Xiong FC, Luo MP, Ruan SM. Construction of a risk prediction model for early postoperative recurrence in stage II/III colorectal cancer. World J Gastrointest Oncol 2025; 17(9): 107968 [DOI: 10.4251/wjgo.v17.i9.107968]
Corresponding Author of This Article
Shan-Ming Ruan, MD, Department of Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, No. 54 Youdian Road, Hangzhou 31000, Zhejiang Province, China. shanmingruan@zcmu.edu.cn
Research Domain of This Article
Oncology
Article-Type of This Article
Retrospective Cohort Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Feng-Chun Xiong, Ming-Peng Luo, Shan-Ming Ruan, Department of Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 31000, Zhejiang Province, China
Co-first authors: Feng-Chun Xiong and Ming-Peng Luo.
Author contributions: Xiong FC contributed to the data collection, compilation, and writing of the manuscript; Luo MP contributed to the design and data analysis of the work; Xiong FC and Luo MP contributed equally to this article, they are the co-first authors of this manuscript; Ruan SM was responsible for the organization, conceptualization, and supervision of the study; and all authors participated in and approved the final manuscript.
Institutional review board statement: This study was approved by the Medical Ethics Committee of Zhejiang Provincial Hospital of Traditional Chinese Medicine, approval No. 2025-KLS-114-01.
Informed consent statement: The informed consent was waived by the Institutional Review Board.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The data supporting the findings of this study are available upon reasonable request from the corresponding author. Contact: Shanmingruan@zcmu.edu.cn.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Shan-Ming Ruan, MD, Department of Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, No. 54 Youdian Road, Hangzhou 31000, Zhejiang Province, China. shanmingruan@zcmu.edu.cn
Received: April 2, 2025 Revised: May 26, 2025 Accepted: August 1, 2025 Published online: September 15, 2025 Processing time: 166 Days and 12.4 Hours
Abstract
BACKGROUND
Colorectal cancer (CRC) recurrence within a year post-surgery poses significant challenges for stage II/III patients. Few models currently predict this early recurrence with multi-dimensional considerations for risk stratification.
AIM
To develop a model for predicting the risk of recurrence within one year after surgery in patients with stage II/III CRC.
METHODS
We conducted a retrospective cohort study at Zhejiang Provincial Hospital of Chinese Medicine, including 349 stage II/III CRC patients. Clinical data were collected, and the dataset was randomly divided into training (n = 244) and testing (n = 105) sets. Univariate and multivariate logistic regression analyses identified risk factors for postoperative recurrence. Then a nomogram model was constructed and evaluated via receiver operating characteristic curves, calibration curves and decision curve analysis.
RESULTS
During the one-year follow-up, 10.9% (38/349) of patients experienced recurrence. Univariate analysis identified tumor size, lymph node metastasis (N2 stage), neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, fatigue, and appetite loss as significant correlates of recurrence. Multivariate logistic regression confirmed N2 stage, appetite loss, tumor size, and neutrophil-to-lymphocyte ratio as independent risk factors. The nomogram model showed excellent performance. The area under the receiver operating characteristic was 0.98 (95% confidence interval: 0.97-1.00) in training set and 0.91 (95% confidence interval: 0.84-0.97) in testing set. The decision curve analysis curves showed strong concordance between predicted and observed recurrence probabilities.
CONCLUSION
The model effectively predicts early postoperative recurrence in stage II/III CRC, integrating clinical, inflammatory, and symptomatic factors.
Core Tip: Colorectal cancer recurrence within a year post-surgery poses significant challenges for stage II/III patients. However, few models predict this early recurrence. This study developed a nomogram to predict recurrence of colorectal stage II/III cancers one year after surgery. N2 stage, appetite loss, tumor size, and neutrophil-to-lymphocyte ratio were identified as independent risk factors. The model shows high accuracy, offering a practical tool for clinical management.
