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Ay OF, Firat D, Özçetin B, Ocakoglu G, Ozcan SGG, Bakır Ş, Ocak B, Taşkin AK. Role of pelvimetry in predicting surgical outcomes and morbidity in rectal cancer surgery: A retrospective analysis. World J Gastrointest Surg 2025; 17:104726. [DOI: 10.4240/wjgs.v17.i4.104726] [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: 12/29/2024] [Revised: 01/28/2025] [Accepted: 02/27/2025] [Indexed: 03/29/2025] Open
Abstract
BACKGROUND Rectal cancer has increased in incidence, and surgery remains the cornerstone of multimodal treatment. Pelvic anatomy, particularly a narrow pelvis, poses challenges in rectal cancer surgery, potentially affecting oncological outcomes and postoperative complications.
AIM To investigate the relationship between radiologically assessed pelvic anatomy and surgical outcomes as well as the impact on local recurrence following rectal cancer surgery.
METHODS We retrospectively analyzed 107 patients with rectal adenocarcinoma treated with elective rectal surgery between January 1, 2017, and September 1, 2022. Pelvimetric measurements were performed using computed tomography (CT)-based two-dimensional methods (n = 77) by assessing the pelvic inlet area in mm², and magnetic resonance imaging (MRI)-based three-dimensional techniques (n = 52) using the pelvic cavity index (PCI). Patient demographic, clinical, radiological, surgical, and pathological characteristics were collected and analyzed in relation to their pelvimetric data.
RESULTS When patients were categorized based on CT measurements into narrow and normal/wide pelvis groups, a significant association was observed with male sex, and a lower BMI was more common in the narrow pelvis group (P = 0.002 for both). A significant association was found between a narrow pelvic structure, indicated by low PCI, and increased surgical morbidity (P = 0.049). Advanced age (P = 0.003) and male sex (P = 0.020) were significantly correlated with higher surgical morbidity. Logistic regression analysis identified four parameters that were significantly correlated with local recurrence: older age, early perioperative readmission, longer operation time, and a lower number of dissected lymph nodes (P < 0.05). However, there were no significant differences between the narrow and normal/wide pelvis groups in terms of the operation time, estimated blood loss, or overall local recurrence rate (P > 0.05).
CONCLUSION MRI-based pelvimetry may be valuable in predicting surgical difficulty and morbidity in rectal cancer surgery, as indicated by the PCI. The observed correlation between low PCI and increased surgical morbidity suggests the potential importance of a preoperative MRI-based pelvimetric evaluation. In contrast, CT-based pelvimetry did not show significant differences in predicting surgical outcomes or cancer recurrence, indicating that the utility of pelvimetry alone may be limited in these respects.
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Affiliation(s)
- Oguzhan Fatih Ay
- Department of General Surgery, Kahramanmaras Necip Fazıl City Hospital, Kahramanmaras 46140, Türkiye
| | - Deniz Firat
- Department of General Surgery, University of Health Science, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa 16110, Türkiye
| | - Bülent Özçetin
- Department of General Surgery, University of Health Science, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa 16110, Türkiye
| | - Gokhan Ocakoglu
- Department of Biostatistics, Uludag University Faculty of Medicine, Bursa 16059, Türkiye
| | - Seray Gizem Gur Ozcan
- Department of Radiology, University of Health Science, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa 16110, Türkiye
| | - Şule Bakır
- Department of Pathology, University of Health Science, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa 16110, Türkiye
| | - Birol Ocak
- Department of Medical Oncology, University of Health Science, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa 16110, Türkiye
| | - Ali Kemal Taşkin
- Department of General Surgery, University of Health Science, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa 16110, Türkiye
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Fujimoto T, Tamura K, Nagayoshi K, Mizuuchi Y, Goto F, Matsuda H, Horioka K, Shindo K, Nakata K, Ohuchida K, Nakamura M. Simple pelvimetry predicts the pelvic manipulation time in robot-assisted low and ultra-low anterior resection for rectal cancer. Surg Today 2024; 54:1184-1192. [PMID: 38548999 DOI: 10.1007/s00595-024-02820-2] [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: 11/28/2023] [Accepted: 02/19/2024] [Indexed: 09/21/2024]
Abstract
