to-stages: KnowST learns the inter-stage distinctions between different stages and the similarities within the same stages, using these as important references for staging differentiation. Factors-to-stages: Clinically, staging is determined by multiple tumor factors. These factors can serve as effective clues to assist KnowST in predicting the correct stage, especially in the case of confusing stages. Second, domain-specific word embeddings are introduced to bridge the gap between pre-trained language models and Chinese radiology reports. Lastly, tumor factor prediction enhances the credibility of the deep model in staging prediction, and its visualized results effectively demonstrate the model's interpretability. Overall, KnowST leverages the joint-knowledge from these two perspectives, effectively utilizing implicit information in radiology reports to achieve interpretable clinical staging. Compared to the optimal baselines, KnowST improves AUC by 7.69% and achieves 90.52% accuracy on 573 real-world radiology reports, while also demonstrating superior stage identification and stable performance across various metrics.
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Li X, Zhang X, Yin S, Nie J. Challenges and prospects in HER2-positive breast cancer-targeted therapy. Crit Rev Oncol Hematol 2025; 207:104624. [PMID: 39826885 DOI: 10.1016/j.critrevonc.2025.104624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 12/29/2024] [Accepted: 01/15/2025] [Indexed: 01/22/2025] Open
Abstract
Breast cancer remains the most prevalent malignancy among women globally and ranks as the leading cause of cancer-related mortality in this demographic. Approximately 13 %-15 % of all breast cancer cases are classified as HER2-positive, a subtype associated with a particularly unfavorable prognosis. A large number of patients with HER2-positive breast cancer continue to face disease progression after receiving standardized treatment. Given these challenges, a thorough exploration into the mechanisms underlying drug resistance in HER2-targeted therapy is imperative. This review focuses on the factors related to drug resistance in HER2-targeted therapy, including tumor heterogeneity, antibody-binding efficacy, variations in the tumor microenvironment, and abnormalities in signal activation and transmission. Additionally, corresponding strategies to counteract these resistance mechanisms are discussed, to advance therapeutic efficacy and clinical benefits in the management of HER2-positive breast cancer.
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Affiliation(s)
- Xiyin Li
- Department of Breast Cancer, Peking University Cancer Hospital Yunnan, Yunnan Cancer Hospital, the Third Affiliated Hospital, Kunming Medical University, 519 Kunzhou Road, Kunming, Yunnan 650118, China.
| | - Xueying Zhang
- Department of Breast Cancer, Peking University Cancer Hospital Yunnan, Yunnan Cancer Hospital, the Third Affiliated Hospital, Kunming Medical University, 519 Kunzhou Road, Kunming, Yunnan 650118, China.
| | - Saige Yin
- Department of Anatomy and Histology and Embryology, Faculty of Basic Medical Science, Kunming Medical University, Kunming, Yunnan 650118, China.
| | - Jianyun Nie
- Department of Breast Cancer, Peking University Cancer Hospital Yunnan, Yunnan Cancer Hospital, the Third Affiliated Hospital, Kunming Medical University, 519 Kunzhou Road, Kunming, Yunnan 650118, China.
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Naemi A, Tashk A, Sorayaie Azar A, Samimi T, Tavassoli G, Bagherzadeh Mohasefi A, Nasiri Khanshan E, Heshmat Najafabad M, Tarighi V, Wiil UK, Bagherzadeh Mohasefi J, Pirnejad H, Niazkhani Z. Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review. Cancers (Basel) 2025; 17:558. [PMID: 39941923 PMCID: PMC11817159 DOI: 10.3390/cancers17030558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2024] [Revised: 01/18/2025] [Accepted: 02/05/2025] [Indexed: 02/16/2025] Open
Abstract
BACKGROUND/OBJECTIVES This systematic literature review examines the application of Artificial Intelligence (AI) in the diagnosis, treatment, and follow-up of metastatic gastrointestinal cancers. METHODS The databases PubMed, Scopus, Embase (Ovid), and Google Scholar were searched for published articles in English from January 2010 to January 2022, focusing on AI models in metastatic gastrointestinal cancers. RESULTS forty-six studies were included in the final set of reviewed papers. The critical appraisal and data extraction followed the checklist for systematic reviews of prediction modeling studies. The risk of bias in the included papers was assessed using the prediction risk of bias assessment tool. CONCLUSIONS AI techniques, including machine learning and deep learning models, have shown promise in improving diagnostic accuracy, predicting treatment outcomes, and identifying prognostic biomarkers. Despite these advancements, challenges persist, such as reliance on retrospective data, variability in imaging protocols, small sample sizes, and data preprocessing and model interpretability issues. These challenges limit the generalizability, clinical application, and integration of AI models.
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Affiliation(s)
- Amin Naemi
- Nordcee, Department of Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Ashkan Tashk
- Cognitive Systems, DTU Compute, The Technical University of Denmark (DTU), 2800 Copenhagen, Denmark;
| | - Amir Sorayaie Azar
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark; (A.S.A.); (U.K.W.); (J.B.M.)
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Tahereh Samimi
- Student Research Committee, Urmia University of Medical Sciences, Urmia 1138, Iran;
- Department of Medical Informatics, Urmia University of Medical Sciences, Urmia 1138, Iran
| | - Ghanbar Tavassoli
- Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 969, Iran;
| | - Anita Bagherzadeh Mohasefi
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Elaheh Nasiri Khanshan
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Mehrdad Heshmat Najafabad
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Vafa Tarighi
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Uffe Kock Wiil
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark; (A.S.A.); (U.K.W.); (J.B.M.)
| | - Jamshid Bagherzadeh Mohasefi
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark; (A.S.A.); (U.K.W.); (J.B.M.)
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Habibollah Pirnejad
- Patient Safety Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia 1138, Iran;
- Department of Family Medicine, Amsterdam University Medical Center, 7057 Amsterdam, The Netherlands
| | - Zahra Niazkhani
- Nephrology and Kidney Transplant Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia 1138, Iran;
- Erasmus School of Health Policy and Management (ESHPM), Erasmus University Rotterdam, 3000 Rotterdam, The Netherlands
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Li X, Tang Z, Liu Y, Du Y, Xing Y, Zhang Z, Xie R. Value of enhanced CT machine learning models combined with clinicoradiological characteristics in predicting lymphatic tissue metastasis in colon cancer. RADIOLOGIE (HEIDELBERG, GERMANY) 2025:10.1007/s00117-024-01412-y. [PMID: 39903282 DOI: 10.1007/s00117-024-01412-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 11/12/2024] [Indexed: 02/06/2025]
Abstract
This study aimed to assess the effectiveness of various machine learning models in identifying lymph node metastasis in colon cancer patients and to explore the potential benefits of combining clinicoradiological and radiomics features for improved diagnosis. A total of 260 patients with pathologically confirmed colon cancer were retrospectively included in study center 1 and study center 2 from January 2015 to August 2024. A total of 198 patients with colon cancer in center 1 were randomly divided into a training set (n = 138) and an internal testing set (n = 60) at a ratio of 7:3. Patients in center 2 were included in the external testing set (n = 62). Five clinical radiological features were used to establish a clinical model. Radiomics features were extracted from the computed tomography venous phase images, and four classifiers, including logistic regression, support vector machine, decision tree, and k‑nearest neighbor, were used to build machine learning models. In addition, a combined model was constructed by joining clinical, radiological, and radiogenomic features. The performance of these models was evaluated in terms of accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), receiver operating curve (ROC) and calibration curves in the training set, internal testing set, and external testing set to determine the diagnostic model with the highest predictive efficiency and to evaluate the stability of the model. Among the four machine learning models, the SVM model had the best predictive performance, with an area under the ROC (AUC) of 0.813, 0.724, and 0.721 for the training set, internal testing set, and external testing set, respectively. The clinical model, radiomics model, and combined model had an AUC of 0.823, 0.813, 0.817, 0.508, 0.724, 0.751, 0.582, 0.721, and 0.744 in the training set, internal testing set, and external testing set, respectively. In conclusion, the combined model performed significantly better than the clinical model (p = 0.017, 0.038), but there was no significant difference between the radiomics model and the combined model (p = 0.556, 0.614).
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Affiliation(s)
- Xinyi Li
- Department of Radiology, Beijing Ditan Hospital, Capital Medical University, No. 8 Jingshun East Street, 100015, Beijing, Chaoyang District, China
| | - Ziwei Tang
- Department of Radiology, Changde Hospital, Xiangya School of Medicine, Central South University, 415000, Changde, China
| | - Yong Liu
- Department of Forensic Medicine, Tongji Medical College, Hua Zhong University of Science and Technology, 430030, Wuhan, China
| | - Yanni Du
- Department of Radiology, Beijing Ditan Hospital, Capital Medical University, No. 8 Jingshun East Street, 100015, Beijing, Chaoyang District, China
| | - Yuxue Xing
- Department of Radiology, Beijing Ditan Hospital, Capital Medical University, No. 8 Jingshun East Street, 100015, Beijing, Chaoyang District, China
| | - Zixin Zhang
- Department of Radiology, Beijing Ditan Hospital, Capital Medical University, No. 8 Jingshun East Street, 100015, Beijing, Chaoyang District, China
| | - Ruming Xie
- Department of Radiology, Beijing Ditan Hospital, Capital Medical University, No. 8 Jingshun East Street, 100015, Beijing, Chaoyang District, China.
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Aryanti C, Lusikooy RE, Sampetoding S, Laidding SR, Warsinggih W, Syarifuddin E, Uwuratuw JA, Kusuma MI, Labeda I, Abdul Rauf M. Development and Validation of Machine Learning Model Platelet Index-based Predictor for Colorectal Cancer Stage. Asian Pac J Cancer Prev 2024; 25:4425-4433. [PMID: 39733436 PMCID: PMC12008319 DOI: 10.31557/apjcp.2024.25.12.4425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2024] [Indexed: 12/31/2024] Open
Abstract
INTRODUCTION Colorectal cancer (CRC) staging is essential for effective treatment planning and prognosis. While platelet indices have shown promise in indicating CRC aggressiveness, a platelet index-based predictor for CRC staging has not been established in Indonesia. This study aimed to explore the relationship between platelet indices and CRC stage and to develop a predictive model and application. METHODS This cross-sectional study analyzed 369 CRC patients from Dr. Wahidin Sudirohusodo Hospital. Key parameters included age, gender, tumor location, and platelet indices: platelet count (PC), mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit, and the MPV/PC ratio. Data were processed using SPSS 25, MATLAB, and Streamlit. RESULTS AND DISCUSSION The analysis revealed significant correlations between elevated platelet indices and advanced CRC stages. Various machine learning models were developed, with Support Vector Machine (SVM) achieving the highest accuracy at 82.9%, followed closely by K-Nearest Neighbors (82.7%), Neural Network (81.5%), Naive Bayes (80.5%), and logistic regression (51.5%). The most effective model was implemented as a portable application through Streamlit, yielding 79.2% internal validation and 89.2% external validation. CONCLUSION This study highlights a significant association between increased platelet indices and advanced CRC stages. The innovative platelet index-based predictor for CRC staging offers promising potential for enhancing individualized clinical decision-making. By providing a non-invasive method that complements existing staging techniques, this approach could significantly improve patient outcomes through earlier and more accurate CRC staging. The findings underscore the importance of integrating simple, accessible biomarkers into clinical practice to enhance diagnostic precision.