Citation: Xiong FC, Luo MP, Ruan SM. Construction of a risk prediction model for early postoperative recurrence in stage II/III colorectal cancer. World J Gastrointest Oncol 2025; 17(9): 107968
Colorectal cancer (CRC) ranks among the top three in terms of incidence and mortality worldwide[1]. More than 20% of CRC patients experience metastasis and recurrence within five years following surgery[2]. A nationwide cohort study from Denmark showed significant peaks of postoperative recurrence at the first and third years, with the recurrence risk increasing with more advanced staging[3], highlighting the importance of postoperative surveillance. Early recurrence after CRC surgery is generally believed to be associated with post-recurrence survival rates[4]. Early identification of high-risk patients is critical to improving the prognosis of their survival.
Current prognostic models for CRC primarily incorporate histopathological characteristics, including tumor size, location, tumor-node-metastasis staging, and lymph node metastasis[5,6]. These parameters facilitate histopathological grading of tumor biological behavior and assessment of local invasion, thereby providing a basis for estimating recurrence probability. Some models integrate laboratory biomarkers such as serum tumor-associated antigens[7], complete blood count derivatives[8], and systemic inflammation indices [e.g., neutrophil-to-lymphocyte ratio (NLR); platelet-to-lymphocyte ratio (PLR)][9]. These hematological profiles reflect host immune-inflammatory responses that demonstrate prognostic correlations with disease relapse. There are also a few models that attempt to incorporate genetic testing indicators to explore potential factors for tumor recurrence at the transcriptomic or epigenetic level[10].
Although existing models have made efforts to predict the prognosis of CRC, research on stage II/III CRC patients is still in the exploratory stage[11]. At present, there is a lack of validated models to assess the risk of early recurrence one year after surgery, and this clinical gap urgently needs to be addressed. Establishing reliable predictive models helps doctors develop personalized monitoring and treatment plans, avoiding the pain and economic burden caused by overtreatment[12]. However, traditional single-parameter assessment frameworks make it difficult to capture the multidimensionality of recurrence risk[13], and models that rely solely on basic clinical features also exhibit limited discriminatory ability[14]. In addition, the application of biomarkers in existing models is often limited by technical complexity and high cost, which hinders their clinical translation. This study was designed to address these issues by adopting a retrospective single-center design. The study reduced biases among the medical institutions with strict screening of cases and homogenized management plans after surgery. This design can systematically integrate pathological features, inflammatory indicators (such as NLR/PLR), and clinical symptoms (such as appetite loss), and preliminarily construct an efficient and clinically applicable multidimensional prediction model. Despite the inherent limitations of a single sample source and retrospective data, this study provides a foundational framework for subsequent multicenter validation, aiming to provide new tools for the postoperative management of stage II/III CRC patients.
MATERIALS AND METHODS
Study design and participants
The study is a retrospective cohort conducted at a single center to collect clinical data. It aimed to predict the incidence of metastasis and recurrence within one year following curative surgery for patients with stage II/III CRC. Participants were recruited from Zhejiang Provincial Hospital of Chinese Medicine. Data collection took place from January 1, 2019 to December 31, 2023. The Ethics Committee at our hospital has approved this study with the approval number of 2025-KLS-114-01. The Declaration of Helsinki was followed in all procedures.
Inclusion criteria: (1) Pathologically diagnosed as stage II/III CRC; (2) Postoperative chemotherapy or other cancer treatments according to the National Comprehensive Cancer Network guidelines; and (3) Availability of complete clinical data records within a year post-surgery.
Exclusion criteria: (1) Patients who experienced recurrence or metastasis within one month after surgery; (2) Multifocal tumor; (3) Incomplete data; (4) Presence of other malignancies; (5) Patients who have received neoadjuvant therapy before surgery; and (6) Severe cardiovascular or cerebrovascular diseases, severe hepatic or renal dysfunction, or other conditions that significantly affect survival. The patient enrollment flowchart is presented in Figure 1.
Figure 1 Flowchart of patient selection.
CRC: Colorectal cancer.
