Transcriptional Changes in Radiation-Induced Lung Injury: A Comparative Analysis of Two Radiation Doses for Preclinical Research.

In a recent stereotactic body radiation therapy animal model, radiation pneumonitis and radiation pulmonary fibrosis were observed at around 2 and 6 weeks, respectively. However, the molecular signature of this model remains unclear. This study aimed to examine the molecular characteristics at these two stages using RNA-seq analysis. Transcriptomic profiling revealed distinct transcriptional patterns for each stage. Inflammatory response and immune cell activation were involved in both stages. Cell cycle processes and response to type II interferons were observed during the inflammation stage. Extracellular matrix organization and immunoglobulin production were noted during the fibrosis stage. To investigate the impact of a 10 Gy difference on fibrosis progression, doses of 45, 55, and 65 Gy were tested. A dose of 65 Gy was selected and compared with 75 Gy. The 65 Gy dose induced inflammation and fibrosis as well as the 75 Gy dose, but with reduced lung damage, fewer inflammatory cells, and decreased collagen deposition, particularly during the inflammation stage. Transcriptomic analysis revealed significant overlap, but differences were observed and clarified in Gene Ontology and KEGG pathway analysis, potentially influenced by changes in interferon-gamma-mediated lipid metabolism. This suggests the suitability of 65 Gy for future preclinical basic and pharmaceutical research connected with radiation-induced lung injury.

Intercellular cross-talk through lineage-specific gap junction of cancer-associated fibroblasts related to stromal fibrosis and prognosis.

Stromal fibrosis in cancer is usually associated with poor prognosis and chemotherapy resistance. It is thought to be caused by fibroblasts; however, the exact mechanism is not yet well understood. The study aimed to identify lineage-specific cancer-associated fibroblast (CAF) subgroup and their associations with extracellular matrix remodeling and clinical significances in various tumor types using single-cell and bulk RNA sequencing data. Through unsupervised clustering, six subclusters of CAFs were identified, including a cluster with exclusively high gap junction protein beta-2 (GJB2) expression. This cluster was named GJB2-positive CAF. It was found to be a unique subgroup of terminally differentiated CAFs associated with collagen gene expression and extracellular matrix remodeling. GJB2-positive CAFs showed higher communication frequency with vascular endothelial cells and cancer cells than GJB2-negative CAFs. Moreover, GJB2 was poorly expressed in normal tissues, indicating that its expression is dependent on interaction with other cells, including vascular endothelial cells and cancer cells. Finally, the study investigated the clinical significance of GJB2 signature score for GJB2-positive CAFs in cancer and found a correlation with poor prognosis. These results suggest that GJB2-positive CAF is a unique fibroblast subtype involved in extracellular matrix remodeling, with significant clinical implications in cancer.

Utility of artificial intelligence with deep learning of hematoxylin and eosin-stained whole slide images to predict lymph node metastasis in T1 colorectal cancer using endoscopically resected specimens; prediction of lymph node metastasis in T1 colorectal cancer.

BACKGROUND: When endoscopically resected specimens of early colorectal cancer (CRC) show high-risk features, surgery should be performed based on current guidelines because of the high-risk of lymph node metastasis (LNM). The aim of this study was to determine the utility of an artificial intelligence (AI) with deep learning (DL) of hematoxylin and eosin (H&E)-stained endoscopic resection specimens without manual-pixel-level annotation for predicting LNM in T1 CRC. In addition, we assessed AI performance for patients with only submucosal (SM) invasion depth of 1000 to 2000 µm known to be difficult to predict LNM in clinical practice. METHODS: H&E-stained whole slide images (WSIs) were scanned for endoscopic resection specimens of 400 patients who underwent endoscopic treatment for newly diagnosed T1 CRC with additional surgery. The area under the curve (AUC) of the receiver operating characteristic curve was used to determine the accuracy of AI for predicting LNM with a fivefold cross-validation in the training set and in a held-out test set. RESULTS: We developed an AI model using a two-step attention-based DL approach without clinical features (AUC, 0.764). Incorporating clinical features into the model did not improve its prediction accuracy for LNM. Our model reduced unnecessary additional surgery by 15.1% more than using the current guidelines (67.4% vs. 82.5%). In patients with SM invasion depth of 1000 to 2000 µm, the AI avoided 16.1% of unnecessary additional surgery than using the JSCCR guidelines. CONCLUSIONS: Our study is the first to show that AI trained with DL of H&E-stained WSIs has the potential to predict LNM in T1 CRC using only endoscopically resected specimens with conventional histologic risk factors.