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Affiliation(s)
- Citra Aryanti
- Departement of Surgery, Faculty of Medicine, Hasanuddin University, Makassar, South Sulawesi, Indonesia.
| | | | - Samuel Sampetoding
- Division of Digestive Surgery, Department of Surgery, Hasanuddin University, Indonesia.
| | - Sachraswaty R. Laidding
- Departement of Surgery, Faculty of Medicine, Hasanuddin University, Makassar, South Sulawesi, Indonesia.
| | - Warsinggih Warsinggih
- Division of Digestive Surgery, Department of Surgery, Hasanuddin University, Indonesia.
| | - Erwin Syarifuddin
- Division of Digestive Surgery, Department of Surgery, Hasanuddin University, Indonesia.
| | | | - Muhammad Ihwan Kusuma
- Departement of Surgery, Faculty of Medicine, Hasanuddin University, Makassar, South Sulawesi, Indonesia.
| | - Ibrahim Labeda
- Division of Digestive, Department of Surgery, Dr. Wahidin Sudirohusodo General Hospital, Makassar, South Sulawesi, Indonesia.
| | - Murny Abdul Rauf
- Department of Surgery, Hasanuddin University, Makasar, South Sulawesi, Indonesia.
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Shahadat N, Lama R, Nguyen A. Lung and Colon Cancer Detection Using a Deep AI Model. Cancers (Basel) 2024; 16:3879. [PMID: 39594834 PMCID: PMC11592951 DOI: 10.3390/cancers16223879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2024] [Revised: 10/31/2024] [Accepted: 11/10/2024] [Indexed: 11/28/2024] Open
Abstract
Lung and colon cancers are among the leading causes of cancer-related mortality worldwide. Early and accurate detection of these cancers is crucial for effective treatment and improved patient outcomes. False or incorrect detection is harmful. Accurately detecting cancer in a patient's tissue is crucial to their effective treatment. While analyzing tissue samples is complicated and time-consuming, deep learning techniques have made it possible to complete this process more efficiently and accurately. As a result, researchers can study more patients in a shorter amount of time and at a lower cost. Much research has been conducted to investigate deep learning models that require great computational ability and resources. However, none of these have had a 100% accurate detection rate for these life-threatening malignancies. Misclassified or falsely detecting cancer can have very harmful consequences. This research proposes a new lightweight, parameter-efficient, and mobile-embedded deep learning model based on a 1D convolutional neural network with squeeze-and-excitation layers for efficient lung and colon cancer detection. This proposed model diagnoses and classifies lung squamous cell carcinomas and adenocarcinoma of the lung and colon from digital pathology images. Extensive experiment demonstrates that our proposed model achieves 100% accuracy for detecting lung, colon, and lung and colon cancers from the histopathological (LC25000) lung and colon datasets, which is considered the best accuracy for around 0.35 million trainable parameters and around 6.4 million flops. Compared with the existing results, our proposed architecture shows state-of-the-art performance in lung, colon, and lung and colon cancer detection.
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Affiliation(s)
- Nazmul Shahadat
- Department of Computer and Data Sciences, Truman State University, Kirksville, MO 63501, USA; (R.L.); (A.N.)
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Nazari E, Khalili-Tanha G, Pourali G, Khojasteh-Leylakoohi F, Azari H, Dashtiahangar M, Fiuji H, Yousefli Z, Asadnia A, Maftooh M, Akbarzade H, Nassiri M, Hassanian SM, Ferns GA, Peters GJ, Giovannetti E, Batra J, Khazaei M, Avan A. The diagnostic and prognostic value of C1orf174 in colorectal cancer. BIOIMPACTS : BI 2024; 15:30566. [PMID: 40256241 PMCID: PMC12008501 DOI: 10.34172/bi.30566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 09/12/2024] [Accepted: 09/23/2024] [Indexed: 04/22/2025]
Abstract
Introduction Colorectal cancer (CRC) is among the lethal cancers, indicating the need for the identification of novel biomarkers for the detection of patients in earlier stages. RNA and microRNA sequencing were analyzed using bioinformatics and machine learning algorithms to identify differentially expressed genes (DEGs), followed by validation in CRC patients. Methods The genome-wide RNA sequencing of 631 samples, comprising 398 patients and 233 normal cases was extracted from the Cancer Genome Atlas (TCGA). The DEGs were identified using DESeq package in R. Survival analysis was evaluated using Kaplan-Meier analysis to identify prognostic biomarkers. Predictive biomarkers were determined by machine learning algorithms such as Deep learning, Decision Tree, and Support Vector Machine. The biological pathways, protein-protein interaction (PPI), the co-expression of DEGs, and the correlation between DEGs and clinical data were evaluated. Additionally, the diagnostic markers were assessed with a combioROC package. Finally, the candidate tope score gene was validated by Real-time PCR in CRC patients. Results The survival analysis revealed five novel prognostic genes, including KCNK13, C1orf174, CLEC18A, SRRM5, and GPR89A. Thirty-nine upregulated, 40 downregulated genes, and 20 miRNAs were detected by SVM with high accuracy and AUC. The upregulation of KRT20 and FAM118A genes and the downregulation of LRAT and PROZ genes had the highest coefficient in the advanced stage. Furthermore, our findings showed that three miRNAs (mir-19b-1, mir-326, and mir-330) upregulated in the advanced stage. C1orf174, as a novel gene, was validated using RT-PCR in CRC patients. The combineROC curve analysis indicated that the combination of C1orf174-AKAP4-DIRC1-SKIL-Scan29A4 can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.90, 0.94, and 0.92, respectively. Conclusion Machine learning algorithms can be used to Identify key dysregulated genes/miRNAs involved in the pathogenesis of diseases, leading to the detection of patients in earlier stages. Our data also demonstrated the prognostic value of C1orf174 in colorectal cancer.
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Affiliation(s)
- Elham Nazari
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ghazaleh Khalili-Tanha
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ghazaleh Pourali
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Hanieh Azari
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Hamid Fiuji
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam U.M.C., VU. University Medical Center (VUMC), Amsterdam, The Netherlands
| | - Zahra Yousefli
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Asadnia
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mina Maftooh
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- College of Medicine, University of Warith Al-Anbiyaa, Karbala, Iraq
| | - Hamed Akbarzade
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammadreza Nassiri
- Recombinant Proteins Research Group, The Research Institute of Biotechnology, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Seyed Mahdi Hassanian
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex BN1 9PH, UK
| | - Godefridus J Peters
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam U.M.C., VU. University Medical Center (VUMC), Amsterdam, The Netherlands
- Professor In Biochemistry, Medical University of Gdansk,Gdansk, Poland
| | - Elisa Giovannetti
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam U.M.C., VU. University Medical Center (VUMC), Amsterdam, The Netherlands
- Cancer Pharmacology Lab, AIRC Start up Unit, Fondazione Pisana per La Scienza, Pisa, Italy
| | - Jyotsna Batra
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane 4059, Australia
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane 4059, Australia
| | - Majid Khazaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane 4059, Australia
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Rouzbahani AK, Khalili-Tanha G, Rajabloo Y, Khojasteh-Leylakoohi F, Garjan HS, Nazari E, Avan A. Machine learning algorithms and biomarkers identification for pancreatic cancer diagnosis using multi-omics data integration. Pathol Res Pract 2024; 263:155602. [PMID: 39357184 DOI: 10.1016/j.prp.2024.155602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Revised: 09/21/2024] [Accepted: 09/24/2024] [Indexed: 10/04/2024]
Abstract
PURPOSE Pancreatic cancer is a lethal type of cancer with most of the cases being diagnosed in an advanced stage and poor prognosis. Developing new diagnostic and prognostic markers for pancreatic cancer can significantly improve early detection and patient outcomes. These biomarkers can potentially revolutionize medical practice by enabling personalized, more effective, targeted treatments, ultimately improving patient outcomes. METHODS The search strategy was developed following PRISMA guidelines. A comprehensive search was performed across four electronic databases: PubMed, Scopus, EMBASE, and Web of Science, covering all English publications up to September 2022. The Newcastle-Ottawa Scale (NOS) was utilized to assess bias, categorizing studies as "good," "fair," or "poor" quality based on their NOS scores. Descriptive statistics for all included studies were compiled and reviewed, along with the NOS scores for each study to indicate their quality assessment. RESULTS Our results showed that SVM and RF are the most widely used algorithms in machine learning and data analysis, particularly for biomarker identification. SVM, a supervised learning algorithm, is employed for both classification and regression by mapping data points in high-dimensional space to identify the optimal separating hyperplane between classes. CONCLUSIONS The application of machine-learning algorithms in the search for novel biomarkers in pancreatic cancer represents a significant advancement in the field. By harnessing the power of artificial intelligence, researchers are poised to make strides towards earlier detection and more effective treatment, ultimately improving patient outcomes in this challenging disease.
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Affiliation(s)
- Arian Karimi Rouzbahani
- Student Research Committee, Lorestan University of Medical Sciences, Khorramabad, Iran; USERN Office, Lorestan University of Medical Sciences, Khorramabad, Iran
| | - Ghazaleh Khalili-Tanha
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Yasamin Rajabloo
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Hassan Shokri Garjan
- Department of Health Information Technology, School of Management University of Medical Sciences, Tabriz, Iran
| | - Elham Nazari
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
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11
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Karthiga R, Narasimhan K, V T, M H, Amirtharajan R. Review of AI & XAI-based breast cancer diagnosis methods using various imaging modalities. MULTIMEDIA TOOLS AND APPLICATIONS 2024. [DOI: 10.1007/s11042-024-20271-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 08/27/2024] [Accepted: 09/11/2024] [Indexed: 01/02/2025]
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Zhao Y, Li X, Zhou C, Peng H, Zheng Z, Chen J, Ding W. A review of cancer data fusion methods based on deep learning. INFORMATION FUSION 2024; 108:102361. [DOI: 10.1016/j.inffus.2024.102361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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13
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Prasath ST, Navaneethan C. Colorectal cancer prognosis based on dietary pattern using synthetic minority oversampling technique with K-nearest neighbors approach. Sci Rep 2024; 14:17709. [PMID: 39085324 PMCID: PMC11292025 DOI: 10.1038/s41598-024-67848-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 07/16/2024] [Indexed: 08/02/2024] Open
Abstract
Generally, a person's life span depends on their food consumption because it may cause deadly diseases like colorectal cancer (CRC). In 2020, colorectal cancer accounted for one million fatalities globally, representing 10% of all cancer casualties. 76,679 males and 78,213 females over the age of 59 from ten states in the United States participated in this analysis. During follow-up, 1378 men and 981 women were diagnosed with colon cancer. This prospective cohort study used 231 food items and their variants as input features to identify CRC patients. Before labelling any foods as colorectal cancer-causing foods, it is ethical to analyse facts like how many grams of food should be consumed daily and how many times a week. This research examines five classification algorithms on real-time datasets: K-Nearest Neighbour (KNN), Decision Tree (DT), Random Forest (RF), Logistic Regression with Classifier Chain (LRCC), and Logistic Regression with Label Powerset (LRLC). Then, the SMOTE algorithm is applied to deal with and identify imbalances in the data. Our study shows that eating more than 10 g/d of low-fat butter in bread (RR 1.99, CI 0.91-4.39) and more than twice a week (RR 1.49, CI 0.93-2.38) increases CRC risk. Concerning beef, eating in excess of 74 g of beef steak daily (RR 0.88, CI 0.50-1.55) and having it more than once a week (RR 0.88, CI 0.62-1.23) decreases the risk of CRC, respectively. While eating beef and dairy products in a daily diet should be cautious about quantity. Consuming those items in moderation on a regular basis will protect us against CRC risk. Meanwhile, a high intake of poultry (RR 0.2, CI 0.05-0.81), fish (RR 0.82, CI 0.31-2.16), and pork (RR 0.67, CI 0.17-2.65) consumption negatively correlates to CRC hazards.