Clinical data
The clinical data collected for all patients included: Age, sex, history of hypertension and diabetes, history of smoking and alcohol abuse, family history of CRC, treatment details (year of surgery, chemotherapy, radiation, Chinese medicine), and pathological features (tumor size, location, and stage). Laboratory test indicators included White blood cell count, red blood cell count, hemoglobin level, platelet (PLT) count, NLR, PLR, and carcinoembryonic antigen (CEA) levels. The presence of fatigue and appetite loss was also recorded. The collection time of hematological indicators is before the first adjuvant therapy after surgery (4-6 weeks after surgery), while standardized follow-up plans are used for imaging re-examination. Baseline assessment is conducted 3 months after surgery, and subsequent enhanced computed tomography/magnetic resonance imaging re-examinations are conducted every 3-6 months. The occurrence of local or distant recurrence was followed during the one-year period after surgery.
Statistical analysis
This study used traditional random sampling to divide the total dataset into a training set (n = 244) and a testing set (n = 105) in a 7:3 ratio. The χ2 test and t-test were used to verify the balance of clinical features between the two groups. For normally distributed continuous variables, data are presented as mean ± SD, and one-way analysis of variance is used for inter-group comparison. For non-normally distributed continuous variables, data are represented by median and interquartile range and compared using the Kruskal-Wallis H test. The categorical data is expressed in frequency and percentage, and inter-group comparisons are performed using Pearson’s χ2 test or Fisher’s exact test. The specific steps are as follows: Firstly, significant variables (P < 0.05) are screened through univariate logistic regression, and then the significant variables are included in multivariate logistic regression. Stepwise regression (forward selection, Akaike information criterion) is used to determine the final model that includes N2 staging, decreased appetite, tumor size, and NLR; Construct a column chart based on the weighted contribution of each variable using regression coefficients, and convert it into a visual scoring tool for assessing the risk of local recurrence or metastasis within one year. After completing the model fitting on the training set, the performance was directly evaluated on an independent test set without cross-validation or parameter tuning. The predictive performance of the model was evaluated by the area under the curve (AUC) 95% confidence interval (CI), and a P < 0.05 was considered statistically significant. All analyses were conducted using R 4.2.2 software, mainly relying on the “rms” and “pROC” packages.
RESULTS
Clinical characteristics of the included patients
This study followed up 349 patients with CRC who met inclusion and exclusion criteria for one year after surgery. The results showed that 38 patients (10.9%) experienced local recurrence or metastasis within one year (Table 1). χ2 test results (Table 1) indicated that tumor size, lymph node metastasis, NLR, PLR, and the presence of fatigue and appetite loss were significantly associated with postoperative recurrence or metastasis. The recurrence group had significantly larger tumors, higher NLR and PLR (P < 0.05). Additionally, patients reporting fatigue and appetite loss had higher recurrence rates (P = 0.004 and < 0.001, respectively). The use of Chinese Medicine, the age of the patient, the sex of the patient, the radiation treatment, or the diagnosis year were all not associated with metastasis. These findings suggest that tumor size, lymph node metastasis, NLR, PLR, fatigue, and appetite loss are significant clinical indicators for recurrence or metastasis within one year after surgery for CRC.
Table 1 The clinical characteristics of the included patients, n (%).
Univariate and multivariate logistic regression analysis for CRC patients in the training set
By random sampling, the total population dataset was divided into a training set (n = 244) and a testing set (n = 105). The clinical characteristics of the two cohorts were not significantly different (P > 0.05, Supplementary Table 1). Univariate logistic regression analysis (Table 2) revealed that the tumor size, American Joint Committee on Cancer N2 stage, NLR, and appetite loss were significantly associated with postoperative recurrence in CRC. Compared with N0 stage, the odds ratio (OR) for N1 stage was 2.63 (95%CI: 0.77-9.05, P = 0.124), and the OR for N2 stage was 12.10 (95%CI: 3.67-39.92, P < 0.001). The recurrence rate was 7.14 times greater in patients with appetite loss than without it (95%CI: 2.84-17.97, P < 0.001). For every unit increase in NLR, the recurrence risk increased by 3.64 times (95%CI: 2.24-5.91, P < 0.001). PLR was not significantly associated with postoperative recurrence (P = 0.059). Multivariate logistic regression analysis (Table 2) showed that after adjusting for other factors, American Joint Committee on Cancer N2 stage, appetite loss, tumor size and NLR remained independent risk factors for postoperative recurrence of CRC.
