Dataset Information

Investigating anorectal function using postoperative MRI-based fibrosis score in patients with locally advanced rectal cancer receiving neoadjuvant chemoradiotherapy: a two-center study.

ABSTRACT:

Purpose

This study aimed to develop a postoperative MRI-based fibrosis scoring system and to assess its correlation with anorectal function in locally advanced rectal cancer (LARC) cases administered neoadjuvant chemoradiotherapy (nCRT).

Methods

Pathologically confirmed LARC cases administered nCRT and radical resection were assessed retrospectively. Based on postoperative magnetic resonance imaging (MRI) findings, anastomotic fibrosis score (AFS) and perirectal fibrosis score (PFS) were determined to evaluate the extent of fibrosis. The Wexner continence score for anorectal function was obtained 2 years postoperatively and assessed for correlation with MRI fibrosis scores. The cases were divided into 2 groups by the median Wexner score. Univariable and multivariable analyses were adopted for building a nomogram model, whose diagnostic performance was estimated by receiver operating characteristic (ROC) and decision curve analyses (DCA).

Results

Finally, 144 patients with LARC were included in cohort 1 (training set). 52 patients were enrolled in cohort 2 (external validation set). Spearman correlation analysis indicated that AFS and PFS were positively correlated with the Wexner score. Univariable and multivariable analyses revealed age, tumor height, AFS, and PFS were independent predictors of anorectal function. The nomogram model achieved a good diagnostic performance, with AUCs of 0.800 and 0.827 in the training and validation sets, respectively; its predicting value was also confirmed by DCA.

Conclusion

The present study showed AFS and PFS derived from postoperative MRI are positively correlated with Wexner score. In addition, the new scoring system was effective in predicting anorectal function in LARC cases administered nCRT.

SUBMITTER: Yuan Y

PROVIDER: S-EPMC10572048 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Investigating anorectal function using postoperative MRI-based fibrosis score in patients with locally advanced rectal cancer receiving neoadjuvant chemoradiotherapy: a two-center study.

Yuan Yuan Y Yu Yue Y Sun Yi Qun YQ Li Shuai S Lu Hai Di HD Ma Xiao Lu XL Lu Jian Ping JP Shao Cheng Wei CW Zhang Wei W Tong Tong T Gao Xian Hua XH Shen Fu F

Annals of medicine 20231012 2

<h4>Purpose</h4>This study aimed to develop a postoperative MRI-based fibrosis scoring system and to assess its correlation with anorectal function in locally advanced rectal cancer (LARC) cases administered neoadjuvant chemoradiotherapy (nCRT).<h4>Methods</h4>Pathologically confirmed LARC cases administered nCRT and radical resection were assessed retrospectively. Based on postoperative magnetic resonance imaging (MRI) findings, anastomotic fibrosis score (AFS) and perirectal fibrosis score (PF ...[more]

PMID: 37824443

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Project description:Highlights • Pre-treatment MRI images were used to construct a prediction model for pathologic T downstaging in locally advanced rectal cancer.• The prediction model showed good performance in training cohort (AUC 0.842), internal testing cohort (AUC 0.809) and prospective cohort (AUC 0.727).• High-probability group (score > 81.82) had potential benefits from sufficient consolidation chemotherapy. Background and purpose Predicting tumour response would be useful for selecting patients with locally advanced rectal cancer (LARC) for organ preservation strategies. We aimed to develop and validate a prediction model for T downstaging (ypT0-2) in LARC patients after neoadjuvant chemoradiotherapy and to identify those who may benefit from consolidation chemotherapy. Materials and methods cT3-4 LARC patients at three tertiary medical centers from January 2012 to January 2019 were retrospectively included, while a prospective cohort was recruited from June 2021 to March 2022. Eight filter (principal component analysis, least absolute shrinkage and selection operator, partial least-squares discriminant analysis, random forest)-classifier (support vector machine, logistic regression) models were established to select radiomic features. A nomogram combining radiomics and significant clinical features was developed and validated by calibration curve and decision curve analysis. Interaction test was conducted to investigate the consolidation chemotherapy benefits. Results A total of 634 patients were included (426 in training cohort, 174 in testing cohort and 34 in prospective cohort). A radiomic prediction model using partial least-squares discriminant analysis and a support vector machine showed the best performance (AUC: 0.832 [training]; 0.763 [testing]). A nomogram combining radiomics and clinical features showed significantly better prognostic performance (AUC: 0.842 [training]; 0.809 [testing]) than the radiomic model. The model was also tested in the prospective cohort with AUC 0.727. High-probability group (score > 81.82) may have potential benefits from ≥ 4 cycles consolidation chemotherapy (OR: 4.173, 95 % CI: 0.953–18.276, p = 0.058, pinteraction = 0.021). Conclusion We identified and validated a model based on multicenter pre-treatment radiomics to predict ypT0-2 in cT3-4 LARC patients, which may facilitate individualised treatment decision-making for organ-preservation strategies and consolidation chemotherapy.

