Development and external validation of MRI-based RAS mutation status prediction model for liver metastases of colorectal cancer.

BACKGROUND: The mutation status of rat sarcoma viral oncogene homolog (RAS) has prognostic significance and serves as a key predictive biomarker for the effectiveness of antiepidermal growth factor receptor (EGFR) therapy. However, there remains a lack of effective models for predicting RAS mutation status in colorectal liver metastases (CRLMs). This study aimed to construct and validate a diagnostic model for predicting RAS mutation status among patients undergoing hepatic resection for CRLMs. METHODS: A diagnostic multivariate prediction model was developed and validated in patients with CRLMs who had undergone hepatectomy between 2014 and 2020. Patients from Institution A were assigned to the model development group (i.e., Development Cohort), while patients from Institutions B and C were assigned to the external validation groups (i.e., Validation Cohort_1 and Validation Cohort_2). The presence of CRLMs was determined by examination of surgical specimens. RAS mutation status was determined by genetic testing. The final predictors, identified by a group of oncologists and radiologists, included several key clinical, demographic, and radiographic characteristics derived from magnetic resonance images. Multiple imputation was performed to estimate the values of missing non-outcome data. A penalized logistic regression model using the adaptive least absolute shrinkage and selection operator penalty was implemented to select appropriate variables for the development of the model. A single nomogram was constructed from the model. The performance of the prediction model, discrimination, and calibration were estimated and reported by the area under the receiver operating characteristic curve (AUC) and calibration plots. Internal validation with a bootstrapping procedure and external validation of the nomogram were assessed. Finally, decision curve analyses were used to characterize the clinical outcomes of the Development and Validation Cohorts. RESULTS: A total of 173 patients were enrolled in this study between January 2014 and May 2020. Of the 173 patients, 117 patients from Institution A were assigned to the Model Development group, while 56 patients (33 from Institution B and 23 from Institution C) were assigned to the Model Validation groups. Forty-six (39.3%) patients harbored RAS mutations in the Development Cohort compared to 14 (42.4%) in Validation Cohort_1 and 8 (34.8%) in Validation Cohort_2. The final model contained the following predictor variables: time of occurrence of CRLMs, location of primary lesion, type of intratumoral necrosis, and early enhancement of liver parenchyma. The diagnostic model based on clinical and MRI data demonstrated satisfactory predictive performance in distinguishing between mutated and wild-type RAS, with AUCs of 0.742 (95% confidence interval [CI]: 0.651─0.834), 0.741 (95% CI: 0.649─0.836), 0.703 (95% CI: 0.514─0.892), and 0.708 (95% CI: 0.452─0.964) in the Development Cohort, bootstrapping internal validation, external Validation Cohort_1 and Validation Cohort_2, respectively. The Hosmer-Lemeshow goodness-of-fit values for the Development Cohort, Validation Cohort_1 and Validation Cohort_2 were 2.868 (p = 0.942), 4.616 (p = 0.465), and 6.297 (p = 0.391), respectively. CONCLUSIONS: Integrating clinical, demographic, and radiographic modalities with a magnetic resonance imaging-based approach may accurately predict the RAS mutation status of CRLMs, thereby aiding in triage and possibly reducing the time taken to perform diagnostic and life-saving procedures. Our diagnostic multivariate prediction model may serve as a foundation for prognostic stratification and therapeutic decision-making.

A radiomics nomogram for predicting postoperative recurrence in esophageal squamous cell carcinoma.

Purpose: To establish and validate a radiomics nomogram for predicting recurrence of esophageal squamous cell carcinoma (ESCC) after esophagectomy with curative intent. Materials and methods: The medical records of 155 patients who underwent surgical treatment for pathologically confirmed ESCC were collected. Patients were randomly divided into a training group (n=109) and a validation group (n=46) in a 7:3 ratio. ​Tumor regions are accurately segmented in computed tomography images of enrolled patients. Radiomic features were then extracted from the segmented tumors. We selected the features by Max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. A radiomics signature was then built by logistic regression analysis. To improve predictive performance, a radiomics nomogram that incorporated the radiomics signature and independent clinical predictors was built. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA). Results: We selected the five most relevant radiomics features to construct the radiomics signature. The radiomics model had general discrimination ability with an area under the ROC curve (AUC) of 0.79 in the training set that was verified by an AUC of 0.76 in the validation set. The radiomics nomogram consisted of the radiomics signature, and N stage showed excellent predictive performance in the training and validation sets with AUCs of 0.85 and 0.83, respectively. Furthermore, calibration curves and the DCA analysis demonstrated good fit and clinical utility of the radiomics nomogram. Conclusion: We successfully established and validated a prediction model that combined radiomics features and N stage, which can be used to predict four-year recurrence risk in patients with ESCC who undergo surgery.

Deep learning radio-clinical signatures for predicting neoadjuvant chemotherapy response and prognosis from pretreatment CT images of locally advanced gastric cancer patients.