Table 2 Univariate and multivariate logistic regression analysis for colorectal cancer patients in the training set.
Based on the above analysis, a nomogram model was constructed to predict the risk of recurrence or metastasis within one year after CRC surgery, incorporating four independent risk factors: N stage, presence of appetite loss, tumor size, and NLR (Figure 2). The AUCs of the nomogram for training and testing sets were 0.98 (95%CI: 0.97-1.00) and 0.91 (95%CI: 0.84-0.97) respectively (Figure 3A and B). According to the calibration curves, the probability predicted by the model is basically consistent with the actual probability (Figure 4A and B). We plotted the DCA curves to evaluate the clinical practicality of this predictive model. The results showed that the nomogram model demonstrated good clinical application value within a relatively wide range of recurrence risks in both cohorts, indicating that it has good clinical utility (Figure 5A and B).
Figure 2 Predictive nomogram for early postoperative recurrence in stage II/III colorectal cancer.
ACJJN2: American Joint Committee on Cancer N2 stage; NLR: Neutrophil-to-lymphocyte ratio.
Figure 3 The receiver operating characteristic curves for the predictive nomogram.
A: Training set; B: Testing set. AUC: Area under the curve; CI: Confidence interval.
Figure 5 Decision curve analysis curves for the predictive nomogram.
A: Training set; B: Testing set.
DISCUSSION
This study innovatively integrated parameters from three dimensions: Tumor invasiveness (N2 staging, tumor size), host immune inflammatory response (NLR), and clinical symptomatology (decreased appetite), and constructed the first multidimensional prediction model for early postoperative recurrence of stage II/III CRC, breaking through the limitations of traditional tumor-node-metastasis staging systems that rely solely on anatomical features. By revealing the pathological synergistic effect between the pro-inflammatory microenvironment reflected by abnormal elevation of NLR and the systemic nutritional metabolic imbalance represented by decreased appetite, this model elucidates the driving mechanism of early recurrence from a new perspective of the interaction between tumor biology and host response, providing targeted evidence for precise intervention. Compared to complex models that rely on a single biomarker or genetic testing, the column chart tool developed in this study only requires routine clinical indicators to achieve an AUC value of 0.91. Its clinical accessibility and cost-effectiveness advantages are significant, especially suitable for rapid identification and hierarchical management of high-risk patients in primary healthcare institutions[15,16]. Mizuuchi et al[17] corroborated that N2 stage independently predicts postoperative recurrence in stage II/III CRC (P < 0.001), aligning with our results. They emphasized the necessity of intensifying adjuvant chemotherapy regimens (e.g., oxaliplatin-containing protocols) for such patients to mitigate recurrence risks.
Enhanced adjuvant therapy mainly refers to dual-drug combination therapy containing oxaliplatin. According to the National Comprehensive Cancer Network guidelines and IDEA research consensus, for N2 or stage III CRC patients with high-risk factors such as vascular invasion, 5-fluorouracil (5-FU)/calcium folinate + oxaliplatin or capecitabine + oxaliplatin regimens are still the most evidence-based intensive adjuvant therapy, and a 6-month course is recommended. Compared with the 3-month short-term treatment that can be considered for low-risk patients, extending the treatment time can significantly reduce the risk of recurrence. Although other schemes, such as 5-FU/calcium folinate + irinotecan + oxaliplatin, have shown advantages in tumors such as pancreatic cancer, they have not yet been proven to be superior to 5-FU/calcium folinate + oxaliplatin/capecitabine + oxaliplatin in adjuvant treatment of CRC. For patients with microsatellite stability and rat sarcoma viral oncogene homolog/rapidly accelerated fibrosarcoma wild-type molecular features, combination therapy with epidermal growth factor receptor monoclonal antibodies may be considered, but strict evaluation of tolerance is required. For mismatch repair-deficient or high microsatellite instability patients, programmed death 1 inhibitors (such as pembrolizumab) have shown significant efficacy in neoadjuvant therapy, but their value in the field of adjuvant therapy still needs further validation in phase III clinical trials.
