This study focused on a novel strategy that combines deep learning and radiomics to predict epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer (NSCLC) using computed tomography (CT). A total of 1280 patients with NSCLC who underwent contrast-enhanced CT scans and EGFR mutation testing before treatment were selected for the final study. Regions of interest were segmented from the CT images to extract radiomics features and obtain tumor images. These tumor images were input into a convolutional neural network model to extract 512 image features, which were combined with radiographic features and clinical data to predict the EGFR mutation. The generalization performance of the model was evaluated using external institutional data. The internal and external datasets contained 324 and 130 EGFR mutants, respectively. Sex, height, weight, smoking history, and clinical stage were significantly different between the EGFR-mutant patient groups. The EGFR mutations were predicted by combining the radiomics and clinical features, and an external validation dataset yielded an area under the curve (AUC) value of 0.7038. The model utilized 1280 tumor images, radiomics features, and clinical characteristics as input data and exhibited an AUC of approximately 0.81 and 0.78 during the primary cohort and external validation, respectively. These results indicate the feasibility of integrating radiomics analysis with deep learning for predicting EGFR mutations. CT-image-based genetic testing is a simple EGFR mutation prediction method, which can improve the prognosis of NSCLC patients and help establish personalized treatment strategies.

This study aimed to evaluate patterns of posttransplant malignancies among renal transplant recipients (RTRs) in South Korea using nationwide data. The nationwide cohort assessed in this study included RTRs from January 1, 2010, to December 31, 2014. We analyzed cancer incidence during the time course after renal transplantation. Additionally, we calculated standardized incidence ratios (SIRs) to evaluate the risk of malignancies in RTRs. A total of 1343 RTRs (871 males and 472 females, mean age 48.5 ± 11.6 years) were assessed. Among them, 104 (7.7%) developed malignancies after transplantation, most commonly in the thyroid cancer (23.1%). The SIR for all cancers was 3.54; particularly, the SIRs for renal cancer, myeloma, and non-Hodgkin lymphoma were 16.31, 24.02, and 28.64, respectively. Females showed a higher risk of malignancy than males (SIRs: 4.04 for women and 3.26 for men). The median interval between transplantation and malignancy diagnosis was 27.2 months (range 12.3–54.8 months). RTRs in South Korea demonstrated a high risk of malignancy after transplantation compared with the general population. This indicates that close surveillance and routine screening for cancer in RTRs are needed.

Background: Localization of the tumor bed of breast cancer is crucial for accurate planning of boost irradiation. Lumpectomy cavity and surgical clips provide localizing information about tumor bed. However, defining the tumor bed is often difficult because of presence of unclear lumpectomy cavity and lack of certain information such as absence of surgical clips. In the present study, we evaluated the feasibility of initial diagnostic PET-CT in localization of the tumor bed using deformable image registration (DIR). Methods: We selected twenty-five patients who had an initial diagnostic PET-CT performed and underwent breast-conserving surgery with surgical clips in tumor bed. In every individual patient, two target volumes were separately delineated on planning CT; 1) target volume based on surgical clips with a margin of 1 cm (TV_clip) and 2) tumor volume based on 90% of maximum SUV on PET-CT registered by DIR (TV_PET). The percent of TV_PET in TV_clip (V_in) was calculated and distance between center points of two volumes (D_center) was also measured. Results: Mean D_center between two volumes was 1.4 cm (range, 0.33 – 2.53). Mean V_in was 94.8% (range, 60.9-100) and 100% in 18 out of 25 patients. When compared to the center of TV_clip, the center of TV_PET tended to be located posteriorly (mean 0.3 cm, standard deviation 0.6), laterally (mean 0.3 cm, standard deviation 0.8) and inferiorly (mean 0.4 cm, standard deviation 0.9). Conclusion: Initial diagnostic PET-CT can be one of the possible references to localize the tumor bed in breast cancer radiotherapy.

Heo J, et al. J Gynecol Oncol. 2018 Aug;30:e15. https://doi.org/10.3802/jgo.2019.30.e15

EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently reflect tumor heterogeneity and lacks interpretability. In this study, we developed a deep learning model to predict the presence of EGFR mutations by analyzing histopathological patterns in whole slide images (WSIs). We also introduced the EGFR mutation prevalence (EMP) score, which quantifies EGFR prevalence in WSIs based on patch‐level predictions, and evaluated its interpretability and utility. Our model estimates the probability of EGFR prevalence in each patch by partitioning the WSI based on multiple‐instance learning and predicts the presence of EGFR mutations at the slide level. We utilized a patch‐masking scheduler training strategy to enable the model to learn various histopathological patterns of EGFR. This study included 868 WSI samples from lung adenocarcinoma patients collected from three medical institutions: Hallym University Medical Center, Inha University Hospital, and Chungnam National University Hospital. For the test dataset, 197 WSIs were collected from Ajou University Medical Center to evaluate the presence of EGFR mutations. Our model demonstrated prediction performance with an area under the receiver operating characteristic curve of 0.7680 (0.7607–0.7720) and an area under the precision‐recall curve of 0.8391 (0.8326–0.8430). The EMP score showed Spearman correlation coefficients of 0.4705 (p = 0.0087) for p.L858R and 0.5918 (p = 0.0037) for exon 19 deletions in 64 samples subjected to next‐generation sequencing analysis. Additionally, high EMP scores were associated with papillary and acinar patterns (p = 0.0038 and p = 0.0255, respectively), whereas low EMP scores were associated with solid patterns (p = 0.0001). These results validate the reliability of our model and suggest that it can provide crucial information for rapid screening and treatment plans.