BACKGROUND: Early noninvasive screening of patients who would benefit from neoadjuvant chemotherapy (NCT) is essential for personalized treatment of locally advanced gastric cancer (LAGC). The aim of this study was to identify radio-clinical signatures from pretreatment oversampled computed tomography (CT) images to predict the response to NCT and prognosis of LAGC patients. METHODS: LAGC patients were retrospectively recruited from six hospitals from January 2008 to December 2021. An SE-ResNet50-based chemotherapy response prediction system was developed from pretreatment CT images preprocessed with an imaging oversampling method (i.e. DeepSMOTE). Then, the deep learning (DL) signature and clinic-based features were fed into the deep learning radio-clinical signature (DLCS). The predictive performance of the model was evaluated based on discrimination, calibration, and clinical usefulness. An additional model was built to predict overall survival (OS) and explore the survival benefit of the proposed DL signature and clinicopathological characteristics. RESULTS: A total of 1060 LAGC patients were recruited from six hospitals; the training cohort (TC) and internal validation cohort (IVC) patients were randomly selected from center I. An external validation cohort (EVC) of 265 patients from five other centers was also included. The DLCS exhibited excellent performance in predicting the response to NCT in the IVC [area under the curve (AUC), 0.86] and EVC (AUC, 0.82), with good calibration in all cohorts ( P >0.05). Moreover, the DLCS model outperformed the clinical model ( P <0.05). Additionally, we found that the DL signature could serve as an independent factor for prognosis [hazard ratio (HR), 0.828, P =0.004]. The concordance index (C-index), integrated area under the time-dependent ROC curve (iAUC), and integrated Brier score (IBS) for the OS model were 0.64, 1.24, and 0.71 in the test set. CONCLUSION: The authors proposed a DLCS model that combined imaging features with clinical risk factors to accurately predict tumor response and identify the risk of OS in LAGC patients prior to NCT, which can then be used to guide personalized treatment plans with the help of computerized tumor-level characterization.

A noninvasive nomogram model based on CT features to predict DNA mismatch repair deficiency in gastric cancer.

Objectives: DNA mismatch repair deficiency (dMMR) status has served as a positive predictive biomarker for immunotherapy and long-term prognosis in gastric cancer (GC). The aim of the present study was to develop a computed tomography (CT)-based nomogram for preoperatively predicting mismatch repair (MMR) status in GC. Methods: Data from a total of 159 GC patients between January 2020 and July 2021 with dMMR GC (n=53) and MMR-proficient (pMMR) GC (n=106) confirmed by postoperative immunohistochemistry (IHC) staining were retrospectively analyzed. All patients underwent abdominal contrast-enhanced CT. Significant clinical and CT imaging features associated with dMMR GC were extracted through univariate and multivariate analyses. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and internal validation of the cohort data were performed. Results: The nomogram contained four potential predictors of dMMR GC, including gender (odds ratio [OR] 9.83, 95% confidence interval [CI] 3.78-28.20, P < 0.001), age (OR 3.32, 95% CI 1.36-8.50, P = 0.010), tumor size (OR 5.66, 95% CI 2.12-16.27, P < 0.001) and normalized tumor enhancement ratio (NTER) (OR 0.15, 95% CI 0.06-0.38, P < 0.001). Using an optimal cutoff value of 6.6 points, the nomogram provided an area under the curve (AUC) of 0.895 and an accuracy of 82.39% in predicting dMMR GC. The calibration curve demonstrated a strong consistency between the predicted risk and observed dMMR GC. The DCA justified the relatively good performance of the nomogram model. Conclusion: The CT-based nomogram holds promise as a noninvasive, concise and accurate tool to predict MMR status in GC patients, which can assist in clinical decision-making.

A Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperative Prediction of DNA Mismatch Repair Deficiency in Gastric Adenocarcinoma.

Purpose: To develop and validate a radiomics nomogram integrated with clinic-radiological features for preoperative prediction of DNA mismatch repair deficiency (dMMR) in gastric adenocarcinoma. Materials and Methods: From March 2014 to August 2020, 161 patients with pathologically confirmed gastric adenocarcinoma were included from two centers (center 1 as the training and internal testing sets, n = 101; center 2 as the external testing sets, n = 60). All patients underwent preoperative contrast-enhanced computerized tomography (CT) examination. Radiomics features were extracted from portal-venous phase CT images. Max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used to select features, and then radiomics signature was constructed using logistic regression analysis. A radiomics nomogram was built incorporating the radiomics signature and independent clinical predictors. The model performance was assessed using receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA). Results: The radiomics signature, which was constructed using two selected features, was significantly associated with dMMR gastric adenocarcinoma in the training and internal testing sets (P < 0.05). The radiomics signature model showed a moderate discrimination ability with an area under the ROC curve (AUC) of 0.81 in the training set, which was confirmed with an AUC of 0.78 in the internal testing set. The radiomics nomogram consisting of the radiomics signature and clinical factors (age, sex, and location) showed excellent discrimination in the training, internal testing, and external testing sets with AUCs of 0.93, 0.82, and 0.83, respectively. Further, calibration curves and DCA analysis demonstrated good fit and clinical utility of the radiomics nomogram. Conclusions: The radiomics nomogram combining radiomics signature and clinical characteristics (age, sex, and location) may be used to individually predict dMMR of gastric adenocarcinoma.