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Affiliation(s)
- S Thanga Prasath
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - C Navaneethan
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
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14
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Khatri M, Yin Y, Deogun J. Enhancing Interpretability in Medical Image Classification by Integrating Formal Concept Analysis with Convolutional Neural Networks. Biomimetics (Basel) 2024; 9:421. [PMID: 39056862 PMCID: PMC11274788 DOI: 10.3390/biomimetics9070421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/19/2024] [Accepted: 06/28/2024] [Indexed: 07/28/2024] Open
Abstract
In this study, we present a novel approach to enhancing the interpretability of medical image classification by integrating formal concept analysis (FCA) with convolutional neural networks (CNNs). While CNNs are increasingly applied in medical diagnoses, understanding their decision-making remains a challenge. Although visualization techniques like saliency maps offer insights into CNNs' decision-making for individual images, they do not explicitly establish a relationship between the high-level features learned by CNNs and the class labels across entire dataset. To bridge this gap, we leverage the FCA framework as an image classification model, presenting a novel method for understanding the relationship between abstract features and class labels in medical imaging. Building on our previous work, which applied this method to the MNIST handwritten image dataset and demonstrated that the performance is comparable to CNNs, we extend our approach and evaluation to histopathological image datasets, including Warwick-QU and BreakHIS. Our results show that the FCA-based classifier offers comparable accuracy to deep neural classifiers while providing transparency into the classification process, an important factor in clinical decision-making.
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Affiliation(s)
- Minal Khatri
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA;
| | - Yanbin Yin
- Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA;
| | - Jitender Deogun
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA;
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Bi S, Zhu J, Huang L, Feng W, Peng L, Leng L, Wang Y, Shan P, Kong W, Zhu S. Comprehensive Analysis of the Function and Prognostic Value of TAS2Rs Family-Related Genes in Colon Cancer. Int J Mol Sci 2024; 25:6849. [PMID: 38999959 PMCID: PMC11241446 DOI: 10.3390/ijms25136849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/09/2024] [Accepted: 06/19/2024] [Indexed: 07/14/2024] Open
Abstract
In the realm of colon carcinoma, significant genetic and epigenetic diversity is observed, underscoring the necessity for tailored prognostic features that can guide personalized therapeutic strategies. In this study, we explored the association between the type 2 bitter taste receptor (TAS2Rs) family-related genes and colon cancer using RNA-sequencing and clinical datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Our preliminary analysis identified seven TAS2Rs genes associated with survival using univariate Cox regression analysis, all of which were observed to be overexpressed in colon cancer. Subsequently, based on these seven TAS2Rs prognostic genes, two colon cancer molecular subtypes (Cluster A and Cluster B) were defined. These subtypes exhibited distinct prognostic and immune characteristics, with Cluster A characterized by low immune cell infiltration and less favorable outcomes, while Cluster B was associated with high immune cell infiltration and better prognosis. Finally, we developed a robust scoring system using a gradient boosting machine (GBM) approach, integrated with the gene-pairing method, to predict the prognosis of colon cancer patients. This machine learning model could improve our predictive accuracy for colon cancer outcomes, underscoring its value in the precision oncology framework.
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Affiliation(s)
- Suzhen Bi
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China; (S.B.); (L.H.); (W.F.); (L.P.); (L.L.); (Y.W.); (P.S.)
| | - Jie Zhu
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 00014 Helsinki, Finland;
| | - Liting Huang
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China; (S.B.); (L.H.); (W.F.); (L.P.); (L.L.); (Y.W.); (P.S.)
| | - Wanting Feng
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China; (S.B.); (L.H.); (W.F.); (L.P.); (L.L.); (Y.W.); (P.S.)
| | - Lulu Peng
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China; (S.B.); (L.H.); (W.F.); (L.P.); (L.L.); (Y.W.); (P.S.)
| | - Liangqi Leng
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China; (S.B.); (L.H.); (W.F.); (L.P.); (L.L.); (Y.W.); (P.S.)
| | - Yin Wang
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China; (S.B.); (L.H.); (W.F.); (L.P.); (L.L.); (Y.W.); (P.S.)
| | - Peipei Shan
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China; (S.B.); (L.H.); (W.F.); (L.P.); (L.L.); (Y.W.); (P.S.)
| | - Weikaixin Kong
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 00014 Helsinki, Finland;
| | - Sujie Zhu
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China; (S.B.); (L.H.); (W.F.); (L.P.); (L.L.); (Y.W.); (P.S.)
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Anuntakarun S, Khamjerm J, Tangkijvanich P, Chuaypen N. Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach. Bioinform Biol Insights 2024; 18:11779322241258586. [PMID: 38846329 PMCID: PMC11155358 DOI: 10.1177/11779322241258586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 05/10/2024] [Indexed: 06/09/2024] Open
Abstract
Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer.
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Affiliation(s)
- Songtham Anuntakarun
- Center of Excellence in Hepatitis and Liver Cancer, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Jakkrit Khamjerm
- Center of Excellence in Hepatitis and Liver Cancer, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Biomedical Engineering Program, Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Pisit Tangkijvanich
- Center of Excellence in Hepatitis and Liver Cancer, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Natthaya Chuaypen
- Center of Excellence in Hepatitis and Liver Cancer, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
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Deng B, Wang Q, Liu Y, Yang Y, Gao X, Dai H. A nomogram based on MRI radiomics features of mesorectal fat for diagnosing T2- and T3-stage rectal cancer. Abdom Radiol (NY) 2024; 49:1850-1860. [PMID: 38349392 DOI: 10.1007/s00261-023-04164-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 12/10/2023] [Accepted: 12/16/2023] [Indexed: 06/29/2024]
Abstract
PURPOSE To develop and validate a nomogram for the preoperative diagnosis of T2 and T3 stage rectal cancer using MRI radiomics features of mesorectal fat. METHODS The data of 288 patients with T2 and T3 stage rectal cancer were retrospectively collected. Radiomics features were extracted from the lesion region of interest (ROI) in the MRI high-resolution T2WI, apparent diffusion coefficient (ADC), and diffusion-weighted imaging (DWI) sequences. After using ICC inter-group consistency analysis and Pearson correlation analysis to reduce dimensions, LASSO regression analysis was performed to select features and calculate Rad-score for each sequence. Then, Combined_Radscore and nomogram were constructed based on the LASSO-selected features and clinical data for each sequence. Receiver operating characteristic curve (ROC) area under the curve (AUC) was used to evaluate the performance of the Rad-score model and nomogram. Decision curve analysis (DCA) was performed to evaluate the clinical usability of the radiomics nomogram, which were combined with calibration curves to evaluate the prediction accuracy. RESULTS The nomogram based on MRI-report T status and Combined_Radscore achieved AUCs of 0.921 and 0.889 in the training and validation cohorts, respectively. CONCLUSION The nomogram can be stated that the radiomics nomogram based on multi-sequence MRI imaging of the mesorectal fat has excellent diagnosing performance for preoperative differentiation of T2 and T3 stage rectal cancer.
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Affiliation(s)
- Bo Deng
- Department of Radiology, Shanghai Fifth Rehabilitation Hospital, Shanghai, China
- Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qian Wang
- Department of Radiology, Shanghai Fifth Rehabilitation Hospital, Shanghai, China
| | - Yuanqing Liu
- Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yanwei Yang
- Magnetic Resonance Room of Orthopedics Department, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaolong Gao
- Department of Radiology, Luodian Hospital, Shanghai University Medical College, Baoshan District, Shanghai, China.
| | - Hui Dai
- Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, China.
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Bareja C, Dwivedi K, Uboveja A, Mathur A, Kumar N, Saluja D. Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics. Sci Rep 2024; 14:9894. [PMID: 38688978 PMCID: PMC11061124 DOI: 10.1038/s41598-024-60715-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/26/2024] [Indexed: 05/02/2024] Open
Abstract
This study aims to decipher crucial biomarkers regulated by p73 for the early detection of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling techniques. The transcriptome profile of HCT116 cell line p53- / - p73+ / + and p53- / - p73 knockdown was performed to identify differentially expressed genes (DEGs). This was corroborated with three CRC tissue expression datasets available in Gene Expression Omnibus. Further analysis involved KEGG and Gene ontology to elucidate the functional roles of DEGs. The protein-protein interaction (PPI) network was constructed using Cytoscape to identify hub genes. Kaplan-Meier (KM) plots along with GEPIA and UALCAN database analysis provided the insights into the prognostic and diagnostic significance of these hub genes. Machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1289 upregulated and 1897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. Comprehensive analysis using gene ontology and KEGG revealed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan-Meier, GEPIA, and UALCAN analyses uncovered the clinicopathological relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. The study represents a pioneer endeavor amalgamating transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-associated genes. These genes are found relevant regarding the patients' prognosis and diagnosis. The unveiled biomarkers exhibit robustness in TNM-stage prediction, thereby laying the foundation for future clinical applications and therapeutic interventions in CRC management.
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Affiliation(s)
- Chanchal Bareja
- Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, 110007, India
| | - Kountay Dwivedi
- Department of Computer Science, Faculty of Mathematical Sciences, University of Delhi, Delhi, 110007, India
| | - Apoorva Uboveja
- Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, 110007, India
| | - Ankit Mathur
- Delhi School of Public Health, Institution of Eminence, University of Delhi, Delhi, 110007, India
| | - Naveen Kumar
- Department of Computer Science, Faculty of Mathematical Sciences, University of Delhi, Delhi, 110007, India
| | - Daman Saluja
- Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, 110007, India.
- Delhi School of Public Health, Institution of Eminence, University of Delhi, Delhi, 110007, India.
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Sasani H, Ozkan M, Simsek MA, Sasani M. Morphometric analysis and tortuosity typing of the large intestine segments on computed tomography colonography with artificial intelligence. Colomb Med (Cali) 2024; 55:e2005944. [PMID: 39564004 PMCID: PMC11573345 DOI: 10.25100/cm.v55i2.5944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/17/2024] [Accepted: 06/15/2024] [Indexed: 11/21/2024] Open
Abstract
Background Morphological properties such as length and tortuosity of the large intestine segments play important roles, especially in interventional procedures like colonoscopy. Objective Using computed tomography (CT) colonoscopy images, this study aimed to examine the morphological features of the colon's anatomical sections and investigate the relationship of these sections with each other or with age groups. The shapes of the transverse colon were analyzed using artificial intelligence. Methods The study was conducted as a two- and three-dimensional examination of CT colonography images of people between 40 and 80 years old, which were obtained retrospectively. An artificial intelligence algorithm (YOLOv8) was used for shape detection on 3D colon images. Results 160 people with a mean age of 89 men and 71 women included in the study were 57.79±8.55 and 56.55±6.60, respectively, and there was no statistically significant difference (p= 0.24). The total colon length was 166.11±25.07 cm for men and 158.73±21.92 cm for women, with no significant difference between groups (p=0.12). As a result of the training of the model Precision, Recall, and Mean Average Precision (mAP) were found to be 0.8578, 0.7940, and 0.9142, respectively. Conclusion The study highlights the importance of understanding the type and morphology of the large intestine for accurate interpretation of CT colonography results and effective clinical management of patients with suspected large intestine abnormalities. Furthermore, this study showed that 88.57% of the images in the test data set were detected correctly and that AI can play an important role in colon typing.
