For stage-I lung adenocarcinoma, the 5-years disease-free survival (DFS) rates of non-invasive adenocarcinoma (non-IA) is different with invasive adenocarcinoma (IA). This study aims to develop CT image based artificial intelligence (AI) schemes to classify between non-IA and IA nodules, and incorporate deep learning (DL) and radiomics features to improve the classification performance. We collect 373 surgical pathological confirmed ground-glass nodules (GGNs) from 323 patients in two centers. It involves 205 non-IA (including 107 adenocarcinoma in situ and 98 minimally invasive adenocarcinoma), and 168 IA. We first propose a recurrent residual convolutional neural network based on U-Net to segment the GGNs. Then, we build two schemes to classify between non-IA and IA namely, DL scheme and radiomics scheme, respectively. Third, to improve the classification performance, we fuse the prediction scores of two schemes by applying an information fusion method. Finally, we conduct an observer study to compare our scheme performance with two radiologists by testing on an independent dataset. Comparing with DL scheme and radiomics scheme (the area under a receiver operating characteristic curve (AUC): 0.83 ± 0.05, 0.87 ± 0.04), our new fusion scheme (AUC: 0.90 ± 0.03) significant improves the risk classification performance (p < 0.05). In a comparison with two radiologists, our new model yields higher accuracy of 80.3%. The kappa value for inter-radiologist agreement is 0.6. It demonstrates that applying AI method is an effective way to improve the invasiveness risk prediction performance of GGNs. In future, fusion of DL and radiomics features may have a potential to handle the classification task with limited dataset in medical imaging.

Background: Gastrointestinal stromal tumors (GISTs), which are the most common mesenchymal tumors of the digestive system, are treated varyingly according to the malignancy. The purpose of this study is to develop and validate a nomogram for preoperative prediction of the malignant potential in patients with GIST.Methods: A total of 440 patients with pathologically confirmed GIST after surgery in our hospital from January 2011 to July 2019 were retrospectively analyzed. They were randomly divided into the training set (n = 308) and validation set (n = 132). CT signs and texture features of each patient were analyzed and predictive model were developed using the least absolute shrinkage and selection operator (lasso) regression. Then a nomogram based on selected parameters was developed. The predictive effectiveness of nomogram was evaluated by the area under receiver operating characteristic (ROC) curve (AUC). Concordance index (C-index) and calibration plots were formulated to evaluate the reliability and accuracy of the nomogram by bootstrapping based on internal (training set) and external (validation set) validity. The clinical application value of the nomogram was determined through the decision curve analysis (DCA).Results: Totally 156 GIST patients with low-malignant (very low and low risk) and 284 ones with high-malignant potential (intermediate and high risk) are enrolled in this study. The prediction nomogram consisting of size, cystoid variation and meanValue had an excellent discrimination both in training and validation sets (AUCs (95% confidence interval(CI)): 0.935 (0.908, 0.961), 0.933 (0.892, 0.974); C-indices (95% CI): 0.941 (0.912, 0.956), 0.935 (0.901, 0.982); sensitivity: 81.4, 90.6%; specificity: 75.0, 75.7%; accuracy: 88.0, 88.6%, respectively). The calibration curves indicated a good consistency between the actual observation and nomogram prediction for differentiating GIST malignancy. Decision curve analysis demonstrated that the nomogram was clinically useful.Conclusion: This study presents a prediction nomogram that incorporates the CT signs and texture parameter, which can be conveniently used to facilitate the preoperative individualized prediction of malignancy in GIST patients.

Purpose: This study aims to develop a CT-based radiomics model to predict clinical outcomes of advanced non-small-cell lung cancer (NSCLC) patients treated with nivolumab.Methods: Forty-six stage IIIB/IV NSCLC patients without EGFR mutation or ALK rearrangement who received nivolumab were enrolled. After segmenting primary tumors depicting on the pre-anti-PD1 treatment CT images, 1,106 radiomics features were computed and extracted to decode the imaging phenotypes of these tumors. A L1-based feature selection method was applied to remove the redundant features and build an optimal feature pool. To predict the risk of progression-free survival (PFS) and overall survival (OS), the selected image features were used to train and test three machine-learning classifiers namely, support vector machine classifier, logistic regression classifier, and Gaussian Naïve Bayes classifier. Finally, the overall patients were stratified into high and low risk subgroups by using prediction scores obtained from three classifiers, and Kaplan–Meier survival analysis was conduct to evaluate the prognostic values of these patients.Results: To predict the risk of PFS and OS, the average area under a receiver operating characteristic curve (AUC) value of three classifiers were 0.73 ± 0.07 and 0.61 ± 0.08, respectively; the corresponding average Harrell’s concordance indexes for three classifiers were 0.92 and 0.79. The average hazard ratios (HR) of three models for predicting PFS and OS were 6.22 and 3.54, which suggested the significant difference of the two subgroup’s PFS and OS (p<0.05).Conclusion: The pre-treatment CT-based radiomics model provided a promising way to predict clinical outcomes for advanced NSCLC patients treated with nivolumab.

Objective: Local therapy (LT) could potentially prolong the survival of patient with advanced epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC) receiving tyrosine kinase inhibitors (TKIs) and harboring oligometastatic/oligoprogressive disease (OMD/OPD). However, the optimal imaging method for identifying patients with OMD/OPD remains controversial. The objective of this study was to investigate the clinical value of incorporating PET/CT in detecting patients with OMD/OPD. Methods: Consecutive cases with metastatic EGFR-mutant NSCLC undergoing first-line EGFR-TKI treatment were retrospectively screened and those receiving baseline PET/CT and brain magnetic resonance imaging (MRI) or complete conventional imaging (CIM), including brain MRI, chest computed tomography (CT), abdomen ultrasound or CT and bone scintigraphy were included. OMD/OPD was defined as metastases/progressions documented at a maximum of five lesions and three organs, otherwise was defined as multiple metastatic/progressive disease (MMD/MPD). Progression-free survival (PFS) and overall survival (OS) were analyzed. Results: Of the 392 patients evaluated, baseline OMD was detected in 22.7% (53/233) of patients by PET/CT and in 18.2% (29/159) of patients by CIM (p = 0.171). Among the patients evaluated with baseline PET/CT, patients with OMD had longer PFS (p = 0.016) and tendency of improved OS (p = 0.058) than those with MMD. However, this result was not observed with patients evaluated using baseline CIM. With a median follow-up of 24.2 (range, 1.1–124.6) months, 297 patients had their first disease progression (FPD), of whom 164 (55.2%) had adequate imaging scans to analyze the tumor distributions at FPD comprehensively. OPD was detected in 63.0% (34/54) and 35.0% (39/110) of patients among the PET/CT and CIM assessed group (p = 0.003), respectively. Among the PET/CT assessed group, patients with OPD had significantly longer post-progressive overall survival (OS2) than those with MPD (p = 0.011). However, no significant difference of OS2 in the CIM assessed group was found. Conclusion: Patients with OMD/OPD, evaluated by PET/CT but not CIM, generally had more favorable survival outcomes than those with MMD/MPD among patients with metastatic NSCLC undergoing first-line EGFR-TKI treatment. Advances in knowledge: PET/CT seems to affect the survival of patients under first-line EGFR-TKI treated metastatic NSCLC with OMD/OPD.