Dataset Information

CT radiomics identifying non-responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer.

ABSTRACT:

Background and purpose

Early detection of non-response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT.

Materials and methods

Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non-response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application.

Results

This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non-responders and 101 responders) and 64 patients in the validation cohort (21 non-responders and 43 responders). For predicting non-response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance.

Conclusion

Pretreatment CT radiomics achieved satisfying performance in predicting non-response to nCRT and could be helpful to assist in treatment planning for patients with LARC.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC9939108 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

CT radiomics identifying non-responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer.

Zhang Zinan Z Yi Xiaoping X Pei Qian Q Fu Yan Y Li Bin B Liu Haipeng H Han Zaide Z Chen Changyong C Pang Peipei P Lin Huashan H Gong Guanghui G Yin Hongling H Zai Hongyan H Chen Bihong T BT

Cancer medicine 20220801 3

<h4>Background and purpose</h4>Early detection of non-response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT.<h4>Materials and methods</h4>Patients with LARC who were treated with nCRT were retrospectively enrolle ...[more]

PMID: 35912919

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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.

Dataset Information

CT radiomics identifying non-responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer.

Background and purpose

Materials and methods

Results

Conclusion

Publications

CT radiomics identifying non-responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer.

Similar Datasets

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