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Affiliation(s)
- Hadi Sasani
- Tekirdag Namik Kemal University, Faculty of Medicine, Department of Radiology, Tekirdag, Turkey Tekirdag Namik Kemal University Tekirdag Namik Kemal University Faculty of Medicine Department of Radiology TekirdagTurkey Turkey
| | - Mazhar Ozkan
- Tekirdag Namık Kemal University, School of Medicine, Department of Anatomy, Tekirdag, Turkey Tekirdag Namik Kemal University Tekirdag Namık Kemal University School of Medicine Department of Anatomy Tekirdag Turkey
| | - Mehmet Ali Simsek
- Tekirdag Namik Kemal University, Vocational School of Technical Sciences, Department of Computer Technologies, Tekirdag, Turkey Tekirdag Namik Kemal University Tekirdag Namik Kemal University Vocational School of Technical Sciences Department of Computer Technologies Tekirdag Turkey
| | - Mahmut Sasani
- Bezmi Alem Vakif University, Faculty of Medicine, Istanbul, Turkey Bezmi Alem Vakif University Bezmi Alem Vakif University Faculty of Medicine Istanbul Turkey
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Leo M, Carcagnì P, Signore L, Corcione F, Benincasa G, Laukkanen MO, Distante C. Convolutional Neural Networks in the Diagnosis of Colon Adenocarcinoma. AI 2024; 5:324-341. [DOI: 10.3390/ai5010016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2025] Open
Abstract
Colorectal cancer is one of the most lethal cancers because of late diagnosis and challenges in the selection of therapy options. The histopathological diagnosis of colon adenocarcinoma is hindered by poor reproducibility and a lack of standard examination protocols required for appropriate treatment decisions. In the current study, using state-of-the-art approaches on benchmark datasets, we analyzed different architectures and ensembling strategies to develop the most efficient network combinations to improve binary and ternary classification. We propose an innovative two-stage pipeline approach to diagnose colon adenocarcinoma grading from histological images in a similar manner to a pathologist. The glandular regions were first segmented by a transformer architecture with subsequent classification using a convolutional neural network (CNN) ensemble, which markedly improved the learning efficiency and shortened the learning time. Moreover, we prepared and published a dataset for clinical validation of the developed artificial neural network, which suggested the discovery of novel histological phenotypic alterations in adenocarcinoma sections that could have prognostic value. Therefore, AI could markedly improve the reproducibility, efficiency, and accuracy of colon cancer diagnosis, which are required for precision medicine to personalize the treatment of cancer patients.
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Affiliation(s)
- Marco Leo
- Institute of Applied Sciences and Intelligent Systems (ISASI), National Research Council (CNR) of Italy, 73100 Lecce, Italy
| | - Pierluigi Carcagnì
- Institute of Applied Sciences and Intelligent Systems (ISASI), National Research Council (CNR) of Italy, 73100 Lecce, Italy
| | - Luca Signore
- Dipartimento di Ingegneria per L’Innovazione, Università del Salento, 73100 Lecce, Italy
| | | | | | - Mikko O. Laukkanen
- Department of Translational Medical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Cosimo Distante
- Institute of Applied Sciences and Intelligent Systems (ISASI), National Research Council (CNR) of Italy, 73100 Lecce, Italy
- Dipartimento di Ingegneria per L’Innovazione, Università del Salento, 73100 Lecce, Italy
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Talebi R, Celis-Morales CA, Akbari A, Talebi A, Borumandnia N, Pourhoseingholi MA. Machine learning-based classifiers to predict metastasis in colorectal cancer patients. Front Artif Intell 2024; 7:1285037. [PMID: 38327669 PMCID: PMC10847339 DOI: 10.3389/frai.2024.1285037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 01/03/2024] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND The increasing prevalence of colorectal cancer (CRC) in Iran over the past three decades has made it a key public health burden. This study aimed to predict metastasis in CRC patients using machine learning (ML) approaches in terms of demographic and clinical factors. METHODS This study focuses on 1,127 CRC patients who underwent appropriate treatments at Taleghani Hospital, a tertiary care facility. The patients were divided into training and test datasets in an 80:20 ratio. Various ML methods, including Naive Bayes (NB), random rorest (RF), support vector machine (SVM), neural network (NN), decision tree (DT), and logistic regression (LR), were used for predicting metastasis in CRC patients. Model performance was evaluated using 5-fold cross-validation, reporting sensitivity, specificity, the area under the curve (AUC), and other indexes. RESULTS Among the 1,127 patients, 183 (16%) had experienced metastasis. In the predictionof metastasis, both the NN and RF algorithms had the highest AUC, while SVM ranked third in both the original and balanced datasets. The NN and RF algorithms achieved the highest AUC (100%), sensitivity (100% and 100%, respectively), and accuracy (99.2% and 99.3%, respectively) on the balanced dataset, followed by the SVM with an AUC of 98.8%, a sensitivity of 97.5%, and an accuracy of 97%. Moreover, lower false negative rate (FNR), false positive rate (FPR), and higher negative predictive value (NPV) can be confirmed by these two methods. The results also showed that all methods exhibited good performance in the test datasets, and the balanced dataset improved the performance of most ML methods. The most important variables for predicting metastasis were the tumor stage, the number of involved lymph nodes, and the treatment type. In a separate analysis of patients with tumor stages I-III, it was identified that tumor grade, tumor size, and tumor stage are the most important features. CONCLUSION This study indicated that NN and RF were the best among ML-based approaches for predicting metastasis in CRC patients. Both the tumor stage and the number of involved lymph nodes were considered the most important features.
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Affiliation(s)
- Raheleh Talebi
- Department of Pure Mathematics, Lecturer of Mathematics at Architecture and Computer Engineering Department, University of Applied Sciences and Technology (Unit 10), Tehran, Iran
| | - Carlos A. Celis-Morales
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
- Human Performance Laboratory, Education, Physical Activity and Health Research Unit, Universidad Católica del Maule, Talca, Chile
| | - Abolfazl Akbari
- Colorectal Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Atefeh Talebi
- Colorectal Research Center, Iran University of Medical Sciences, Tehran, Iran
- British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom
| | - Nasrin Borumandnia
- Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohamad Amin Pourhoseingholi
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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22
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Adiwinata R, Tandarto K, Arifputra J, Waleleng BJ, Gosal F, Rotty L, Winarta J, Waleleng A, Simadibrata P, Simadibrata M. The Impact of Artificial Intelligence in Improving Polyp and Adenoma Detection Rate During Colonoscopy: Systematic-Review and Meta-Analysis. Asian Pac J Cancer Prev 2023; 24:3655-3663. [PMID: 38019222 PMCID: PMC10772777 DOI: 10.31557/apjcp.2023.24.11.3655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 11/10/2023] [Indexed: 11/30/2023] Open
Abstract
INTRODUCTION Colonoscopy may detect colorectal polyp and facilitate its removal in order to prevent colorectal cancer. However, substantial miss rate for colorectal adenomas detection still occurred during screening colonoscopy procedure. Nowadays, artificial intelligence (AI) have been employed in trials to improve polyp detection rate (PDR) and adenoma detection rate (ADR). Therefore, we would like to determine the impact of AI in increasing PDR and ADR. METHODS The present study adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 (PRISMA 2020) statement. To identify relevant literature, comprehensive searches were conducted on major scientific databases, including Pubmed, EBSCO-host, and Proquest. The search was limited to articles published up to November 30, 2022. Inclusion criteria for the study encompassed full-text accessibility, articles written in the English language, and randomized controlled trials (RCTs) that reported both ADR and PDR values, comparing conventional diagnostic methods with AI-aided approaches. To synthesize the data, we computed the combined pooled odds ratio (OR) using a random-effects model. This model was chosen due to the expectation of considerable heterogeneity among the selected studies. To evaluate potential publication bias, the Begg's funnel diagram was employed. RESULTS A total of 13 studies were included in this study. Colonoscopy with AI had significantly higher PDR compared to without AI (pooled OR 1.46, 95% CI 1.13-1.89, p = 0.003) and higher ADR (pooled OR 1.58, 95% CI 1.37-1.82, p < 0.00001). PDR analysis showed moderate heterogeneity between included studies (p = 0.004; I2=63%). Furthermore, ADR analysis showed moderate heterogeneity (p < 0.007; I2 = 57%). Additionally, the funnels plot of ADR and PDR analysis showed an asymmetry plot and low publication bias. CONCLUSION AI may improve colonoscopy result quality through improving PDR and ADR.
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Affiliation(s)
- Randy Adiwinata
- Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Kevin Tandarto
- S.K Lerik Regional Public Hospital, Kupang, East Nusa Tenggara, Indonesia.
| | - Jonathan Arifputra
- Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Bradley Jimmy Waleleng
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Fandy Gosal
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Luciana Rotty
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Jeanne Winarta
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Andrew Waleleng
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Paulus Simadibrata
- Department of Internal Medicine, Abdi Waluyo Hospital, Jakarta, Indonesia.
| | - Marcellus Simadibrata
- Division of Gastroenterology, Pancreatobiliary and Digestive Endoscopy, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo General Hospital, Jakarta, Indonesia.
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23
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Daneshvar S, Zamanian MY, Ivraghi MS, Golmohammadi M, Modanloo M, Kamiab Z, Pourhosseini SME, Heidari M, Bazmandegan G. A comprehensive view on the apigenin impact on colorectal cancer: Focusing on cellular and molecular mechanisms. Food Sci Nutr 2023; 11:6789-6801. [PMID: 37970406 PMCID: PMC10630840 DOI: 10.1002/fsn3.3645] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/08/2023] [Accepted: 08/13/2023] [Indexed: 11/17/2023] Open
Abstract
Colon cancer (CC) is one of the most common and deadly cancers worldwide. Oncologists are facing challenges such as development of drug resistance and lack of suitable drug options for CC treatment. Flavonoids are a group of natural compounds found in fruits, vegetables, and other plant-based foods. According to research, they have a potential role in the prevention and treatment of cancer. Apigenin is a flavonoid that is present in many fruits and vegetables. It has been used as a natural antioxidant for a long time and has been considered due to its anticancer effects and low toxicity. The results of this review study show that apigenin has potential anticancer effects on CC cells through various mechanisms. In this comprehensive review, we present the cellular targets and signaling pathways of apigenin indicated to date in in vivo and in vitro CC models. Among the most important modulated pathways, Wnt/β-catenin, PI3K/AKT/mTOR, MAPK/ERK, JNK, STAT3, Bcl-xL and Mcl-1, PKM2, and NF-kB have been described. Furthermore, apigenin suppresses the cell cycle in G2/M phase in CC cells. In CC cells, apigenin-induced apoptosis is increased by inhibiting the formation of autophagy. According to the results of this study, apigenin appears to have the potential to be a promising agent for CC therapy, but more research is required in the field of pharmacology and pharmacokinetics to establish the apigenin effects and its dosage for clinical studies.
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Affiliation(s)
- Siamak Daneshvar
- Department of General SurgerySchool of MedicineShahid Beheshti University of Medical SciencesTehranIran
| | - Mohammad Yasin Zamanian
- Department of PhysiologySchool of MedicineHamadan University of Medical SciencesHamadanIran
- Department of Pharmacology and ToxicologySchool of PharmacyHamadan University of Medical SciencesHamadanIran
| | | | | | - Mona Modanloo
- Pharmaceutical Sciences Research CenterMazandaran University of Medical SciencesSariIran
| | - Zahra Kamiab
- Clinical Research Development UnitAli‐Ibn Abi‐Talib HospitalRafsanjan University of Medical SciencesRafsanjanIran
- Department of Community MedicineSchool of MedicineRafsanjan University of Medical SciencesRafsanjanIran
| | - Seyed Mohammad Ebrahim Pourhosseini
- Non‐Communicable Diseases Research CenterRafsanjan University of Medical SciencesRafsanjanIran
- Department of Internal MedicineSchool of MedicineRafsanjan University of Medical SciencesRafsanjanIran
| | - Mahsa Heidari
- Department of BiochemistryInstitute of Biochemistry and Biophysics (IBB)University of TehranTehranIran
| | - Gholamreza Bazmandegan
- Physiology‐Pharmacology Research CenterResearch Institute of Basic Medical SciencesRafsanjan University of Medical SciencesRafsanjanIran
- Department of Physiology and PharmacologySchool of MedicineRafsanjan University of Medical SciencesRafsanjanIran
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24
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Sahoo PK, Gupta P, Lai YC, Chiang SF, You JF, Onthoni DD, Chern YJ. Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning Approach. Bioengineering (Basel) 2023; 10:972. [PMID: 37627857 PMCID: PMC10451186 DOI: 10.3390/bioengineering10080972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/26/2023] [Accepted: 07/31/2023] [Indexed: 08/27/2023] Open
Abstract
Abdominal computed tomography (CT) is a frequently used imaging modality for evaluating gastrointestinal diseases. The detection of colorectal cancer is often realized using CT before a more invasive colonoscopy. When a CT exam is performed for indications other than colorectal evaluation, the tortuous structure of the long, tubular colon makes it difficult to analyze the colon carefully and thoroughly. In addition, the sensitivity of CT in detecting colorectal cancer is greatly dependent on the size of the tumor. Missed incidental colon cancers using CT are an emerging problem for clinicians and radiologists; consequently, the automatic localization of lesions in the CT images of unprepared bowels is needed. Therefore, this study used artificial intelligence (AI) to localize colorectal cancer in CT images. We enrolled 190 colorectal cancer patients to obtain 1558 tumor slices annotated by radiologists and colorectal surgeons. The tumor sites were double-confirmed via colonoscopy or other related examinations, including physical examination or image study, and the final tumor sites were obtained from the operation records if available. The localization and training models used were RetinaNet, YOLOv3, and YOLOv8. We achieved an F1 score of 0.97 (±0.002), a mAP of 0.984 when performing slice-wise testing, 0.83 (±0.29) sensitivity, 0.97 (±0.01) specificity, and 0.96 (±0.01) accuracy when performing patient-wise testing using our derived model YOLOv8 with hyperparameter tuning.
