Multiparametric MRI-based radiomics signature for preoperative estimation of tumor-stroma ratio in rectal cancer.

OBJECTIVE: To determine whether a radiomics signature (rad-score) outperforms ADC in TSR estimation by developing a radiomics biomarker for preoperative TSR diagnosis in rectal cancer. METHODS: This study included 149 patients (119 and 30 in the training and validation cohorts, respectively). All patients underwent T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted imaging. A rad-score was generated using the least absolute shrinkage and selection operator (LASSO) algorithm and stepwise multivariate logistic regression. Meanwhile, the mean ADCs were calculated from ADC maps. For both the mean ADC and rad-score, binary logistic regression and Spearman correlation coefficients were used to determine associations with the TSR, and the area under the receiver operating characteristic (ROC) curve was used to assess the diagnostic performance. The reliability of the rad-score was quantified by comparing the imaging-estimated TSR with the actual TSR of each patient. RESULTS: Both the mean ADC and rad-score were positively correlated with the TSR in the training cohort (mean ADC: p < 0.001, r = 0.566; rad-score: p < 0.001, r = 0.559) and validation cohort (mean ADC: p < 0.001, r = 0.671; rad-score: p = 0.002, r = 0.536). The rad-score, with AUCs of 0.917 (95% CI 0.869-0.965) and 0.787 (95% CI 0.602-0.972) in the training and validation cohorts, respectively, outperformed the mean ADC (training cohort: AUC = 0.776, 95% CI 0.693-0.859; validation cohort: AUC = 0.764, 95% CI 0.592-0.936) in TSR estimation. CONCLUSION: The ADC possesses potential diagnostic value for TSR estimation in rectal cancer, and the rad-score shows increased diagnostic value over the ADC and may be a promising supplemental tool for patient stratification and informing decision-making. KEY POINTS: • Tumor-stroma ratio has been verified as an independent prognostic factor for various solid tumors including rectal cancer. • The ADC and multiparametric MRI-based radiomics features were significantly and positively correlated with the tumor-stroma ratio in rectal cancer. • The radiomics signature outperformed the ADC in discriminating TSR in rectal cancer.

Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer.

PURPOSE: To develop and validate a radiomics model based on high-resolution T2WI and a clinical-radiomics model for tumour-stroma ratio (TSR) evaluation with a gold standard of TSR evaluated by rectal specimens without therapeutic interference and further apply them in prognosis prediction of locally advanced rectal cancer (LARC) patients who received neoadjuvant chemoradiotherapy. METHODS: A total of 178 patients (mean age: 59.35, range 20-85 years; 65 women and 113 men) with rectal cancer who received surgery alone from January 2016 to October 2020 were enrolled and randomly separated at a ratio of 7:3 into training and validation sets. A senior radiologist reviewed after 2 readers manually delineated the whole tumour in consensus on preoperative high-resolution T2WI in the training set. A total of 1046 features were then extracted, and recursive feature elimination embedded with leave-one-out cross validation was applied to select features, with which an MR-TSR evaluation model was built containing 6 filtered features via a support vector machine classifier trained by comparing patients' pathological TSR. Stepwise logistic regression was employed to integrate clinical factors with the radiomics model (Fusion-TSR) in the training set. Later, the MR-TSR and Fusion-TSR models were replicated in the validation set for diagnostic effectiveness evaluation. Subsequently, 243 patients (mean age: 53.74, range 23-74 years; 63 women and 180 men) with LARC from October 2012 to September 2017 who were treated with NCRT prior to surgery and underwent standard pretreatment rectal MR examination were enrolled. The MR-TSR and Fusion-TSR were applied, and the Kaplan-Meier method and log-rank test were used to compare the survival of patients with different MR-TSR and Fusion-TSR. Cox proportional hazards regression was used to calculate the hazard ratio (HR). RESULTS: Both the MR-TSR and Fusion-TSR models were validated with favourable diagnostic power: the AUC of the MR-TSR was 0.77 (p = 0.01; accuracy = 69.8 %, sensitivity = 88.9 %, specificity = 65.9 %, PPV = 34.8 %, NPV = 96.7 %), while the AUC of the Fusion-TSR was 0.76 (p = 0.014; accuracy = 67.9 %, sensitivity = 88.9 %, specificity = 63.6 %, PPV = 33.3 %, NPV = 96.6 %), outperforming their effectiveness in the training set: the AUC of the MR-TSR was 0.65 (p = 0.035; accuracy = 66.4 %, sensitivity = 61.9 %, specificity = 67.3 %, PPV = 27.7 %, NPV = 90.0 %), while the AUC of the Fusion-TSR was 0.73 (p = 0.001; accuracy = 73.6 %, sensitivity = 71.4 %, specificity = 74.0 %, PPV = 35.73 %, NPV = 92.8 %). With further prognostic analysis, the MR-TSR was validated as a significant prognostic factor for DFS in LARC patients treated with NCRT (p = 0.020, HR = 1.662, 95 % CI = 1.077-2.565), while the Fusion-TSR was a significant prognostic factor for OS (p = 0.005, HR = 2.373, 95 % CI = 1.281-4.396). CONCLUSIONS: We developed and validated a radiomics TSR and a clinical-radiomics TSR model and successfully applied them to better risk stratification for LARC patients receiving NCRT and for better decision making.

Magnetic resonance imaging-based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study.

Our study investigated whether magnetic resonance imaging (MRI)-based radiomics features could predict good response (GR) to neoadjuvant chemoradiotherapy (nCRT) and clinical outcome in patients with locally advanced rectal cancer (LARC). Radiomics features were extracted from the T2 weighted (T2W) and Apparent diffusion coefficient (ADC) images of 1070 LARC patients retrospectively and prospectively recruited from three hospitals. To create radiomic models for GR prediction, three classifications were utilized. The radiomic model with the best performance was integrated with important clinical MRI features to create the combined model. Finally, two clinical MRI features and ten radiomic features were chosen for GR prediction. The combined model, constructed with the tumor size, MR-detected extramural venous invasion, and radiomic signature generated by Support Vector Machine (SVM), showed promising discrimination of GR, with area under the curves of 0.799 (95% CI, 0.760-0.838), 0.797 (95% CI, 0.733-0.860), 0.754 (95% CI, 0.678-0.829), and 0.727 (95% CI, 0.641-0.813) in the training and three validation datasets, respectively. Decision curve analysis verified the clinical usefulness. Furthermore, according to Kaplan-Meier curves, patients with a high likelihood of GR as determined by the combined model had better disease-free survival than those with a low probability. This radiomics model was developed based on large-sample size, multicenter datasets, and prospective validation with high radiomics quality score, and also had clinical utility.