Dataset Information

An MRI-based scoring system for pretreatment risk stratification in locally advanced rectal cancer.

ABSTRACT:

Background

Accurately assessing the risk of recurrence in patients with locally advanced rectal cancer (LARC) before treatment is important for the development of treatment strategies. The purpose of this study is to develop an MRI-based scoring system to predict the risk of recurrence in patients with LARC.

Methods

This was a multicenter observational study that enrolled participants who underwent neoadjuvant chemoradiotherapy. To evaluate the risk of recurrence in these patients, we developed the mrDEC scoring system and assessed inter-reader agreement. Additionally, we plotted Kaplan-Meier curves to compare the 3-year disease-free survival (DFS) and 5-year overall survival (OS) rates among patients with different mrDEC scores.

Results

A total of 1287 patients with LARC were included in this study. We observed substantial inter-reader agreement for mrDEC. Based on the mrDEC scores ranging from 0 to 3, the patients were categorized into four groups. The 3-year DFS rates for the groups were 91.0%, 79.5%, 65.5%, and 44.0% (P < 0.0001), respectively, and the 5-year OS rates were 92.9%, 87.1%, 74.8%, and 44.5%, respectively (P < 0.0001).

Conclusions

The mrDEC scoring system proved to be an effective tool for predicting the prognosis of patients with LARC and can assist clinicians in clinical decision-making.

SUBMITTER: Zhao M

PROVIDER: S-EPMC10539304 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

An MRI-based scoring system for pretreatment risk stratification in locally advanced rectal cancer.

Zhao Minning M Feng Lili L Zhao Ke K Cui Yanfen Y Li Zhenhui Z Ke Chenglu C Yang Xinyue X Qiu Qing Q Lu Weirong W Liang Yanting Y Xie ChuanMiao C Wan Xiangbo X Liu Zaiyi Z

British journal of cancer 20230809 7

<h4>Background</h4>Accurately assessing the risk of recurrence in patients with locally advanced rectal cancer (LARC) before treatment is important for the development of treatment strategies. The purpose of this study is to develop an MRI-based scoring system to predict the risk of recurrence in patients with LARC.<h4>Methods</h4>This was a multicenter observational study that enrolled participants who underwent neoadjuvant chemoradiotherapy. To evaluate the risk of recurrence in these patients ...[more]

PMID: 37558922

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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:BackgroundTo evaluate the value of pretreatment inflammatory-nutritional biomarkers in predicting responses to neoadjuvant chemoradiotherapy (nCRT) and survival in patients with locally advanced rectal cancer (LARC).MethodsPatients with LARC who underwent nCRT and subsequent surgery between October 2012 and December 2019 were considered for inclusion. Neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), and prognostic nutritional index (PNI) were calculated from according to routine laboratory data within 1 week prior to nCRT. The correlations between baseline inflammatory-nutritional biomarkers and responses were analyzed using Chi-square test or Fisher's exact test, and multivariate logistic regression analysis was performed to identify the independent predictors of pathological responses to nCRT. Univariate and multivariate Cox proportional hazard models were used to assess the correlations of predictors with disease-free survival (DFS) and overall survival (OS).ResultsA total of 273 patients with LARC were enrolled in this study. Higher LMR and PNI were observed in the good-response group, meanwhile higher NLR and PLR were observed in the poor-response group. Multivariate logistic regression analysis results revealed that PLR and PNI independently predicted responses to nCRT. Multivariable Cox regression analysis determined that PNI was an independent predictor of DFS and OS in patients with LARC. The value of pretreatment PNI in predicting responses and survival was continuously superior to those of NLR, PLR, and LMR. The optimal cutoff value of the PNI was approximate 45. Subgroup analyses indicated that the pathological responses and survival in the high PNI group (≥ 45) were significantly better than those in the low PNI group (< 45), especially in patients with clinical stage III rectal cancer.ConclusionThe pretreatment PNI can serve as a promising predictor of response to nCRT and survival in patients with LACR, which is superior to NLR, PLR, and LMR, and the patients with clinical stage III rectal cancer who have a higher PNI are more likely to benefit from nCRT.

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

An MRI-based scoring system for pretreatment risk stratification in locally advanced rectal cancer.

Background

Methods

Results

Conclusions

Publications

An MRI-based scoring system for pretreatment risk stratification in locally advanced rectal cancer.

Similar Datasets

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