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Affiliation(s)
- Prasan Kumar Sahoo
- Department of Computer Science and Information Engineering, Chang Gung University, Guishan, Taoyuan 33302, Taiwan; (P.K.S.); (P.G.); (D.D.O.)
- Department of Neurology, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, Taiwan
| | - Pushpanjali Gupta
- Department of Computer Science and Information Engineering, Chang Gung University, Guishan, Taoyuan 33302, Taiwan; (P.K.S.); (P.G.); (D.D.O.)
| | - Ying-Chieh Lai
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, Taiwan;
- Department of Metabolomics Core Lab, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, Taiwan
| | - Sum-Fu Chiang
- Division of Colon and Rectal Surgery, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, Taiwan; (S.-F.C.); (J.-F.Y.)
- College of Medicine, Chang Gung University, Guishan, Taoyuan 33302, Taiwan
| | - Jeng-Fu You
- Division of Colon and Rectal Surgery, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, Taiwan; (S.-F.C.); (J.-F.Y.)
- College of Medicine, Chang Gung University, Guishan, Taoyuan 33302, Taiwan
| | - Djeane Debora Onthoni
- Department of Computer Science and Information Engineering, Chang Gung University, Guishan, Taoyuan 33302, Taiwan; (P.K.S.); (P.G.); (D.D.O.)
| | - Yih-Jong Chern
- Division of Colon and Rectal Surgery, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, Taiwan; (S.-F.C.); (J.-F.Y.)
- Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Guishan, Taoyuan 33302, Taiwan
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25
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Bostanci E, Kocak E, Unal M, Guzel MS, Acici K, Asuroglu T. Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer. SENSORS (BASEL, SWITZERLAND) 2023; 23:3080. [PMID: 36991790 PMCID: PMC10052105 DOI: 10.3390/s23063080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/09/2023] [Accepted: 03/11/2023] [Indexed: 06/19/2023]
Abstract
Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.
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Affiliation(s)
- Erkan Bostanci
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Engin Kocak
- Department of Analytical Chemistry, Faculty of Gülhane Pharmacy, University of Health Sciences, 06018 Ankara, Turkey
| | - Metehan Unal
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Mehmet Serdar Guzel
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Koray Acici
- Department of Artificial Intelligence and Data Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Tunc Asuroglu
- Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
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26
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Mansur A, Saleem Z, Elhakim T, Daye D. Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions. Front Oncol 2023; 13:1065402. [PMID: 36761957 PMCID: PMC9905815 DOI: 10.3389/fonc.2023.1065402] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 01/09/2023] [Indexed: 01/26/2023] Open
Abstract
Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.
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Affiliation(s)
- Arian Mansur
- Harvard Medical School, Boston, MA, United States
| | | | - Tarig Elhakim
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
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27
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Du J, Huang M, Liu L. AI-Aided Disease Prediction in Visualized Medicine. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1199:107-126. [PMID: 37460729 DOI: 10.1007/978-981-32-9902-3_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Artificial intelligence (AI) is playing a vitally important role in promoting the revolution of future technology. Healthcare is one of the promising applications in AI, which covers medical imaging, diagnosis, robotics, disease prediction, pharmacy, health management, and hospital management. Numbers of achievements that made in these fields overturn every aspect in traditional healthcare system. Therefore, to understand the state-of-art AI in healthcare, as well as the chances and obstacles in its development, the applications of AI in disease detection and outlook and the future trends of AI-aided disease prediction were discussed in this chapter.
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Affiliation(s)
- Juan Du
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
| | - Mengen Huang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Lin Liu
- Tianjin Key Laboratory of Retinal Functions and Diseases, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
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28
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Cakmak A, Ayaz H, Arıkan S, Ibrahimzada AR, Demirkol Ş, Sönmez D, Hakan MT, Sürmen ST, Horozoğlu C, Doğan MB, Küçükhüseyin Ö, Cacına C, Kıran B, Zeybek Ü, Baysan M, Yaylım İ. Predicting the predisposition to colorectal cancer based on SNP profiles of immune phenotypes using supervised learning models. Med Biol Eng Comput 2023; 61:243-258. [PMID: 36357628 DOI: 10.1007/s11517-022-02707-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 10/22/2022] [Indexed: 11/12/2022]
Abstract
This study explores the machine learning-based assessment of predisposition to colorectal cancer based on single nucleotide polymorphisms (SNP). Such a computational approach may be used as a risk indicator and an auxiliary diagnosis method that complements the traditional methods such as biopsy and CT scan. Moreover, it may be used to develop a low-cost screening test for the early detection of colorectal cancers to improve public health. We employ several supervised classification algorithms. Besides, we apply data imputation to fill in the missing genotype values. The employed dataset includes SNPs observed in particular colorectal cancer-associated genomic loci that are located within DNA regions of 11 selected genes obtained from 115 individuals. We make the following observations: (i) random forest-based classifier using one-hot encoding and K-nearest neighbor (KNN)-based imputation performs the best among the studied classifiers with an F1 score of 89% and area under the curve (AUC) score of 0.96. (ii) One-hot encoding together with K-nearest neighbor-based data imputation increases the F1 scores by around 26% in comparison to the baseline approach which does not employ them. (iii) The proposed model outperforms a commonly employed state-of-the-art approach, ColonFlag, under all evaluated settings by up to 24% in terms of the AUC score. Based on the high accuracy of the constructed predictive models, the studied 11 genes may be considered a gene panel candidate for colon cancer risk screening.
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Affiliation(s)
- Ali Cakmak
- Department of Computer Engineering, Istanbul Technical University, Ayazaga Campus, Reşitpaşa, 34467, Sarıyer, Istanbul, Turkey.
| | | | - Soykan Arıkan
- Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey
| | | | | | - Dilara Sönmez
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
| | - Mehmet T Hakan
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
| | - Saime T Sürmen
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
| | | | - Mehmet B Doğan
- Istanbul Research and Training Hospital, Istanbul, Turkey
| | - Özlem Küçükhüseyin
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
| | - Canan Cacına
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
| | | | - Ümit Zeybek
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
| | - Mehmet Baysan
- Department of Computer Engineering, Istanbul Technical University, Ayazaga Campus, Reşitpaşa, 34467, Sarıyer, Istanbul, Turkey
| | - İlhan Yaylım
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
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29
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Lee C, Baek B, Cho SH, Jang T, Jeon S, Lee S, Lee H, Nam J. Machine learning with in silico analysis markedly improves survival prediction modeling in colon cancer patients. Cancer Med 2022; 12:7603-7615. [PMID: 36345155 PMCID: PMC10067044 DOI: 10.1002/cam4.5420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 10/03/2022] [Accepted: 10/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Predicting the survival of cancer patients provides prognostic information and therapeutic guidance. However, improved prediction models are needed for use in diagnosis and treatment. OBJECTIVE This study aimed to identify genomic prognostic biomarkers related to colon cancer (CC) based on computational data and to develop survival prediction models. METHODS We performed machine-learning (ML) analysis to screen pathogenic survival-related driver genes related to patient prognosis by integrating copy number variation and gene expression data. Moreover, in silico system analysis was performed to clinically assess data from ML analysis, and we identified RABGAP1L, MYH9, and DRD4 as candidate genes. These three genes and tumor stages were used to generate survival prediction models. Moreover, the genes were validated by experimental and clinical analyses, and the theranostic application of the survival prediction models was assessed. RESULTS RABGAP1L, MYH9, and DRD4 were identified as survival-related candidate genes by ML and in silico system analysis. The survival prediction model using the expression of the three genes showed higher predictive performance when applied to predict the prognosis of CC patients. A series of functional analyses revealed that each knockdown of three genes reduced the protumor activity of CC cells. In particular, validation with an independent cohort of CC patients confirmed that the coexpression of MYH9 and DRD4 gene expression reflected poorer clinical outcomes in terms of overall survival and disease-free survival. CONCLUSIONS Our survival prediction approach will contribute to providing information on patients and developing a therapeutic strategy for CC patients.
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Affiliation(s)
- Choong‐Jae Lee
- School of Life Sciences Gwangju Institute of Science and Technology Gwangju Korea
| | - Bin Baek
- School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea
| | - Sang Hee Cho
- Department of Hemato‐Oncology Chonnam National University Medical School Gwangju Korea
| | - Tae‐Young Jang
- School of Life Sciences Gwangju Institute of Science and Technology Gwangju Korea
| | - So‐El Jeon
- School of Life Sciences Gwangju Institute of Science and Technology Gwangju Korea
| | - Sunjae Lee
- School of Life Sciences Gwangju Institute of Science and Technology Gwangju Korea
| | - Hyunju Lee
- School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea
| | - Jeong‐Seok Nam
- School of Life Sciences Gwangju Institute of Science and Technology Gwangju Korea
- Cell Logistics Research Center Gwangju Institute of Science and Technology Gwangju South Korea
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30
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Bayrak T, Çetin Z, Saygılı Eİ, Ogul H. Identifying the tumor location-associated candidate genes in development of new drugs for colorectal cancer using machine-learning-based approach. Med Biol Eng Comput 2022; 60:2877-2897. [DOI: 10.1007/s11517-022-02641-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 07/28/2022] [Indexed: 02/07/2023]
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31
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Liu Y, Kang R, Zheng H, Wang P, Jiang W, Xiong B, Chen J, Xu J. Female Colon Cancer Metastasis Pattern and Prognosis: A SEER-Based Study. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3865601. [PMID: 35845938 PMCID: PMC9283037 DOI: 10.1155/2022/3865601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/18/2022] [Accepted: 06/21/2022] [Indexed: 12/24/2022]
Abstract
The purpose of this study was to compare the metastatic pattern and prognosis of female colon cancer (FCC) to that of male colon cancer (MCC) to ascertain the independent factors impacting the prognosis of patients with FCC. The data of the present study population were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Descriptive analysis, the Kaplan-Meier method, and the Cox regression were used to evaluated FCC characteristics and factors associated with prognosis. There were 56,442 patients diagnosed with FCC, of whom 8,817 had distant metastases. Compared to patients with nonmetastatic FCC, a greater proportion of metastatic FCC patients was less than 60 years of age, black race, and grade III-IV. The primary sites were mainly located on the left side and have more possibility to receive chemotherapy and radiotherapy. Compared to metastatic MCC, a higher proportion of metastatic FCC patients ranged over 60 years of age, black race, treated without chemotherapy, and insurance, while the primary site was located on the right side. Liver and lung were the two most common sites of solitary metastases in CC, and among patients with solitary metastases in CC, patients who had lung metastases had a better prognosis than those who developed other types of metastasizes. Patients with FCC with metastases of the liver had a worse prognosis than their MCC counterparts. Cox multivariate regression analysis showed that the risk ratio was higher in metastatic FCC patients compared to those without metastases. We report the survival comparison of metastatic FCC with nonmetastatic FCC through the SEER database. Our results suggest that it has unique clinicopathological features and differs from metastatic MCC. Furthermore, patients with liver metastatic FCC have a worse prognosis than those with MCC. Emphasis on screening for colon cancer in women and additional clinical care should be paid for, especially for patients with FCC with metastatic liver cancer.
