Eun Kyoung Hong

Generative artificial intelligence can enhance efficiency and consistency in chest radiograph reporting when used with radiologist oversight and standardized evaluation frameworks.

A domain-specific multimodal generative AI model demonstrated potential for high diagnostic accuracy and clinical value in providing preliminary interpretations of chest radiographs for radiologists.

Use of a multimodal generative artificial intelligence model increased the efficiency and quality of chest radiograph interpretations by reducing reading times and increasing report accuracy and agreement.

Purpose: Pleural plaques (PPs) are morphologic manifestations of long-term asbestos exposure. The relationship between PP and lung function is not well understood, whereas the time-consuming nature of PP delineation to obtain volume impedes research. To automate the laborious task of delineation, we aimed to develop automatic artificial intelligence (AI)–driven segmentation of PP. Moreover, we aimed to explore the relationship between pleural plaque volume (PPV) and pulmonary function tests. Materials and Methods: Radiologists manually delineated PPs retrospectively in computed tomography (CT) images of patients with occupational exposure to asbestos (May 2014 to November 2019). We trained an AI model with a no-new-UNet architecture. The Dice Similarity Coefficient quantified the overlap between AI and radiologists. The Spearman correlation coefficient (r) was used for the correlation between PPV and pulmonary function test metrics. When recorded, these were vital capacity (VC), forced vital capacity (FVC), and diffusing capacity for carbon monoxide (DLCO). Results: We trained the AI system on 422 CT scans in 5 folds, each time with a different fold (n = 84 to 85) as a test set. On these independent test sets combined, the correlation between the predicted volumes and the ground truth was r = 0.90, and the median overlap was 0.71 Dice Similarity Coefficient. We found weak to moderate correlations with PPV for VC (n = 80, r = −0.40) and FVC (n = 82, r = −0.38), but no correlation for DLCO (n = 84, r = −0.09). When the cohort was split on the median PPV, we observed statistically significantly lower VC (P = 0.001) and FVC (P = 0.04) values for the higher PPV patients, but not for DLCO (P = 0.19). Conclusion: We successfully developed an AI algorithm to automatically segment PP in CT images to enable fast volume extraction. Moreover, we have observed that PPV is associated with loss in VC and FVC.

Purpose To assess the role of radiomics in detection of high-risk (pT3-4) colon cancer and develop a combined model that combines both radiomics and CT staging of colon cancer. Methods We included 292 colon cancer patients who underwent pre-operative CT and primary surgical resection within 2 months. Three-dimensional segmentations and CT staging of primary colon tumors were done. From each 3D segmentation of colon tumor, radiomic features were automatically extracted. Logistic regression analysis was performed to identify associations between radiomic features and high-risk (pT3-4) colon tumors. A combined model that integrated both radiomics and CT staging was developed and their diagnostic performance was compared with that of conventional CT staging. Tenfold cross-validation was used to validate the performance of the model and CT staging. Results The model that combined radiomic features and CT staging demonstrated a significantly better performance in detection of high-risk colon tumors in training set (AUC = 0.799, 95% CI: 0.720–0.839 for combined model and AUC = 0.697, 95% CI = 0.538–0.756 for CT staging only, p < 0.001 for difference). Cross-validation results also demonstrated significantly better detection performance of combined model (AUC = 0.727, 95% Confidence Interval (CI): 0.621–0.777 for combined model and AUC = 0.628, 95% CI = 0.558–0.689 for CT staging only, Boot CI = 0.099). Conclusion CT radiomic features of primary colon cancer, combined with CT staging, can improve the detection of high-risk colon cancer patients.

Purpose To evaluate the diagnostic accuracy of imaging features to predict lymph node status of colon cancer using CT. Methods This was a retrospective study from 2 tertiary hospitals in South Korea and Netherlands. 317 Colon cancer patients who underwent primary surgical treatment were included. Number of lymph nodes according to the anatomical location, size, cluster, degree of attenuation, shape, presence of internal heterogeneity and ill-defined margin of the lymph node were assessed and compared according to histological lymph node status. Results The largest short diameter of lymph node and presence of internal heterogeneity of lymph node showed significant association with malignant lymph node status (P < 0.001 and P = 0.041, respectively). The ROC curve analysis revealed AUC of 0.703 for the largest short diameter of lymph node (P < 0.001), and AUC of the presence of internal heterogeneity was 0.630 (P < 0.001). In addition, our study showed that a total number of lymph nodes, regardless of size, (P = 0.022) and number of lymph nodes in peritumoral area (P < 0.001) and along the mesenteric vessels (P < 0.001) on CT demonstrated significant association with malignant status of lymph nodes in colon cancer. Conclusions There were significant associations between lymph node status and imaging features of lymph nodes on CT in colon cancer patients. The largest short diameter of lymph node and presence of internal heterogeneity can be used to predict the malignant status of lymph node in colon cancer patients. Also, the number of lymph nodes near the colonic tumor should be considered in assessment of colon cancer lymph node involvement on CT.

Hong EK, et al. Korean J Radiol. 2020 Jul;21:e150. https://doi.org/10.3348/kjr.2020.0011