PRRX1 is a master transcription factor of stromal fibroblasts for myofibroblastic lineage progression.

Although stromal fibroblasts play a critical role in cancer progression, their identities remain unclear as they exhibit high heterogeneity and plasticity. Here, a master transcription factor (mTF) constructing core-regulatory circuitry, PRRX1, which determines the fibroblast lineage with a myofibroblastic phenotype, is identified for the fibroblast subgroup. PRRX1 orchestrates the functional drift of fibroblasts into myofibroblastic phenotype via TGF-ß signaling by remodeling a super-enhancer landscape. Such reprogrammed fibroblasts have myofibroblastic functions resulting in markedly enhanced tumorigenicity and aggressiveness of cancer. PRRX1 expression in cancer-associated fibroblast (CAF) has an unfavorable prognosis in multiple cancer types. Fibroblast-specific PRRX1 depletion induces long-term and sustained complete remission of chemotherapy-resistant cancer in genetically engineered mice models. This study reveals CAF subpopulations based on super-enhancer profiles including PRRX1. Therefore, mTFs, including PRRX1, provide another opportunity for establishing a hierarchical classification system of fibroblasts and cancer treatment by targeting fibroblasts.

Clinical Outcomes and Adverse Events of Gastric Endoscopic Submucosal Dissection of the Mid to Upper Stomach under General Anesthesia and Monitored Anesthetic Care.

BACKGROUND/AIMS: Endoscopic submucosal dissection (ESD) of gastric tumors in the mid-to-upper stomach is a technically challenging procedure. This study compared the therapeutic outcomes and adverse events of ESD of tumors in the mid-to-upper stomach performed under general anesthesia (GA) or monitored anesthesia care (MAC). METHODS: Between 2012 and 2018, 674 patients underwent ESD for gastric tumors in the midbody, high body, fundus, or cardia (100 patients received GA; 574 received MAC). The outcomes of the propensity score (PS)-matched (1:1) patients receiving either GA or MAC were analyzed. RESULTS: The PS matching identified 94 patients who received GA and 94 patients who received MAC. Both groups showed high rates of en bloc resection (GA, 95.7%; MAC, 97.9%; p=0.68) and complete resection (GA, 81.9%; MAC, 84.0%; p=0.14). There were no significant differences between the rates of adverse events (GA, 16.0%; MAC, 8.5%; p=0.18) in the anesthetic groups. Logistic regression analysis indicated that the method of anesthesia did not affect the rates of complete resection or adverse events. CONCLUSION: ESD of tumors in the mid-to-upper stomach at our high-volume center had good outcomes, regardless of the method of anesthesia. Our results demonstrate no differences between the efficacies and safety of ESD performed under MAC and GA.

Deep learning-based virtual cytokeratin staining of gastric carcinomas to measure tumor-stroma ratio.

The tumor-stroma ratio (TSR) determined by pathologists is subject to intra- and inter-observer variability. We aimed to develop a computational quantification method of TSR using deep learning-based virtual cytokeratin staining algorithms. Patients with 373 advanced (stage III [n = 171] and IV [n = 202]) gastric cancers were analyzed for TSR. Moderate agreement was observed, with a kappa value of 0.623, between deep learning metrics (dTSR) and visual measurement by pathologists (vTSR) and the area under the curve of receiver operating characteristic of 0.907. Moreover, dTSR was significantly associated with the overall survival of the patients (P = 0.0024). In conclusion, we developed a virtual cytokeratin staining and deep learning-based TSR measurement, which may aid in the diagnosis of TSR in gastric cancer.

Predicting Successes and Failures of Clinical Trials With Outer Product-Based Convolutional Neural Network.