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Affiliation(s)
- Yurong Liu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Rongbin Kang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Huida Zheng
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Pengcheng Wang
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Weixin Jiang
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Bin Xiong
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Jintao Chen
- Department of Endocrinology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jianhua Xu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
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Zhang YF, Ma C, Qian XP. Development and external validation of a novel nomogram for predicting cancer-specific survival in patients with ascending colon adenocarcinoma after surgery: a population-based study. World J Surg Oncol 2022; 20:126. [PMID: 35439983 PMCID: PMC9020108 DOI: 10.1186/s12957-022-02576-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 03/17/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND This study aimed to develop and validate a novel nomogram to predict the cancer-specific survival (CSS) of patients with ascending colon adenocarcinoma after surgery. METHODS Patients with ascending colon adenocarcinoma were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database from 1973 to 2015 and randomly divided into a training set (5930) and a validation set (2540). The cut-off values for age, tumour size and lymph node ratio (LNR) were calculated via X-tile software. In the training set, independent prognostic factors were identified using univariate and multivariate Cox analyses, and a nomogram incorporating these factors was subsequently built. Data from the validation set were used to assess the reliability and accuracy of the nomogram and then compared with the 8th edition of the American Joint Committee on Cancer (AJCC) tumour-node-metastasis (TNM) staging system. Furthermore, external validation was performed from a single institution in China. RESULTS A total of 8470 patients were enrolled from the SEER database, 5930 patients were allocated to the training set, 2540 were allocated to the internal validation set and a separate set of 473 patients was allocated to the external validation set. The optimal cut-off values of age, tumour size and lymph node ratio were 73 and 85, 33 and 75 and 4.9 and 32.8, respectively. Univariate and multivariate Cox multivariate regression revealed that age, AJCC 8th edition T, N and M stage, carcinoembryonic antigen (CEA), tumour differentiation, chemotherapy, perineural invasion and LNR were independent risk factors for patient CSS. The nomogram showed good predictive ability, as indicated by discriminative ability and calibration, with C statistics of 0.835 (95% CI, 0.823-0.847) and 0.848 (95% CI, 0.830-0.866) in the training and validation sets and 0.732 (95% CI, 0.664-0.799) in the external validation set. The nomogram showed favourable discrimination and calibration abilities and performed better than the AJCC TNM staging system. CONCLUSIONS A novel validated nomogram could effectively predict patients with ascending colon adenocarcinoma after surgery, and this predictive power may guide clinicians in accurate prognostic judgement.
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Affiliation(s)
- Yi Fan Zhang
- Comprehensive Cancer Center, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210000, China
- Department of Radiotherapy, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou, 221000, China
| | - Cheng Ma
- Department of Gastrointestinal Surgery, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou, 221000, China
| | - Xiao Ping Qian
- Comprehensive Cancer Center, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210000, China.
- Comprehensive Cancer Center, Nanjing Drum Tower Hospital, Medical School of Nanjing University, Clinical Cancer Institute of Nanjing University, Nanjing, 210000, China.
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Qiu H, Ding S, Liu J, Wang L, Wang X. Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer. Curr Oncol 2022; 29:1773-1795. [PMID: 35323346 PMCID: PMC8947571 DOI: 10.3390/curroncol29030146] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/28/2022] [Accepted: 03/03/2022] [Indexed: 12/29/2022] Open
Abstract
Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.
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Affiliation(s)
- Hang Qiu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
- Correspondence: (H.Q.); (X.W.)
| | - Shuhan Ding
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA;
| | - Jianbo Liu
- West China School of Medicine, Sichuan University, Chengdu 610041, China;
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Liya Wang
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Xiaodong Wang
- West China School of Medicine, Sichuan University, Chengdu 610041, China;
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
- Correspondence: (H.Q.); (X.W.)
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A Prediction Model for Tumor Recurrence in Stage II–III Colorectal Cancer Patients: From a Machine Learning Model to Genomic Profiling. Biomedicines 2022; 10:biomedicines10020340. [PMID: 35203549 PMCID: PMC8961774 DOI: 10.3390/biomedicines10020340] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/27/2022] [Accepted: 01/28/2022] [Indexed: 01/27/2023] Open
Abstract
Background: Colorectal cancer (CRC) is one of the most prevalent malignant diseases worldwide. Risk prediction for tumor recurrence is important for making effective treatment decisions and for the survival outcomes of patients with CRC after surgery. Herein, we aimed to explore a prediction algorithm and the risk factors for postoperative tumor recurrence using a machine learning (ML) approach with standardized pathology reports for patients with stage II and III CRC. Methods: Pertinent clinicopathological features were compiled from medical records and standardized pathology reports of patients with stage II and III CRC. Four ML models based on logistic regression (LR), random forest (RF), classification and regression decision trees (CARTs), and support vector machine (SVM) were applied for the development of the prediction algorithm. The area under the curve (AUC) of the ML models was determined in order to compare the prediction accuracy. Genomic studies were performed using a panel-targeted next-generation sequencing approach. Results: A total of 1073 patients who received curative intent surgery at the National Cheng Kung University Hospital between January 2004 and January 2019 were included. Based on conventional statistical methods, chemotherapy (p = 0.003), endophytic tumor configuration (p = 0.008), TNM stage III disease (p < 0.001), pT4 (p < 0.001), pN2 (p < 0.001), increased numbers of lymph node metastases (p < 0.001), higher lymph node ratios (LNR) (p < 0.001), lymphovascular invasion (p < 0.001), perineural invasion (p < 0.001), tumor budding (p = 0.004), and neoadjuvant chemoradiotherapy (p = 0.025) were found to be correlated with the tumor recurrence of patients with stage II–III CRC. While comparing the performance of different ML models for predicting cancer recurrence, the AUCs for LR, RF, CART, and SVM were found to be 0.678, 0.639, 0.593, and 0.581, respectively. The LR model had a better accuracy value of 0.87 and a specificity value of 1 in the testing set. Two prognostic factors, age and LNR, were selected by multivariable analysis and the four ML models. In terms of age, older patients received fewer cycles of chemotherapy and radiotherapy (p < 0.001). Right-sided colon tumors (p = 0.002), larger tumor sizes (p = 0.008) and tumor volumes (p = 0.049), TNM stage II disease (p < 0.001), and advanced pT3–4 stage diseases (p = 0.04) were found to be correlated with the older age of patients. However, pN2 diseases (p = 0.005), lymph node metastasis number (p = 0.001), LNR (p = 0.004), perineural invasion (p = 0.018), and overall survival rate (p < 0.001) were found to be decreased in older patients. Furthermore, PIK3CA and DNMT3A mutations (p = 0.032 and 0.039, respectively) were more frequently found in older patients with stage II–III CRC compared to their younger counterparts. Conclusions: This study demonstrated that ML models have a comparable predictive power for determining cancer recurrence in patients with stage II–III CRC after surgery. Advanced age and high LNR were significant risk factors for cancer recurrence, as determined by ML algorithms and multivariable analyses. Distinctive genomic profiles may contribute to discrete clinical behaviors and survival outcomes between patients of different age groups. Studies incorporating complete molecular and genomic profiles in cancer prediction models are beneficial for patients with stage II–III CRC.
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Liang F, Wang S, Zhang K, Liu TJ, Li JN. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World J Gastrointest Oncol 2022; 14:124-152. [PMID: 35116107 PMCID: PMC8790413 DOI: 10.4251/wjgo.v14.i1.124] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/19/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.
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Affiliation(s)
- Feng Liang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Shu Wang
- Department of Radiotherapy, Jilin University Second Hospital, Changchun 130041, Jilin Province, China
| | - Kai Zhang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Tong-Jun Liu
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Jian-Nan Li
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
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Ahmedt-Aristizabal D, Armin MA, Denman S, Fookes C, Petersson L. A survey on graph-based deep learning for computational histopathology. Comput Med Imaging Graph 2022; 95:102027. [DOI: 10.1016/j.compmedimag.2021.102027] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 11/25/2021] [Accepted: 12/04/2021] [Indexed: 12/21/2022]
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SAFRON: Stitching Across the Frontier Network for Generating Colorectal Cancer Histology Images. Med Image Anal 2021; 77:102337. [PMID: 35016078 DOI: 10.1016/j.media.2021.102337] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 10/13/2021] [Accepted: 12/14/2021] [Indexed: 12/12/2022]
Abstract
Automated synthesis of histology images has several potential applications including the development of data-efficient deep learning algorithms. In the field of computational pathology, where histology images are large in size and visual context is crucial, synthesis of large high-resolution images via generative modeling is an important but challenging task due to memory and computational constraints. To address this challenge, we propose a novel framework called SAFRON (Stitching Across the FROntier Network) to construct realistic, large high-resolution tissue images conditioned on input tissue component masks. The main novelty in the framework is integration of stitching in its loss function which enables generation of images of arbitrarily large sizes after training on relatively small image patches while preserving morphological features with minimal boundary artifacts. We have used the proposed framework for generating, to the best of our knowledge, the largest-sized synthetic histology images to date (up to 11K×8K pixels). Compared to existing approaches, our framework is efficient in terms of the memory required for training and computations needed for synthesizing large high-resolution images. The quality of generated images was assessed quantitatively using Frechet Inception Distance as well as by 7 trained pathologists, who assigned a realism score to a set of images generated by SAFRON. The average realism score across all pathologists for synthetic images was as high as that of real images. We also show that training with additional synthetic data generated by SAFRON can significantly boost prediction performance of gland segmentation and cancer detection algorithms in colorectal cancer histology images.
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Cianci P, Restini E. Artificial intelligence in colorectal cancer management. Artif Intell Cancer 2021; 2:79-89. [DOI: 10.35713/aic.v2.i6.79] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/22/2021] [Accepted: 12/29/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a new branch of computer science involving many disciplines and technologies. Since its application in the medical field, it has been constantly studied and developed. AI includes machine learning and neural networks to create new technologies or to improve existing ones. Various AI supporting systems are available for a personalized and novel strategy for the management of colorectal cancer (CRC). This mini-review aims to summarize the progress of research and possible clinical applications of AI in the investigation, early diagnosis, treatment, and management of CRC, to offer elements of knowledge as a starting point for new studies and future applications.
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Affiliation(s)
- Pasquale Cianci
- Department of Surgery and Traumatology, ASL BAT, Lorenzo Bonomo Hospital, Andria 76123, Puglia, Italy
| | - Enrico Restini
- Department of Surgery and Traumatology, ASL BAT, Lorenzo Bonomo Hospital, Andria 76123, Puglia, Italy
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Benecke J, Benecke C, Ciutan M, Dosius M, Vladescu C, Olsavszky V. Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania. PLoS Negl Trop Dis 2021; 15:e0009831. [PMID: 34723982 PMCID: PMC8584970 DOI: 10.1371/journal.pntd.0009831] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 11/11/2021] [Accepted: 09/22/2021] [Indexed: 12/04/2022] Open
Abstract
The epidemiology of neglected tropical diseases (NTD) is persistently underprioritized, despite NTD being widespread among the poorest populations and in the least developed countries on earth. This situation necessitates thorough and efficient public health intervention. Romania is at the brink of becoming a developed country. However, this South-Eastern European country appears to be a region that is susceptible to an underestimated burden of parasitic diseases despite recent public health reforms. Moreover, there is an evident lack of new epidemiologic data on NTD after Romania's accession to the European Union (EU) in 2007. Using the national ICD-10 dataset for hospitalized patients in Romania, we generated time series datasets for 2008-2018. The objective was to gain deep understanding of the epidemiological distribution of three selected and highly endemic parasitic diseases, namely, ascariasis, enterobiasis and cystic echinococcosis (CE), during this period and forecast their courses for the ensuing two years. Through descriptive and inferential analysis, we observed a decline in case numbers for all three NTD. Several distributional particularities at regional level emerged. Furthermore, we performed predictions using a novel automated time series (AutoTS) machine learning tool and could interestingly show a stable course for these parasitic NTD. Such predictions can help public health officials and medical organizations to implement targeted disease prevention and control. To our knowledge, this is the first study involving a retrospective analysis of ascariasis, enterobiasis and CE on a nationwide scale in Romania. It is also the first to use AutoTS technology for parasitic NTD.