Higher metastatic lymph node ratio correlates with inferior disease-free survival and overall survival[18], potentially attributable to occult nodal micro-metastases[19]. N2 staging not only signifies anatomical progression but also reflects enhanced tumor biological aggressiveness. Patients with N2 stage or concurrent high-risk features (e.g., low immunoscore[20], vascular invasion[21]) warrant enhanced adjuvant therapy and vigilant surveillance.
Tumor size has also been established as a critical determinant of CRC prognosis[22-24]. A retrospective analysis of 221 rectal adenocarcinoma patients demonstrated that tumors ≥ 5 cm exhibited a 23.0% 5-year local recurrence rate, compared to only 1.4% in the < 5 cm group (hazard ratio = 0.14, P = 0.013)[25]. There is a significant increase in distant metastasis with larger tumors: 25.3% with > 100 mm tumors, compared to 1.1% with 1-10 mm tumors[26]. Mechanistically, tumor size progression may be modulated by PLT-derived growth factor receptor α expression levels in CRC[27], thus exhibiting more aggressive biological characteristics. Our research findings are consistent with previous literature, supporting tumor size as an independent predictor of recurrence. Although different studies have differences in tumor type or threshold settings, the overall trend is consistent, indicating that the larger the tumor volume, the higher the risk of recurrence. Our findings further validate this association and reinforce the clinical significance of tumor size in prognostic assessment.
NLR serves as a biomarker reflecting the interaction between cancer cells and immune system components, with elevated NLR values indicating systemic inflammatory responses and disrupted anti-tumor immune homeostasis[28]. High NLR is frequently accompanied by reduced absolute lymphocyte counts, which impair immune surveillance mechanisms and promote residual tumor cell evasion[29]. In CRC, patients with NLR > 3 exhibit significantly increased 2-year postoperative mortality[30], while those with lower NLR demonstrate superior 5-year overall survival[31]. In our study, we did not set a fixed threshold, but treated NLR as a continuous variable. Multivariate logistic regression showed that for every 1 unit increase in NLR, the risk of recurrence increased by 3.8 times (OR = 3.80, 95%CI: 1.77-8.14). By quantifying its continuous effect through a column chart model (Figure 1), it was found that when NLR ≥ 4, the contribution to the total score significantly increased, indicating a dose-response relationship between this indicator and recurrence risk. NLR elevation is associated with a higher risk of immuno-related adverse reactions during the use of immune checkpoint inhibitors, often resulting to a compromised tolerance for treatment[32]. Li et al[33] emphasized the clinical value of NLR in early CRC detection and monitoring, advocating for prognostic models that integrate NLR with other clinical parameters to evaluate systemic inflammatory status and predict outcomes.
The selection of NLR and PLR is based on their biological mechanisms reflecting tumor-associated inflammation and immune evasion. Neutrophils promote tumor angiogenesis and extracellular matrix remodeling by releasing vascular endothelial growth factor, matrix metalloproteinases, and reactive oxygen species, while secreting arginase-1 to inhibit T cell function, forming an immunosuppressive microenvironment. The reduction of lymphocytes, especially cytotoxic T cells and natural killer cells, weakens the immune surveillance function, allowing residual tumor cells to survive[34].
The prognostic value of PLR is related to the PLT-mediated metastasis process. Activated PLTs mediate the adhesion of tumor cells to vascular endothelium through P-selectin, and promote epithelial mesenchymal transition by releasing growth factors such as transforming growth factor β and platelet-derived growth factor. The synergistic effect of promoting inflammation and metastasis makes NLR and PLR important indicators reflecting the biological invasiveness of tumors[35]. Therefore, incorporating NLR and PLR into the prediction model not only conforms to the principles of tumor immunobiology, but also has evidence-based medicine for multi-center research.
