Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Background: s/Aims: The hepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT) is classified as the advanced stage (BCLC stage C) with extremely poor prognosis, and in current guidelines is recommended for systemic therapy. This study aimed to evaluate the surgical outcomes and long-term prognosis after hepatic resection (HR) for patients who have HCC combined with PVTT. Methods: We retrospectively analyzed 332 patients who underwent HR for HCC with PVTT at ten tertiary referral hospitals in South Korea. Results: The median overall and recurrence-free survival after HR were 32.4 and 8.6 months, while the 1-, 3-, and 5-year overall survival rates were 75%, 48%, and 39%, respectively. In multivariate analysis, tumor number, tumor size, AFP, PIVKA−II, neutrophil-to-lymphocyte ratio, and albumin–bilirubin (ALBI) grade were significant prognostic factors. The risk scoring was developed using these seven factors–tumor, inflammation and hepatic function (TIF), to predict patient prognosis. The prognosis of the patients was well stratified according to the scores (log-rank test, p < 0.001). Conclusions HR for patients who have HCC combined with PVTT provided favorable survival outcomes. The risk scoring was useful in predicting prognosis, and determining the appropriate treatment strategy for those patients who have HCC with PVTT.

Purpose: It is unclear whether performing endosonography first in non–small cell lung cancer (NSCLC) patients with radiological N1 (rN1) has any advantages over surgery without nodal staging. We aimed to compare surgery without endosonography to performing endosonography first in rN1 on the overall survival (OS) of patients with NSCLC. Materials and Methods: This is a retrospective analysis of patients with rN1 NSCLC between 2013 and 2019. Patients were divided into ‘no endosonography’ and ‘endosonography first’ groups. We investigated the effect of nodal staging through endosonography on OS using propensity score matching (PSM) and multivariable Cox proportional hazard regression analysis. Results: In the no endosonography group, pathologic N2 occurred in 23.0% of patients. In the endosonography first group, endosonographic N2 and N3 occurred in 8.6% and 1.6% of patients, respectively. Additionally, 51 patients were pathologic N2 among 249 patients who underwent surgery and mediastinal lymph node dissection (MLND) in endosonography first group. After PSM, the 5-year OSs were 68.1% and 70.6% in the no endosonography and endosonography first groups, respectively. However, the 5-year OS was 80.2% in the subgroup who underwent surgery and MLND of the endosonography first group. Moreover, in patients receiving surgical resection with MLND, the endosonography first group tended to have a better OS than the no endosonography group in adjusted analysis using various models. Conclusion In rN1 NSCLC, preoperative endosonography shows better OS than surgery without endosonography. For patients with rN1 NSCLC who are candidates for surgery, preoperative endosonography may help improve survival through patient selection.

Purpose: Optimal treatment for stage IIIA/N2 non–small cell lung cancer (NSCLC) is controversial. We aimed to assess the efficacy and safety of adjuvant pembrolizumab for stage IIIA/N2 NSCLC completely resected after neoadjuvant concurrent chemoradiation therapy (CCRT). Materials and Methods: In this open-label, single-center, single-arm phase 2 trial, patients with stage IIIA/N2 NSCLC received adjuvant pembrolizumab for up to 2 years after complete resection following neoadjuvant CCRT. The primary endpoint was disease-free survival (DFS). Secondary endpoints included overall survival (OS) and safety. As an exploratory biomarker analysis, we evaluated the proliferative response of blood CD39+PD-1+CD8+ T cells using fold changes in the percentage of proliferating Ki-67+ cells from days 1 to 7 of cycle 1 (Ki-67D7/D1). Results: Between October 2017 and October 2018, 37 patients were enrolled. Twelve (32%) and three (8%) patients harbored EGFR and ALK alterations, respectively. Of 34 patients with programmed cell death ligand 1 assessment, 21 (62%), nine (26%), and four (12%) had a tumor proportion score of < 1%, 1%-50%, and ≥ 50%, respectively. The median follow-up was 71 months. The median DFS was 22.4 months in the overall population, with a 5-year DFS rate of 29%. The OS rate was 86% at 2 years and 76% at 5 years. Patients with tumor recurrence within 6 months had a significantly lower Ki-67D7/D1 among CD39+PD-1+CD8+ T cells than those without (p=0.036). No new safety signals were identified. Conclusion Adjuvant pembrolizumab may offer durable disease control in a subset of stage IIIA/N2 NSCLC patients after neoadjuvant CCRT and surgery.

Objective: Numerous studies have identified hemodynamic changes in adults with major depressive disorder (MDD) by using functional near-infrared spectroscopy (fNIRS). However, studies on adolescents with MDD are limited. As adolescence is a stage of rapid brain development, differences may occur depending on age. This study used fNIRS as an objective tool to investigate hemodynamic changes in the frontal lobe according to depression severity and age in adolescents with MDD. Methods: Thirty adolescents (12 aged 12−15 years and 18 aged 16−18 years) were retrospectively investigated. The Children’s Depression Inventory was used as a psychiatric evaluation scale, fNIRS was used as an objective brain function evaluation tool, and the Verbal Fluency Test was performed. Results: During the Verbal Fluency Test, in the younger MDD group, oxygenated-hemoglobin concentration increased in the right dorsolateral prefrontal cortex region as the severity of depression increased. In the older MDD group, the oxygenated-hemoglobin concentration decreased in the right dorsolateral prefrontal cortex region as the severity of depression increased. Conclusion These results suggest that fNIRS may be an objective tool for identifying age differences among adolescents with MDD. To generalize the results and verify fNIRS as a potential biomarker tool, follow-up studies with a larger sample group should be conducted.