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Affiliation(s)
- Johannes Benecke
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Cornelius Benecke
- Barcelona Institute for Global Health, University of Barcelona, Barcelona, Spain
| | - Marius Ciutan
- National School of Public Health Management and Professional Development, Bucharest, Romania
| | - Mihnea Dosius
- National School of Public Health Management and Professional Development, Bucharest, Romania
| | - Cristian Vladescu
- National School of Public Health Management and Professional Development, Bucharest, Romania
- University Titu Maiorescu, Faculty of Medicine, Bucharest, Romania
| | - Victor Olsavszky
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
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Tedesco S, Andrulli M, Larsson MA, Kelly D, Timmons S, Alamaki A, Barton J, Condell J, O'Flynn B, Nordstrom A. Investigation of the analysis of wearable data for cancer-specific mortality prediction in older adults. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1848-1851. [PMID: 34891647 DOI: 10.1109/embc46164.2021.9630370] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cancer is an aggressive disease which imparts a tremendous socio-economic burden on the international community. Early detection is an important aspect in improving survival rates for cancer sufferers; however, very few studies have investigated the possibility of predicting which people have the highest risk to develop this disease, even years before the traditional symptoms first occur. In this paper, a dataset from a longitudinal study which was collected among 2291 70-year olds in Sweden has been analyzed to investigate the possibility for predicting 2-7 year cancer-specific mortality. A tailored ensemble model has been developed to tackle this highly imbalanced dataset. The performance with different feature subsets has been investigated to evaluate the impact that heterogeneous data sources may have on the overall model. While a full-features model shows an Area Under the ROC Curve (AUC-ROC) of 0.882, a feature subset which only includes demographics, self-report health and lifestyle data, and wearable dataset collected in free-living environments presents similar performance (AUC-ROC: 0.857). This analysis confirms the importance of wearable technology for providing unbiased health markers and suggests its possible use in the accurate prediction of 2-7 year cancer-related mortality in older adults.
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Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning. Diagnostics (Basel) 2021; 11:diagnostics11081398. [PMID: 34441332 PMCID: PMC8394415 DOI: 10.3390/diagnostics11081398] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 07/25/2021] [Accepted: 07/28/2021] [Indexed: 01/03/2023] Open
Abstract
Colorectal cancer is one of the leading causes of cancer-related death worldwide. The early diagnosis of colon cancer not only reduces mortality but also reduces the burden related to the treatment strategies such as chemotherapy and/or radiotherapy. However, when the microscopic examination of the suspected colon tissue sample is carried out, it becomes a tedious and time-consuming job for the pathologists to find the abnormality in the tissue. In addition, there may be interobserver variability that might lead to conflict in the final diagnosis. As a result, there is a crucial need of developing an intelligent automated method that can learn from the patterns themselves and assist the pathologist in making a faster, accurate, and consistent decision for determining the normal and abnormal region in the colorectal tissues. Moreover, the intelligent method should be able to localize the abnormal region in the whole slide image (WSI), which will make it easier for the pathologists to focus on only the region of interest making the task of tissue examination faster and lesser time-consuming. As a result, artificial intelligence (AI)-based classification and localization models are proposed for determining and localizing the abnormal regions in WSI. The proposed models achieved F-score of 0.97, area under curve (AUC) 0.97 with pretrained Inception-v3 model, and F-score of 0.99 and AUC 0.99 with customized Inception-ResNet-v2 Type 5 (IR-v2 Type 5) model.
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Shui L, Li X, Peng Y, Tian J, Li S, He D, Li A, Tian B, Li M, Gao H, An N, Yi C, Cao D. The germline/somatic DNA damage repair gene mutations modulate the therapeutic response in Chinese patients with advanced pancreatic ductal adenocarcinoma. J Transl Med 2021; 19:301. [PMID: 34247626 PMCID: PMC8273977 DOI: 10.1186/s12967-021-02972-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 04/17/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is a fatal disease with molecular heterogeneity, inducing differences in biological behavior, and therapeutic strategy. NGS profiles of pathogenic alterations in the Chinese PDAC population are limited. We conducted a retrospective study to investigate the predictive role of DNA damage repair (DDR) mutations in precision medicine. METHODS The NGS profiles were performed on resected tissues from 195 Chinese PDAC patients. Baseline clinical or genetic characteristics and survival status were collected. The Kaplan-Meier survival analyses were performed by the R version 3.6.1. RESULTS The main driver genes were KRAS, TP53, CDKN2A, and SMAD4. Advanced patients with KRAS mutation showed a worse OS than KRAS wild-type (p = 0.048). DDR pathogenic deficiency was identified in 30 (15.38%) of overall patients, mainly involving BRCA2 (n = 9, 4.62%), ATM (n = 8, 4.10%) and RAD50 genes (n = 3, 1.54%). No significance of OS between patients with or without DDR mutations (p = 0.88). But DDR mutation was an independent prognostic factor for survival analysis of advanced PDAC patients (p = 0.032). For DDR mutant patients, treatment with platinum-based chemotherapy (p = 0.0096) or olaparib (p = 0.018) respectively improved the overall survival. No statistical difference between tumor mutation burden (TMB) and DDR mutations was identified. Treatment of PD-1 blockades did not bring significantly improved OS to DDR-mutated patients than the naive DDR group (p = 0.14). CONCLUSIONS In this retrospective study, we showed the role of germline and somatic DDR mutation in predicting the efficacy of olaparib and platinum-based chemotherapy in Chinese patients. However, the value of DDR mutation in the prediction of hypermutation status and the sensitivity to the PD-1 blockade needed further investigation.
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Affiliation(s)
- Lin Shui
- Department of Abdominal Oncology, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
- Department of Oncology Radiation and Chemotherapy, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Xiaofen Li
- Department of Abdominal Oncology, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Yang Peng
- Department of Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jiangfang Tian
- Department of Oncology, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, China
| | - Shuangshuang Li
- Department of Abdominal Oncology, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Du He
- Department of pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Ang Li
- Pancreatic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Bole Tian
- Pancreatic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Mao Li
- Pancreatic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Heli Gao
- Department of Oncology, the Cancer Hospital of Fudan University, Shanghai, China
| | - Ning An
- Department of oncology, the People’s Hospital of Sichuan Province, Chengdu, China
| | - Cheng Yi
- Department of Abdominal Oncology, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Dan Cao
- Department of Abdominal Oncology, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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Parimbelli E, Wilk S, Cornet R, Sniatala P, Sniatala K, Glaser SLC, Fraterman I, Boekhout AH, Ottaviano M, Peleg M. A review of AI and Data Science support for cancer management. Artif Intell Med 2021; 117:102111. [PMID: 34127240 DOI: 10.1016/j.artmed.2021.102111] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 12/23/2020] [Accepted: 05/11/2021] [Indexed: 02/09/2023]
Abstract
INTRODUCTION Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home. OBJECTIVE Our main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt. METHODS We designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient's state from it and deliver coaching/behavior change interventions. RESULTS Starting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching. CONCLUSION Development of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.
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Affiliation(s)
| | - S Wilk
- Poznan University of Technology, Poland
| | - R Cornet
- Amsterdam University Medical Centre, the Netherlands
| | | | | | - S L C Glaser
- Amsterdam University Medical Centre, the Netherlands
| | - I Fraterman
- Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - A H Boekhout
- Netherlands Cancer Institute, Amsterdam, the Netherlands
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He L, Luo L, Hou X, Liao D, Liu R, Ouyang C, Wang G. Predicting venous thromboembolism in hospitalized trauma patients: a combination of the Caprini score and data-driven machine learning model. BMC Emerg Med 2021; 21:60. [PMID: 33971809 PMCID: PMC8111727 DOI: 10.1186/s12873-021-00447-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 04/06/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Venous thromboembolism (VTE) is a common complication of hospitalized trauma patients and has an adverse impact on patient outcomes. However, there is still a lack of appropriate tools for effectively predicting VTE for trauma patients. We try to verify the accuracy of the Caprini score for predicting VTE in trauma patients, and further improve the prediction through machine learning algorithms. METHODS We retrospectively reviewed emergency trauma patients who were admitted to a trauma center in a tertiary hospital from September 2019 to March 2020. The data in the patient's electronic health record (EHR) and the Caprini score were extracted, combined with multiple feature screening methods and the random forest (RF) algorithm to constructs the VTE prediction model, and compares the prediction performance of (1) using only Caprini score; (2) using EHR data to build a machine learning model; (3) using EHR data and Caprini score to build a machine learning model. True Positive Rate (TPR), False Positive Rate (FPR), Area Under Curve (AUC), accuracy, and precision were reported. RESULTS The Caprini score shows a good VTE prediction effect on the trauma hospitalized population when the cut-off point is 11 (TPR = 0.667, FPR = 0.227, AUC = 0.773), The best prediction model is LASSO+RF model combined with Caprini Score and other five features extracted from EHR data (TPR = 0.757, FPR = 0.290, AUC = 0.799). CONCLUSION The Caprini score has good VTE prediction performance in trauma patients, and the use of machine learning methods can further improve the prediction performance.
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Affiliation(s)
- Lingxiao He
- Trauma Center of West China Hospital/West China School of Nursing, Sichuan University, Guo Xue Road 37#, Chengdu, 610041, China
| | - Lei Luo
- College of Chemical Engineering, Sichuan University, Chengdu, China
| | - Xiaoling Hou
- Trauma Center of West China Hospital/West China School of Nursing, Sichuan University, Guo Xue Road 37#, Chengdu, 610041, China
| | - Dengbin Liao
- Trauma Center of West China Hospital/West China School of Nursing, Sichuan University, Guo Xue Road 37#, Chengdu, 610041, China
| | - Ran Liu
- Engineering Research Center of Medical Information Technology, Ministry of Education, West China Hospital of Sichuan University, Chengdu, China
| | - Chaowei Ouyang
- Trauma Center of West China Hospital/West China School of Nursing, Sichuan University, Guo Xue Road 37#, Chengdu, 610041, China
| | - Guanglin Wang
- Trauma Center of West China Hospital/West China School of Medicine, Sichuan University, Guo Xue Road 37#, Chengdu, 610041, China.