Project description:BackgroundThe skeletal muscle index (SMI) can serve as a surrogate for a patient's nutritional status, which is associated with treatment toxicity. This study aims to investigate the potential of baseline skeletal muscle radiomics features to predict gastrointestinal toxicity of neoadjuvant chemoradiotherapy for rectal cancer.MethodsA total of 214 rectal cancer patients (115, 49 and 50 in the training, internal and external validation set, respectively) who underwent neoadjuvant pelvic radiotherapy with capecitabine and irinotecan were retrospectively identified. The skeletal muscle at the level of the third lumber vertebra was contoured, and the radiomics features were extracted from computed tomography scans. In the training set, the least absolute shrinkage and selection operator (LASSO) regression algorithm was applied to select features that were most significantly associated with grade 3-4 gastrointestinal toxicity (diarrhea, nausea, vomiting and proctitis). The predictive performance and clinical utility were estimated using the area under the receiver operator characteristic curve (AUC), F1-score and decision curve analysis (DCA).ResultsNine features, including the SMI and eight radiomics features, were associated with grade 3-4 gastrointestinal toxicity and included in the logistic regression. This combined predictive model, which incorporated the SMI and radiomics features, showed better discrimination than the SMI alone, with an AUC of 0.856 (95 % CI: 0.782-0.929) in the training cohort, 0.812 (95 % CI: 0.667-0.956) in the internal validation cohort and 0.745 (95 % CI: 0.600-0.890) in the external validation cohort. DCA further verified the clinical utility of the combined predictive model.ConclusionRadiomics features of skeletal muscle were significantly associated with gastrointestinal toxicity. The predictive model incorporating the SMI and radiomics features exhibits favorable discrimination and may be highly informative for clinical decision-makings.

Project description:ObjectivesTo construct and validate multiparametric MR-based radiomic models based on primary tumors for predicting lymph node metastasis (LNM) following neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) patients.MethodsA total of 150 LARC patients from two independent centers were enrolled. The training cohort comprised 100 patients from center A. Fifty patients from center B were included in the external validation cohort. Radiomic features were extracted from the manually segmented volume of interests of the primary tumor before and after nCRT. Feature selection was performed using multivariate logistic regression analysis. The clinical risk factors were selected via the least absolute shrinkage and selection operator method. The radiologist's assessment of LNM was performed. Eight models were constructed using random forest classifiers, including four single-sequence models, three combined-sequence models, and a clinical model. The models' discriminative performance was assessed via receiver operating characteristic curve analysis quantified by the area under the curve (AUC).ResultsThe AUCs of the radiologist's assessment, the clinical model, and the single-sequence models ranged from 0.556 to 0.756 in the external validation cohort. Among the single-sequence models, modelpost_DWI exhibited superior predictive power, with an AUC of 0.756 in the external validation set. In combined-sequence models, modelpre_T2_DWI_post had the best diagnostic performance in predicting LNM after nCRT, with a significantly higher AUC (0.831) than those of the clinical model, modelpre_T2_DWI, and the single-sequence models (all p < 0.05).ConclusionsA multiparametric model that incorporates MR radiomic features before and after nCRT is optimal for predicting LNM after nCRT in LARC.Critical relevance statementThis study enrolled 150 LARC patients from two independent centers and constructed multiparametric MR-based radiomic models based on primary tumors for predicting LNM following nCRT, which aims to guide therapeutic decisions and predict prognosis for LARC patients.Key pointsThe biological characteristics of primary tumors and metastatic LNs are similar in rectal cancer. Radiomics features and clinical data before and after nCRT provide complementary tumor information. Preoperative prediction of LN status after nCRT contributes to clinical decision-making.

Dataset Information

Investigating anorectal function using postoperative MRI-based fibrosis score in patients with locally advanced rectal cancer receiving neoadjuvant chemoradiotherapy: a two-center study.

Purpose

Methods

Results

Conclusion

Publications

Investigating anorectal function using postoperative MRI-based fibrosis score in patients with locally advanced rectal cancer receiving neoadjuvant chemoradiotherapy: a two-center study.

Similar Datasets

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