PURPOSE This study explored the difficulty factors in robot-assisted low and ultra-low anterior resection, focusing on simple measurements of the pelvic anatomy. METHODS This was a retrospective analysis of the clinical data of 61 patients who underwent robot-assisted low and ultra-low anterior resection for rectal cancer between October 2018 and April 2023. The relationship between the operative time in the pelvic phase and clinicopathological data, especially pelvic anatomical parameters measured on X-ray and computed tomography (CT), was evaluated. The operative time in the pelvic phase was defined as the time between mobilization from the sacral promontory and rectal resection. RESULTS Robot-assisted low and ultra-low anterior resections were performed in 32 and 29 patients, respectively. The median operative time in the pelvic phase was 126 (range, 31-332) min. A multiple linear regression analysis showed that a short distance from the anal verge to the lower edge of the cancer, a narrow area comprising the iliopectineal line, short anteroposterior and transverse pelvic diameters, and a small angle of the pelvic mesorectum were associated with a prolonged operative time in the pelvic phase. CONCLUSION Simple pelvic anatomical measurements using abdominal radiography and CT may predict the pelvic manipulation time in robot-assisted surgery for rectal cancer.
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Affiliation(s)
- Takaaki Fujimoto
- Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
| | - Koji Tamura
- Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Kinuko Nagayoshi
- Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Yusuke Mizuuchi
- Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Fumika Goto
- Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Hironao Matsuda
- Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Kohei Horioka
- Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Koji Shindo
- Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Kohei Nakata
- Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Kenoki Ohuchida
- Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Masafumi Nakamura
- Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
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Li X, Zhou Z, Zhu B, Wu Y, Xing C. Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer. World J Surg Oncol 2024; 22:111. [PMID: 38664824 PMCID: PMC11044303 DOI: 10.1186/s12957-024-03389-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 04/14/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND The objective of this study is to develop and validate a machine learning (ML) prediction model for the assessment of laparoscopic total mesorectal excision (LaTME) surgery difficulty, as well as to identify independent risk factors that influence surgical difficulty. Establishing a nomogram aims to assist clinical practitioners in formulating more effective surgical plans before the procedure. METHODS This study included 186 patients with rectal cancer who underwent LaTME from January 2018 to December 2020. They were divided into a training cohort (n = 131) versus a validation cohort (n = 55). The difficulty of LaTME was defined based on Escal's et al. scoring criteria with modifications. We utilized Lasso regression to screen the preoperative clinical characteristic variables and intraoperative information most relevant to surgical difficulty for the development and validation of four ML models: logistic regression (LR), support vector machine (SVM), random forest (RF), and decision tree (DT). The performance of the model was assessed based on the area under the receiver operating characteristic curve(AUC), sensitivity, specificity, and accuracy. Logistic regression-based column-line plots were created to visualize the predictive model. Consistency statistics (C-statistic) and calibration curves were used to discriminate and calibrate the nomogram, respectively. RESULTS In the validation cohort, all four ML models demonstrate good performance: SVM AUC = 0.987, RF AUC = 0.953, LR AUC = 0.950, and DT AUC = 0.904. To enhance visual evaluation, a logistic regression-based nomogram has been established. Predictive factors included in the nomogram are body mass index (BMI), distance between the tumor to the dentate line ≤ 10 cm, radiodensity of visceral adipose tissue (VAT), area of subcutaneous adipose tissue (SAT), tumor diameter >3 cm, and comorbid hypertension. CONCLUSION In this study, four ML models based on intraoperative and preoperative risk factors and a nomogram based on logistic regression may be of help to surgeons in evaluating the surgical difficulty before operation and adopting appropriate responses and surgical protocols.
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Affiliation(s)
- Xiangyong Li
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu province, China
| | - Zeyang Zhou
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu province, China
| | - Bing Zhu
- Department of Anesthesiology, Dongtai People's Hospital, Yancheng, Jiangsu Province, China
| | - Yong Wu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu province, China.
| | - Chungen Xing
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu province, China.
