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A lack of standardization in heart team implementation potentially leads to suboptimal decision-making quality, and we previously established a modified heart team protocol to improve the decision-making quality. The present trial was to validate the effect of the modified heart team implementation protocol on improving the decision-making quality versus the conventional protocol in complex coronary artery disease (CAD). Eligible interventional cardiologists, cardiac surgeons and non-interventional cardiologists were randomly allocated to the intervention or control arm and established 12 heart teams in each arm. The 12 heart teams in each arm were randomly divided into 6 pairs, and 480 historic cases with complex CAD into 6 sets of 80 cases. In each arm, each set of 80 cases was discussed independently by one pair of heart teams, with each case finally receiving two heart team decisions ('pairwise decisions'). The intervention arm conducted heart team decision-making according to the previously established protocol and the control arm based on guideline recommendations. The primary outcome was the overall percent agreement of the inter-team pairwise decisions. Decision-making appropriateness was further analysed. A total of 36 cardiac surgeons, 36 interventional cardiologists and 12 non-interventional cardiologists from 26 centres were enrolled. The overall percent agreement was significantly higher in the intervention arm than the control arm (72.1% vs 65.8%, P = 0.04; kappa 0.51 vs 0.37). Both team-level (19.4% vs 33.0%; P < 0.001) and specialist-level (interventional cardiologists, 19.8% vs 37.7%, P < 0.001; cardiac surgeons, 19.8% vs 28.7%, P < 0.001) inappropriateness rate of decision-making was significantly lower in the intervention arm than the control arm. The modified heart team implementation protocol improved the decision-making quality and appropriateness compared with the guideline-based protocol.

ObjectivesTo evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) presenting as solitary solid pulmonary nodules (SSPNs).MethodsRoutine CT images of 550 patients with SSPNs were retrospectively obtained from two centers. A convolutional neural network was used to extract deep learning features from all lesions. The training set consisted of data for 218 patients. The least absolute shrinkage and selection operator logistic regression was used to create a deep learning signature (DLS). Clinical factors and CT-based subjective findings were combined in a clinical model. An individualized DLN incorporating DLS, clinical factors, and CT-based subjective findings was constructed to validate the diagnostic ability. The performance of the DLN was assessed by discrimination and calibration using internal (n = 140) and external validation cohorts (n = 192).ResultsDLS, gender, age, and lobulated shape were found to be independent predictors and were used to build the DLN. The combination showed better diagnostic accuracy than any single model evaluated using the net reclassification improvement method (p < 0.05). The areas under the curve in the training, internal validation, and external validation cohorts were 0.889 (95% confidence interval [CI], 0.839–0.927), 0.879 (95% CI, 0.813–0.928), and 0.809 (95% CI, 0.746–0.862), respectively. Decision curve analysis and stratification analysis showed that the DLN has potential generalization ability.ConclusionsThe CT-based DLN can preoperatively distinguish between LAC and TBG in patients presenting with SSPNs.Key Points• The deep learning nomogram was developed to preoperatively differentiate TBG from LAC in patients with SSPNs.• The performance of the deep learning feature was superior to that of the radiomics feature.• The deep learning nomogram achieved superior performance compared to the deep learning signature, the radiomics signature, or the clinical model alone.

Objectives Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs). Methods Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve. Results To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924–0.993), 0.868 (95% CI: 0.765–0.970), 0.846 (95% CI: 0.750–0.942), and 0.846 (95% CI: 0.735–0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model. Conclusions The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs. Clinical relevance statement Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice. Key Points • A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients . • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model . • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs .

Purpose To develop a radiomics nomogram based on computed tomography (CT) images that can help differentiate lung adenocarcinomas and granulomatous lesions appearing as sub-centimeter solid nodules (SCSNs). Materials and methods The records of 214 consecutive patients with SCSNs that were surgically resected and histologically confirmed as lung adenocarcinomas ( n  = 112) and granulomatous lesions ( n  = 102) from 2 medical institutions between October 2011 and June 2019 were retrospectively analyzed. Patients from center 1 ware enrolled as training cohort ( n  = 150) and patients from center 2 were included as external validation cohort ( n  = 64), respectively. Radiomics features were extracted from non-contrast chest CT images preoperatively. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature extraction and radiomics signature construction. Clinical characteristics, subjective CT findings, and radiomics signature were used to develop a predictive radiomics nomogram. The performance was examined by assessment of the area under the receiver operating characteristic curve (AUC). Results Lung adenocarcinoma was significantly associated with an irregular margin and lobulated shape in the training set ( p  = 0.001, < 0.001) and external validation set ( p  = 0.016, = 0.018), respectively. The radiomics signature consisting of 22 features was significantly associated with lung adenocarcinomas of SCSNs ( p  < 0.001). The radiomics nomogram incorporated the radiomics signature, gender and lobulated shape. The AUCs of combined model in the training and external validation dataset were 0.885 (95% confidence interval [CI]: 0.823–0.931), 0.808 (95% CI: 0.690–0.896), respectively. Decision curve analysis (DCA) demonstrated that the radiomics nomogram was clinically useful. Conclusion A radiomics signature based on non-enhanced CT has the potential to differentiate between lung adenocarcinomas and granulomatous lesions. The radiomics nomogram incorporating the radiomics signature and subjective findings may facilitate the individualized, preoperative treatment in patients with SCSNs.

Purpose: This study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features. Then, a model was built to preoperatively distinguish lung granulomatous nodules (LGNs) from lung adenocarcinoma (LAC) in solitary pulmonary solid nodules (SPSNs). Methods: Data from 841 patients with SPSNs from five centres were collected retrospectively. First, adaptive cross-domain transfer learning was used to construct transfer learning signatures (TLS) under different source domain data and conduct a comparative analysis. The Wasserstein distance was used to assess the similarity between the source domain and target domain data in cross-domain transfer learning. Second, a cross-domain transfer learning radiomics model (TLRM) combining the best performing TLS, clinical factors and subjective CT findings was constructed. Finally, the performance of the model was validated through multicentre validation cohorts. Results: Relative to other source domain data, TLS based on lung whole slide images as source domain data (TLS-LW) had the best performance in all validation cohorts (AUC range: 0.8228–0.8984). Meanwhile, the Wasserstein distance of TLS-LW was 1.7108, which was minimal. Finally, TLS-LW, age, spiculated sign and lobulated shape were used to build the TLRM. In all validation cohorts, The AUC ranges were 0.9074–0.9442. Compared with other models, decision curve analysis and integrated discrimination improvement showed that TLRM had better performance. Conclusions: The TLRM could assist physicians in preoperatively differentiating LGN from LAC in SPSNs. Furthermore, compared with other images, cross-domain transfer learning can extract robust image features when using lung whole slide images as source domain data and has a better effect.