A radiomics nomogram based on 18 F-FDG PET/CT and clinical risk factors for the prediction of peritoneal metastasis in gastric cancer.

PURPOSE: Peritoneal metastasis (PM) is usually considered an incurable factor of gastric cancer (GC) and not fit for surgery. The aim of this study is to develop and validate an 18 F-FDG PET/CT-derived radiomics model combining with clinical risk factors for predicting PM of GC. METHOD: In this retrospective study, 410 GC patients (PM - = 281, PM + = 129) who underwent preoperative 18 F-FDG PET/CT images from January 2015 to October 2021 were analyzed. The patients were randomly divided into a training cohort (n = 288) and a validation cohort (n = 122). The maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator method were applied to select feature. Multivariable logistic regression analysis was preformed to develop the predicting model. Discrimination, calibration, and clinical usefulness were used to evaluate the performance of the nomogram. RESULT: Fourteen radiomics feature parameters were selected to construct radiomics model. The area under the curve (AUC) of the radiomics model were 0.86 [95% confidence interval (CI), 0.81-0.90] in the training cohort and 0.85 (95% CI, 0.78-0.92) in the validation cohort. After multivariable logistic regression, peritoneal effusion, mean standardized uptake value (SUVmean), carbohydrate antigen 125 (CA125) and radiomics signature showed statistically significant differences between different PM status patients( P  < 0.05). They were chosen to construct the comprehensive predicting model which showed a performance with an AUC of 0.92 (95% CI, 0.89-0.95) in the training cohort and 0.92 (95% CI, 0.86-0.98) in the validation cohort, respectively. CONCLUSION: The nomogram based on 18 F-FDG PET/CT radiomics features and clinical risk factors can be potentially applied in individualized treatment strategy-making for GC patients before the surgery.

Quantitative assessment of intratumoral 2-[18F]FDG metabolic spatial distribution in hypermetabolic pulmonary lesions in PET/CT.

Background: The purpose of this study was to evaluate the value of quantitative assessment of intratumoral 2-deoxy-2-[18F]fluoro-D-glucose (2-[18F]FDG) metabolic spatial distribution (Q-FMSD) in differentiating pulmonary lesions with high 2-[18F]FDG uptake. Methods: In this retrospective study, a total of 564 patients with pulmonary lesions who underwent 2-[18F]FDG positron emission tomography/computed tomography (PET/CT) examination were analyzed. The maximum standard uptake value (SUVmax) of the proximal (pSUVmax) and distal (dSUVmax) regions of the lesions were measured, respectively. Then, Q-FMSD was obtained by the ratio of pSUVmax to dSUVmax. The diagnostic performance and area under receiver operating characteristic curve (AUC) were compared between Q-FMSD and conventional PET/CT methods for the diagnosis of pulmonary lesions with high 2-[18F]FDG uptake. Results: The malignant tumors presented significantly higher Q-FMSD values than the benign lesions (1.11 vs. 0.94, P<0.001), which indicated that the 2-[18F]FDG uptake in the proximal region was significantly higher than that of distal region in malignant lesions when compared with benign ones. For distinguishing hypermetabolic pulmonary malignant and benign lesions, the sensitivity, specificity and accuracy of Q-FMSD were 96.9%, 83.2% and 92.7%, respectively. Compared with other traditional methods, Q-FMSD presented significantly higher specificity than visual PET/CT (61.8%, P<0.001), retention index (RI) (33.8%, P<0.001) and SUVmax (11.0%, P<0.001). The AUC of Q-FMSD was 0.920, which was obviously larger than that of the SUVmax (0.587, P<0.001), RI (0.701, P<0.001), and visual PET/CT (0.781, P<0.001). Conclusions: Q-FMSD provides a simply and quantitative indicator for differentiating hypermetabolic pulmonary lesions with higher diagnostic performance than conventional PET/CT methods. Therefore, Q-FMSD should be recommended as a new promising marker to improve the diagnostic performance of hypermetabolic pulmonary lesions in clinical practice.

Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis.

OBJECTIVES: The aim of this study was to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting peritoneal metastasis (PM) of gastric cancer (GC). METHODS: In this study, a total of 355 patients (109PM+, 246PM-) who underwent preoperative fluorine-18-fludeoxyglucose (18F-FDG) PET images were retrospectively analyzed. According to a 7:3 ratio, patients were randomly divided into a training set and a validation set. Radiomics features and metabolic parameters data were extracted from PET images. The radiomics features were selected by logistic regression after using maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator (LASSO) method. The radiomics models were based on the rest of these features. The performance of the models was determined by their discrimination, calibration, and clinical usefulness in the training and validation sets. RESULTS: After dimensionality reduction, 12 radiomics feature parameters were obtained to construct radiomics signatures. According to the results of the multivariate logistic regression analysis, only carbohydrate antigen 125 (CA125), maximum standardized uptake value (SUVmax), and the radiomics signature showed statistically significant differences between patients (P<0.05). A radiomics model was developed based on the logistic analyses with an AUC of 0.86 in the training cohort and 0.87 in the validation cohort. The clinical prediction model based on CA125 and SUVmax was 0.76 in the training set and 0.69 in the validation set. The comprehensive model, which contained a rad-score and the clinical factor (CA125) as well as the metabolic parameter (SUVmax), showed promising performance with an AUC of 0.90 in the training cohort and 0.88 in the validation cohort, respectively. The calibration curve showed the actual rate of the nomogram-predicted probability of peritoneal metastasis. Decision curve analysis (DCA) also demonstrated the good clinical utility of the radiomics nomogram. CONCLUSIONS: The comprehensive model based on the rad-score and other factors (SUVmax, CA125) can provide a novel tool for predicting peritoneal metastasis of gastric cancer patients preoperatively.