This study further investigated the prognostic significance of fatigue and appetite loss as clinical symptoms in CRC recurrence. While both fatigue and appetite loss demonstrated significant associations with early postoperative recurrence, only appetite loss emerged as an independent risk factor through multivariate analysis, and subsequently incorporated into our predictive model. Current prognostic frameworks predominantly focus on clinicopathological characteristics[36,37] and molecular biomarkers[38,39], yet clinically practical tools integrating biological features with symptomatic manifestations remain scarce. Emerging evidence highlights the critical role of host-tumor interactions in disease progression. Appetite loss, beyond being a quality-of-life indicator, exhibits correlations with survival outcomes[40]. Patients with appetite loss frequently present with hypoalbuminemia and elevated C-reactive protein levels[41], biochemical alterations that perpetuate systemic inflammation and create a tumor-permissive microenvironment through nutritional depletion[42]. Appetite loss has shown potential to reverse cancer-associated cachexia and improve survival rates[43]. Our simplified clinical screening tool innovatively combines pathological characteristics (lymph node staging, tumor size), inflammatory indices (NLR), and symptomatic manifestations (appetite loss) to identify high-risk stage II/III CRC patients susceptible to 1-year postoperative recurrence. Compared to existing biomarker-dependent models requiring specialized assays, this instrument demonstrates superior clinical applicability across diverse healthcare settings, enabling rapid risk stratification without advanced laboratory support.
The four independent risk factors identified in this study demonstrated strong concordance with the nomogram model, indicating that integrating clinical parameters with inflammatory indices enhances recurrence prediction accuracy. Compared with genomic profiling and other high-cost methodologies, our model prioritizes clinically accessible indicators, ensuring practical implementation across diverse healthcare settings. However, several limitations warrant consideration: First, as a single-center retrospective study derived exclusively from Zhejiang Provincial Hospital of Chinese Medicine, the geographical homogeneity in patient demographics and treatment protocols may limit generalizability. Future multi-center prospective studies are recommended to validate these findings. Second, while variable selection incorporated pathological features, laboratory indices, and symptomatic manifestations, potential confounding factors such as treatment adherence variations and socioeconomic determinants were not systematically evaluated, possibly affecting model precision. Third, the calibration curve in the validation cohort deviated slightly from the ideal diagonal, suggesting the need for external validation using larger real-world datasets to optimize predictive performance. Fourth, although our study identified decreased appetite as a strong independent risk factor for early recurrence (OR = 46.52), the inherent limitations of retrospective symptom assessment and the subjective nature of symptoms may introduce bias, as neither standardized scales nor objective nutritional markers (such as albumin, body mass index) were systematically collected. Similarly, the lack of significance in fatigue in multivariate analysis may reflect inconsistent evaluation criteria. In the future, we will incorporate prospective studies and objective measurements to elucidate the true prognostic contribution of cancer-related symptoms. Last but not least, although this model achieves risk stratification through nomogram scoring, there is currently no clinically validated threshold standard for stratification, and there is a lack of specific management recommendations for patients at different risk levels (such as monitoring frequency adjustment, adjuvant therapy enhancement plans, etc.), which may temporarily limit the direct clinical translation of the model. To promote clinical application, future research needs to establish multidisciplinary expert consensus through Delphi method, determine the optimal stratification threshold based on cost-benefit analysis, and conduct intervention trials to validate the stratification management strategy.
CONCLUSION
This retrospective study of 349 stage II/III CRC patients established and validated a predictive model for early postoperative recurrence. Lymph node stage N2, tumor size, NLR, and appetite loss were identified as independent risk factors for recurrence. Both training and validation data showed good performance. The model stratified patients at high risk of recurrence and offered a tool that could be used for clinical surveillance.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
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