Dataset Information

Nomogram to predict disease recurrence in patients with locally advanced rectal cancer undergoing rectal surgery after neoadjuvant therapy: retrospective cohort study.

ABSTRACT:

Introduction

Prognostic models can be used for predicting survival outcomes and guiding patient management. TNM staging alone is insufficient for predicting recurrence after chemoradiotherapy (CRT) and surgery for locally advanced rectal cancer. This study aimed to develop a nomogram to better predict cancer recurrence after CRT followed by total mesorectal excision (TME) and tailor postoperative management and follow-up.

Materials and methods

Between 2002 and 2019, data were retrospectively collected on patients with rectal adenocarcinoma. Data on sex, age, carcinoembryonic antigen (CEA) level, tumour location, induction chemotherapy, adjuvant chemotherapy, tumour downsizing, perineural invasion, lymphovascular invasion, pathological stage, resection margins (R0 versus R1), and pelvic septic complications were analysed. The variables significantly associated with cancer recurrence were used to build a nomogram that was validated in both the training and validation cohorts. Model performance was evaluated by receiver operating characteristic curve and area under the curve (AUC) analyses.

Results

After applying exclusion criteria, 634 patients with rectal adenocarcinoma were included in this study. Eight factors (CEA level, adjuvant chemotherapy, tumour downsizing, perineural invasion, lymphovascular invasion, pathological stage, resection margins (R0 versus R1), and pelvic septic complications) were identified as nomogram variables. Our nomogram showed good performance with an AUC of 0.74 and 0.75 in the training and validation cohorts respectively.

Conclusion

Our nomogram is a simple tool for predicting cancer recurrence in patients with locally advanced rectal cancer after neoadjuvant CRT followed by TME. It provides an individual risk prediction of recurrence to tailor surveillance.

SUBMITTER: Saadoun JE

PROVIDER: S-EPMC9749481 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

Nomogram to predict disease recurrence in patients with locally advanced rectal cancer undergoing rectal surgery after neoadjuvant therapy: retrospective cohort study.

Saadoun Jacques-Emmanuel JE Meillat Hélène H Zemmour Christophe C Brunelle Serge S Lapeyre Alexandra A de Chaisemartin Cécile C Lelong Bernard B

BJS open 20221101 6

<h4>Introduction</h4>Prognostic models can be used for predicting survival outcomes and guiding patient management. TNM staging alone is insufficient for predicting recurrence after chemoradiotherapy (CRT) and surgery for locally advanced rectal cancer. This study aimed to develop a nomogram to better predict cancer recurrence after CRT followed by total mesorectal excision (TME) and tailor postoperative management and follow-up.<h4>Materials and methods</h4>Between 2002 and 2019, data were retr ...[more]

PMID: 36515671

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Project description:ObjectiveTo interrogate the mesorectal fat using MRI radiomics feature analysis in order to predict clinical outcomes in patients with locally advanced rectal cancer.MethodsThis retrospective study included patients who underwent neoadjuvant chemoradiotherapy for locally advanced rectal cancer from 2009 to 2015. Three radiologists independently segmented mesorectal fat on baseline T2-weighted axial MRI. Radiomics features were extracted from segmented volumes and calculated using CERR software, with adaptive synthetic sampling being employed to combat large class imbalances. Outcome variables included pathologic complete response (pCR), local recurrence, distant recurrence, clinical T-category (cT), post-treatment T category (ypT), and post-treatment N category (ypN). A maximum of eight most important features were selected for model development using support vector machines and fivefold cross-validation to predict each outcome parameter via elastic net regularization. Diagnostic metrics of the final models were calculated, including sensitivity, specificity, PPV, NPV, accuracy, and AUC.ResultsThe study included 236 patients (54 ± 12 years, 135 men). The AUC, sensitivity, specificity, PPV, NPV, and accuracy for each clinical outcome were as follows: for pCR, 0.89, 78.0%, 85.1%, 52.5%, 94.9%, 83.9%; for local recurrence, 0.79, 68.3%, 80.7%, 46.7%, 91.2%, 78.3%; for distant recurrence, 0.87, 80.0%, 88.4%, 58.3%, 95.6%, 87.0%; for cT, 0.80, 85.8%, 56.5%, 89.1%, 49.1%, 80.1%; for ypN, 0.74, 65.0%, 80.1%, 52.7%, 87.0%, 76.3%; and for ypT, 0.86, 81.3%, 84.2%, 96.4%, 46.4%, 81.8%.ConclusionRadiomics features of mesorectal fat can predict pathological complete response and local and distant recurrence, as well as post-treatment T and N categories.Key points• Mesorectal fat contains important prognostic information in patients with locally advanced rectal cancer (LARC). • Radiomics features of mesorectal fat were significantly different between those who achieved complete vs incomplete pathologic response (accuracy 83.9%, 95% CI: 78.6-88.4%). • Radiomics features of mesorectal fat were significantly different between those who did vs did not develop local or distant recurrence (accuracy 78.3%, 95% CI: 72.0-83.7% and 87.0%, 95% CI: 81.6-91.2% respectively).

Dataset Information

Nomogram to predict disease recurrence in patients with locally advanced rectal cancer undergoing rectal surgery after neoadjuvant therapy: retrospective cohort study.

Introduction

Materials and methods

Results

Conclusion

Publications

Nomogram to predict disease recurrence in patients with locally advanced rectal cancer undergoing rectal surgery after neoadjuvant therapy: retrospective cohort study.

Similar Datasets

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