A radiomic-based model of different contrast-enhanced CT phase for differentiate intrahepatic cholangiocarcinoma from inflammatory mass with hepatolithiasis.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is hard to distinguish from inflammatory mass (IM) complicated with hepatolithiasis in clinical practice preoperatively. This study looked to develop and confirm the radiomics models to make a distinction between ICC with hepatolithiasis from IM and to compare the results of different contrast-enhanced computed tomography (CT) phase. METHODS: The models were developed in a training cohort of 110 patients from January 2005 to June 2020. Radiomics features were extracted from both arterial phase and portal venous phase contrast-enhanced computed tomography (CT) scans. The radiomics scores based on radiomics features, were built by logistic regression after using the least absolute shrinkage and selection operator (LASSO) method. The rad-scores of two contrast -enhanced CT phases and clinical features were incorporated into a novel model. The performance of the models were determined by theirs discrimination, calibration, and clinical usefulness. The models were externally validated in 35 consecutive patients. RESULTS: The radiomics signature comprised two features in arterial phase (training cohort, AUC = 0.809, sensitivity 0.700, specificity 0.848, and accuracy 0.774;validation cohort, AUC = 0.790, sensitivity 0.714, specificity 0.800, and accuracy 0.757) and three related features in portal venous phase (training cohort, AUC = 0.801, sensitivity 0.800, specificity 0.717, and accuracy 0.759; validation cohort, AUC = 0.830, sensitivity 0.700, specificity 0.750, and accuracy 0.775) showed significant association with ICC in both cohorts (P < 0.05).We also developed a model only based on clinical variables (training cohort, AUC = 0.778, sensitivity 0.567, specificity 0.891, and accuracy 0.729; validation cohort, AUC = 0.788, sensitivity 0.571, specificity 0.950, and accuracy 0.761). The radiomics-based model contained rad-score of two phases and two clinical factors (CEA and CA19-9) showed the best performance (training cohort, AUC = 0.864, sensitivity 0.867, specificity 0.804, and accuracy 0.836; validation cohort, AUC = 0.843, sensitivity 0.643, specificity 0.980, and accuracy 0.821). CONCLUSIONS: Our radiomics-based models provided a diagnostic tool for differentiate intrahepatic cholangiocarcinoma (ICC) from inflammatory mass (IM) with hepatolithiasis both in arterial phase and portal venous phase. To go a step further, the diagnostic accuracy will improved by a clinico-radiologic model.

Development and Validation of a Radiomic-Based Model for Prediction of Intrahepatic Cholangiocarcinoma in Patients With Intrahepatic Lithiasis Complicated by Imagologically Diagnosed Mass.

BACKGROUND: This study was conducted with the intent to develop and validate a radiomic model capable of predicting intrahepatic cholangiocarcinoma (ICC) in patients with intrahepatic lithiasis (IHL) complicated by imagologically diagnosed mass (IM). METHODS: A radiomic model was developed in a training cohort of 96 patients with IHL-IM from January 2005 to July 2019. Radiomic characteristics were obtained from arterial-phase computed tomography (CT) scans. The radiomic score (rad-score), based on radiomic features, was built by logistic regression after using the least absolute shrinkage and selection operator (LASSO) method. The rad-score and other independent predictors were incorporated into a novel comprehensive model. The performance of the Model was determined by its discrimination, calibration, and clinical usefulness. This model was externally validated in 35 consecutive patients. RESULTS: The rad-score was able to discriminate ICC from IHL in both the training group (AUC 0.829, sensitivity 0.868, specificity 0.635, and accuracy 0.723) and the validation group (AUC 0.879, sensitivity 0.824, specificity 0.778, and accuracy 0.800). Furthermore, the comprehensive model that combined rad-score and clinical features was great in predicting IHL-ICC (AUC 0.902, sensitivity 0.771, specificity 0.923, and accuracy 0.862). CONCLUSIONS: The radiomic-based model holds promise as a novel and accurate tool for predicting IHL-ICC, which can identify lesions in IHL timely for hepatectomy or avoid unnecessary surgical resection.