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Yu M, Yuan Z, Li R, Shi B, Wan D, Dong X. Interpretable machine learning model to predict surgical difficulty in laparoscopic resection for rectal cancer. Front Oncol 2024; 14:1337219. [PMID: 38380369 PMCID: PMC10878416 DOI: 10.3389/fonc.2024.1337219] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 01/15/2024] [Indexed: 02/22/2024] Open
Abstract
Background Laparoscopic total mesorectal excision (LaTME) is standard surgical methods for rectal cancer, and LaTME operation is a challenging procedure. This study is intended to use machine learning to develop and validate prediction models for surgical difficulty of LaTME in patients with rectal cancer and compare these models' performance. Methods We retrospectively collected the preoperative clinical and MRI pelvimetry parameter of rectal cancer patients who underwent laparoscopic total mesorectal resection from 2017 to 2022. The difficulty of LaTME was defined according to the scoring criteria reported by Escal. Patients were randomly divided into training group (80%) and test group (20%). We selected independent influencing features using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression method. Adopt synthetic minority oversampling technique (SMOTE) to alleviate the class imbalance problem. Six machine learning model were developed: light gradient boosting machine (LGBM); categorical boosting (CatBoost); extreme gradient boost (XGBoost), logistic regression (LR); random forests (RF); multilayer perceptron (MLP). The area under receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity and F1 score were used to evaluate the performance of the model. The Shapley Additive Explanations (SHAP) analysis provided interpretation for the best machine learning model. Further decision curve analysis (DCA) was used to evaluate the clinical manifestations of the model. Results A total of 626 patients were included. LASSO regression analysis shows that tumor height, prognostic nutrition index (PNI), pelvic inlet, pelvic outlet, sacrococcygeal distance, mesorectal fat area and angle 5 (the angle between the apex of the sacral angle and the lower edge of the pubic bone) are the predictor variables of the machine learning model. In addition, the correlation heatmap shows that there is no significant correlation between these seven variables. When predicting the difficulty of LaTME surgery, the XGBoost model performed best among the six machine learning models (AUROC=0.855). Based on the decision curve analysis (DCA) results, the XGBoost model is also superior, and feature importance analysis shows that tumor height is the most important variable among the seven factors. Conclusions This study developed an XGBoost model to predict the difficulty of LaTME surgery. This model can help clinicians quickly and accurately predict the difficulty of surgery and adopt individualized surgical methods.
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Affiliation(s)
| | | | | | | | - Daiwei Wan
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaoqiang Dong
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
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Huang CK, Shih CH, Kao YS. Elderly Rectal Cancer: An Updated Review. Curr Oncol Rep 2024; 26:181-190. [PMID: 38270849 DOI: 10.1007/s11912-024-01495-9] [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] [Accepted: 01/03/2024] [Indexed: 01/26/2024]
Abstract
PURPOSE OF REVIEW Treatment of rectal cancer patients of advanced age should be modulated by life expectancy and tolerance. Due to the rapid advance of this field, we aim to conduct an updated review of this topic. RECENT FINDINGS The field of elderly rectal cancer has advanced a lot. This review covers all the treatment aspects of elderly rectal cancer, including the prognostic factor, surgery, radiotherapy, chemotherapy, and palliative treatment. We also provide the future aspect of the management of elderly rectal cancer. The advancement of prognostic factor research, surgery, radiotherapy, chemotherapy, and palliative treatment has made the care of elderly rectal cancer patients better. The future of these fields should focus on the definition of the elderly and the application of particle therapy.
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Affiliation(s)
- Chih-Kai Huang
- Division of General Surgery, Department of Surgery, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Chi-Hsiu Shih
- Division of Hematology and Oncology, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
| | - Yung-Shuo Kao
- Department of Radiation Oncology, Taoyuan General Hospital, Ministry of Health and Welfare, No.1492, Zhongshan Rd., Taoyuan Dist., Taoyuan City, 330, Taiwan.
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