Purpose Lymphovascular invasion (LVI) and perineural invasion (PNI) are independent prognostic factors in patients with colorectal cancer (CRC). In this study, we aimed to develop and validate a preoperative predictive model based on high‐throughput radiomic features and clinical factors for accurate prediction of LVI/PNI in these patients. Methods Two hundred and sixty‐three patients who underwent colorectal resection for histologically confirmed CRC between 1 February 2011 and 30 June 2020 were retrospectively enrolled. Between 1 February 2011 and 30 September 2018, 213 patients were randomly divided into a training cohort (n = 149) and a validation cohort (n = 64) by a ratio of 7:3. We used a 10000‐iteration bootstrap analysis to estimate the prediction error and confidence interval for two cohorts. The independent test cohort consisted of 50 patients between 1 October 2018 and 30 June 2020. Regions of interest (ROIs) were manually delineated in high‐resolution T2‐weighted and diffusion‐weighted images using ITK‐SNAP software on each CRC tumor slice. In total, 3356 radiomic features were extracted from each ROI. Next, we used the maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms to select the strongest of these features to establish a clinical‐radiomics model for predicting LVI/PNI. Receiver‐operating characteristic and calibration curves were then plotted to evaluate the predictive performance of the model in the training, validation, and independent test cohorts. Results A multiparametric clinical‐radiomics model combining MRI‐reported extramural vascular invasion (EMVI) status and a Radiomics score for the LVI/PNI estimation was established. This model had significant predictive power in the training cohort (area under the curve [AUC] 0.91; 95% confidence interval [CI]: 0.85–0.97), validation cohort (AUC: 0.88; 95% CI: 0.79–89), and independent test cohorts (AUC 0.83, 95% CI 0.72–0.95). The model performed well in the independent test cohort with sensitivity of 0.818, specificity of 0.714, and accuracy of 0.760. Calibration curve and decision curve analysis demonstrated clinical benefits. Conclusion Multiparametric clinical‐radiomics models can accurately predict LVI/PNI in patients with CRC. Our model has predictive ability that should improve preoperative diagnostic performance and allow more individualized treatment decisions.

PurposeTo evaluate the efficacy and safety of percutaneous radiofrequency ablation (RFA) for subcapsular colorectal cancer liver metastases (CLMs).Materials and MethodsWith the approval of the Institutional Review Board, the clinical data of CLM patients who underwent percutaneous RFA for the first time from August 2010 to August 2020 were continuously collected. All CLMs were divided into subcapsular and non-capsular groups. Baseline characteristic data, technical effectiveness, minimal ablative margin, complications, local tumor progression (LTP), and overall survival (OS) between the two groups were analyzed using the t-test or chi-square test. A Cox regression model was used to evaluate the prognostic factors of LTP.ResultsOne hundred and ninety-nine patients (124 males; mean age, 60.2 years) with 402 CLMs (221 subcapsular; mean size, 16.0 mm) were enrolled in the study. Technical effectiveness was achieved in 93.5% (376/402) of CLMs, with a major complication rate of 5.5%. Compared with non-subcapsular tumors, the minimal ablative margin achieved in subcapsular CLM was smaller (χ2 = -8.047, P < 0.001). With a median follow-up time of 23 months (range, 3−96 months), 37.1% of the tumors had LTP. The estimated cumulative OS at 1, 3, and 5 years was 96.1%, 66.0%, and 44.2%, respectively. There were no statistically significant differences between the two groups in terms of technical effectiveness (χ2 = 0.484, P = 0.487), major complications (χ2 = 0.082, P = 0.775), local tumor progression-free survival (LTPFS) (χ2 = 0.881, P = 0.348), and OS (χ2 = 2.874, P = 0.090). Minimal ablative margin, tumor size (≥20 mm), and technical effectiveness were predictors of LTP (all P < 0.05).ConclusionRFA is a safe and effective technique for local tumor control of subcapsular CLMs.

Objective: To explore independent risk factors for incomplete radiofrequency ablation (iRFA) of colorectal cancer liver metastases (CRLM) and evaluate adverse outcomes following iRFA. Materials and Methods: Magnetic resonance imaging data of CRLM patients who received percutaneous RFA were randomized into training (70%) and validation set 1 (30%) data sets. An independent validation set 2 was derived from computed tomography scans. Uni-and multivariate analyses identified independent risk factors for iRFA. Area under the curve (AUC) values were used to evaluate the predictive model performance. Risk points were assigned to independent predictors, and iRFA was predicted according to the total risk score. Kaplan-Meier curves were used to assess new intrahepatic metastases (NIHM), unablated tumor progression, and overall survival (OS). Results: Multivariate regression determined as independent iRFA risk factors perivascular tumor location, subcapsular tumor location, tumor size !20 mm, and minimal ablative margin 5 mm. The AUC values of the model in the training set, validation set 1, and validation set 2 were 0.867, 0.772, and 0.820, respectively. The respective AUC values of the total risk score were 0.864, 0.768, and 0.817. During the 6-year follow-up, the cumulative OS was significantly shorter in the iRFA than in the complete RFA group, and NIHM (hazard ratio [HR] ¼ 2.79; 95% confidence interval [CI]: 1.725, 4.513) and unablated tumor progression (HR ¼ 3.473; 95% CI: 1.506, 8.007) were more severe. Conclusions: Perivascular tumor location, subcapsular tumor location, tumor size !20 mm, and minimal ablative margin 5 mm were independent risk factors for iRFA. iRFA may be a potential predictor of NIHM, unablated tumor progression, and OS.

BackgroundThis study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC). MethodsIn 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation.ResultsA total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04).ConclusionsA comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.