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Guo C, Wang J, Wang Y, Qu X, Shi Z, Meng Y, Qiu J, Hua K. Novel artificial intelligence machine learning approaches to precisely predict survival and site-specific recurrence in cervical cancer: A multi-institutional study. Transl Oncol 2021; 14:101032. [PMID: 33618238 PMCID: PMC7907920 DOI: 10.1016/j.tranon.2021.101032] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 01/24/2021] [Accepted: 01/28/2021] [Indexed: 12/09/2022] Open
Abstract
BACKGROUND Machine learning (ML) has been gradually integrated into oncologic research but seldom applied to predict cervical cancer (CC), and no model has been reported to predict survival and site-specific recurrence simultaneously. Thus, we aimed to develop ML models to predict survival and site-specific recurrence in CC and to guide individual surveillance. METHODS We retrospectively collected data on CC patients from 2006 to 2017 in four hospitals. The survival or recurrence predictive value of the variables was analyzed using multivariate Cox, principal component, and K-means clustering analyses. The predictive performances of eight ML models were compared with logistic or Cox models. A novel web-based predictive calculator was developed based on the ML algorithms. RESULTS This study included 5112 women for analysis (268 deaths, 343 recurrences): (1) For site-specific recurrence, larger tumor size was associated with local recurrence, while positive lymph nodes were associated with distant recurrence. (2) The ML models exhibited better prognostic predictive performance than traditional models. (3) The ML models were superior to traditional models when multiple variables were used. (4) A novel predictive web-based calculator was developed and externally validated to predict survival and site-specific recurrence. CONCLUSION ML models might be a better analytic approach in CC prognostic prediction than traditional models as they can predict survival and site-specific recurrence simultaneously, especially when using multiple variables. Moreover, our novel web-based calculator may provide clinicians with useful information and help them make individual postoperative follow-up plans and further treatment strategies.
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Affiliation(s)
- Chenyan Guo
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, 419 Fangxie Road, Shanghai 200011, China; Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, 413 Zhaozhou Road, Shanghai 200011, China
| | - Jue Wang
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, 419 Fangxie Road, Shanghai 200011, China; Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, 413 Zhaozhou Road, Shanghai 200011, China
| | - Yongming Wang
- Shanghai Changjiang Science and Technology Development Co. LTD, China
| | - Xinyu Qu
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, 419 Fangxie Road, Shanghai 200011, China; Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, 413 Zhaozhou Road, Shanghai 200011, China
| | - Zhiwen Shi
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, 419 Fangxie Road, Shanghai 200011, China; Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, 413 Zhaozhou Road, Shanghai 200011, China
| | - Yan Meng
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, 419 Fangxie Road, Shanghai 200011, China; Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, 413 Zhaozhou Road, Shanghai 200011, China
| | - Junjun Qiu
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, 419 Fangxie Road, Shanghai 200011, China; Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, 413 Zhaozhou Road, Shanghai 200011, China.
| | - Keqin Hua
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, 419 Fangxie Road, Shanghai 200011, China; Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, 413 Zhaozhou Road, Shanghai 200011, China.
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Yi PS, Hu CJ, Li CH, Yu F. Clinical value of artificial intelligence in hepatocellular carcinoma: Current status and prospect. Artif Intell Gastroenterol 2021; 2:42-55. [DOI: 10.35712/aig.v2.i2.42] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/25/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most commonly diagnosed type of liver cancer and the fourth leading cause of cancer-related mortality worldwide. The early identification of HCC and effective treatments for it have been challenging. Due to the sufficient compensatory ability of early patients and its nonspecific symptoms, HCC is more likely to escape diagnosis in the incipient stage, during which patients can achieve a more satisfying overall survival if they undergo resection or liver transplantation. Patients at advanced stages can profit from radical therapies in a limited way. In order to improve the unfavorable prognosis of HCC, diagnostic ability and treatment efficiency must be improved. The past decade has seen rapid advancements in artificial intelligence, underlying its unique usefulness in almost every field, including that of medicine. Herein, we sought and reviewed studies that put emphasis on artificial intelligence and HCC.
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Affiliation(s)
- Peng-Sheng Yi
- Department of Hepato-Biliary-Pancreas II, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Chen-Jun Hu
- Department of Hepato-Biliary-Pancreas II, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Chen-Hui Li
- Department of Obstetrics and Gynecology, Nanchong Traditional Chinese Medicine Hospital, Nanchong 637000, Sichuan Province, China
| | - Fei Yu
- Department of Radiology, Yingshan County People’s Hospital, Nanchong 610041, Sichuan Province, China
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Increasing prediction accuracy of pathogenic staging by sample augmentation with a GAN. PLoS One 2021; 16:e0250458. [PMID: 33905431 PMCID: PMC8078779 DOI: 10.1371/journal.pone.0250458] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 04/07/2021] [Indexed: 11/19/2022] Open
Abstract
Accurate prediction of cancer stage is important in that it enables more appropriate treatment for patients with cancer. Many measures or methods have been proposed for more accurate prediction of cancer stage, but recently, machine learning, especially deep learning-based methods have been receiving increasing attention, mostly owing to their good prediction accuracy in many applications. Machine learning methods can be applied to high throughput DNA mutation or RNA expression data to predict cancer stage. However, because the number of genes or markers generally exceeds 10,000, a considerable number of data samples is required to guarantee high prediction accuracy. To solve this problem of a small number of clinical samples, we used a Generative Adversarial Networks (GANs) to augment the samples. Because GANs are not effective with whole genes, we first selected significant genes using DNA mutation data and random forest feature ranking. Next, RNA expression data for selected genes were expanded using GANs. We compared the classification accuracies using original dataset and expanded datasets generated by proposed and existing methods, using random forest, Deep Neural Networks (DNNs), and 1-Dimensional Convolutional Neural Networks (1DCNN). When using the 1DCNN, the F1 score of GAN5 (a 5-fold increase in data) was improved by 39% in relation to the original data. Moreover, the results using only 30% of the data were better than those using all of the data. Our attempt is the first to use GAN for augmentation using numeric data for both DNA and RNA. The augmented datasets obtained using the proposed method demonstrated significantly increased classification accuracy for most cases. By using GAN and 1DCNN in the prediction of cancer stage, we confirmed that good results can be obtained even with small amounts of samples, and it is expected that a great deal of the cost and time required to obtain clinical samples will be reduced. The proposed sample augmentation method could also be applied for other purposes, such as prognostic prediction or cancer classification.
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Onthoni DD, Sheng TW, Sahoo PK, Wang LJ, Gupta P. Deep Learning Assisted Localization of Polycystic Kidney on Contrast-Enhanced CT Images. Diagnostics (Basel) 2020; 10:diagnostics10121113. [PMID: 33371503 PMCID: PMC7767504 DOI: 10.3390/diagnostics10121113] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 12/14/2020] [Accepted: 12/16/2020] [Indexed: 12/20/2022] Open
Abstract
Total Kidney Volume (TKV) is essential for analyzing the progressive loss of renal function in Autosomal Dominant Polycystic Kidney Disease (ADPKD). Conventionally, to measure TKV from medical images, a radiologist needs to localize and segment the kidneys by defining and delineating the kidney's boundary slice by slice. However, kidney localization is a time-consuming and challenging task considering the unstructured medical images from big data such as Contrast-enhanced Computed Tomography (CCT). This study aimed to design an automatic localization model of ADPKD using Artificial Intelligence. A robust detection model using CCT images, image preprocessing, and Single Shot Detector (SSD) Inception V2 Deep Learning (DL) model is designed here. The model is trained and evaluated with 110 CCT images that comprise 10,078 slices. The experimental results showed that our derived detection model outperformed other DL detectors in terms of Average Precision (AP) and mean Average Precision (mAP). We achieved mAP = 94% for image-wise testing and mAP = 82% for subject-wise testing, when threshold on Intersection over Union (IoU) = 0.5. This study proves that our derived automatic detection model can assist radiologist in locating and classifying the ADPKD kidneys precisely and rapidly in order to improve the segmentation task and TKV calculation.
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Affiliation(s)
- Djeane Debora Onthoni
- Department of Computer Science and Information Engineering, Chang Gung University, Guishan 33302, Taiwan; (D.D.O.); (P.G.)
| | - Ting-Wen Sheng
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Guishan 33302, Taiwan;
- Department of Medical Imaging and Intervention, New Taipei Municipal TuCheng Hospital, Chang Gung Medical Foundation, New Taipei City 236017, Taiwan
| | - Prasan Kumar Sahoo
- Department of Computer Science and Information Engineering, Chang Gung University, Guishan 33302, Taiwan; (D.D.O.); (P.G.)
- Division of Colon and Rectal Surgery, Chang Gung Memorial Hospital, Linkou 33305, Taiwan
- Correspondence: (P.K.S.); (L.-J.W.); Tel.: +886-3-211-8800 (ext. 3804) (P.K.S.)
| | - Li-Jen Wang
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Guishan 33302, Taiwan;
- Department of Medical Imaging and Intervention, New Taipei Municipal TuCheng Hospital, Chang Gung Medical Foundation, New Taipei City 236017, Taiwan
- Correspondence: (P.K.S.); (L.-J.W.); Tel.: +886-3-211-8800 (ext. 3804) (P.K.S.)
| | - Pushpanjali Gupta
- Department of Computer Science and Information Engineering, Chang Gung University, Guishan 33302, Taiwan; (D.D.O.); (P.G.)
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Chan P, Zhou X, Wang N, Liu Q, Bruno R, Jin JY. Application of Machine Learning for Tumor Growth Inhibition - Overall Survival Modeling Platform. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 10:59-66. [PMID: 33280255 PMCID: PMC7825187 DOI: 10.1002/psp4.12576] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022]
Abstract
Machine learning (ML) was used to leverage tumor growth inhibition (TGI) metrics to characterize the relationship with overall survival (OS) as a novel approach and to compare with traditional TGI‐OS modeling methods. Historical dataset from a phase III non‐small cell lung cancer study (OAK, atezolizumab vs. docetaxel, N = 668) was used. ML methods support the validity of TGI metrics in predicting OS. With lasso, the best model with TGI metrics outperforms the best model without TGI metrics. Boosting was the best linear ML method for this dataset with reduced estimation bias and lowest Brier score, suggesting better prediction accuracy. Random forest did not outperform linear ML methods despite hyperparameter optimization. Kernel machine was marginally the best nonlinear ML method for this dataset and uncovered nonlinear and interaction effects. Nonlinear ML may improve prediction by capturing nonlinear effects and covariate interactions, but its predictive performance and value need further evaluation with larger datasets.
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Affiliation(s)
- Phyllis Chan
- Clinical Pharmacology, Roche/Genentech, South San Francisco, California, USA
| | - Xiaofei Zhou
- Clinical Pharmacology, Roche/Genentech, South San Francisco, California, USA.,Formerly of Department of Statistics, The Ohio State University, Columbus, Ohio, USA
| | - Nina Wang
- Clinical Pharmacology, Roche/Genentech, South San Francisco, California, USA
| | - Qi Liu
- Clinical Pharmacology, Roche/Genentech, South San Francisco, California, USA
| | - René Bruno
- Clinical Pharmacology, Roche/Genentech, Marseille, France
| | - Jin Y Jin
- Clinical Pharmacology, Roche/Genentech, South San Francisco, California, USA
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Wang Y, He X, Nie H, Zhou J, Cao P, Ou C. Application of artificial intelligence to the diagnosis and therapy of colorectal cancer. Am J Cancer Res 2020; 10:3575-3598. [PMID: 33294256 PMCID: PMC7716173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 10/14/2020] [Indexed: 06/12/2023] Open
Abstract
Artificial intelligence (AI) is a relatively new branch of computer science involving many disciplines and technologies, including robotics, speech recognition, natural language and image recognition or processing, and machine learning. Recently, AI has been widely applied in the medical field. The effective combination of AI and big data can provide convenient and efficient medical services for patients. Colorectal cancer (CRC) is a common type of gastrointestinal cancer. The early diagnosis and treatment of CRC are key factors affecting its prognosis. This review summarizes the research progress and clinical application value of AI in the investigation, early diagnosis, treatment, and prognosis of CRC, to provide a comprehensive theoretical basis for AI as a promising diagnostic and treatment tool for CRC.
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Affiliation(s)
- Yutong Wang
- Department of Pathology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
| | - Xiaoyun He
- Department of Pathology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
- Department of Endocrinology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
| | - Hui Nie
- Department of Pathology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
| | - Jianhua Zhou
- Department of Pathology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
| | - Pengfei Cao
- Department of Hematology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
| | - Chunlin Ou
- Department of Pathology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
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