Despite several improvements in the drug development pipeline over the past decade, drug failures due to unexpected adverse effects have rapidly increased at all stages of clinical trials. To improve the success rate of clinical trials, it is necessary to identify potential loser drug candidates that may fail at clinical trials. Therefore, we need to develop reliable models for predicting the outcomes of clinical trials of drug candidates, which have the potential to guide the drug discovery process. In this study, we propose an outer product-based convolutional neural network (OPCNN) model which integrates effectively chemical features of drugs and target-based features. The validation results via 10-fold cross-validations on the dataset used for a data-driven approach PrOCTOR proved that our OPCNN model performs quite well in terms of accuracy, F1-score, Matthews correlation coefficient (MCC), precision, recall, area under the curve (AUC) of the receiver operating characteristic, and area under the precision-recall curve (AUPRC). In particular, the proposed OPCNN model showed the best performance in terms of MCC, which is widely used in biomedicine as a performance metric and is a more reliable statistical measure. Through 10-fold cross-validation experiments, the accuracy of the OPCNN model is as high as 0.9758, F1 score is as high as 0.9868, the MCC reaches 0.8451, the precision is as high as 0.9889, the recall is as high as 0.9893, the AUC is as high as 0.9824, and the AUPRC is as high as 0.9979. The results proved that our OPCNN model shows significantly good prediction performance on outcomes of clinical trials and it can be quite helpful in early drug discovery.

Tumor Margin Contains Prognostic Information: Radiomic Margin Characteristics Analysis in Lung Adenocarcinoma Patients.

We aimed to investigate the relationship between tumor radiomic margin characteristics and prognosis in patients with lung cancer. We enrolled 334 patients who underwent complete resection for lung adenocarcinoma. A quantitative computed tomography analysis was performed, and 76 radiomic margin characteristics were extracted. The radiomic margin characteristics were correlated with overall survival. The selected clinical variables and radiomic margin characteristics were used to calculate a prognostic model with subsequent internal and external validation. Nearly all of the radiomic margin characteristics showed excellent reproducibility. The least absolute shrinkage and selection operator (LASSO) method was used to select eight radiomic margin characteristics. When compared to the model with clinical variables only (C-index = 0.738), the model incorporating clinical variables and radiomic margin characteristics (C-index = 0.753) demonstrated a higher C-index for predicting overall survival. In the model integrating both clinical variables and radiomic margin characteristics, convexity, a Laplace of Gaussian (LoG) kurtosis of 3, and the roundness factor were each independently predictive of overall survival. In addition, radiomic margin characteristics were also correlated with the micropapillary subtype, and the sphericity value was able to predict the presence of the micropapillary subtype. In conclusion, our study showed that radiomic margin characteristics helped predict overall survival in patients with lung adenocarcinomas, thus implying that the tumor margin contains prognostic information.

Prediction of drug-target binding affinity using similarity-based convolutional neural network.

Identifying novel drug-target interactions (DTIs) plays an important role in drug discovery. Most of the computational methods developed for predicting DTIs use binary classification, whose goal is to determine whether or not a drug-target (DT) pair interacts. However, it is more meaningful but also more challenging to predict the binding affinity that describes the strength of the interaction between a DT pair. If the binding affinity is not sufficiently large, such drug may not be useful. Therefore, the methods for predicting DT binding affinities are very valuable. The increase in novel public affinity data available in the DT-related databases enables advanced deep learning techniques to be used to predict binding affinities. In this paper, we propose a similarity-based model that applies 2-dimensional (2D) convolutional neural network (CNN) to the outer products between column vectors of two similarity matrices for the drugs and targets to predict DT binding affinities. To our best knowledge, this is the first application of 2D CNN in similarity-based DT binding affinity prediction. The validation results on multiple public datasets show that the proposed model is an effective approach for DT binding affinity prediction and can be quite helpful in drug development process.

Eradication of Helicobacter pylori infection decreases risk for dyslipidemia: A cohort study.

BACKGROUND: Previous studies have suggested a relationship between Helicobacter pylori infection and dyslipidemia; however, large-scale longitudinal studies have not elucidated this association. This study assessed the longitudinal effects of H. pylori infection and eradication on lipid profiles in a large cohort. METHODS: This cohort study included 2,626 adults without dyslipidemia at baseline, who participated in a repeated, regular health-screening examination, which included upper gastrointestinal endoscopy, between January 2009 and December 2018. The primary outcome was incident dyslipidemia at follow-up. RESULTS: During the 10,324 person-years of follow-up, participants with persistent H. pylori infection had a higher incidence rate (130.5 per 1,000 person-years) of dyslipidemia than those whose infections had been successfully controlled (98.1 per 1,000 person-years). In a multivariable model adjusted for age, sex, waist circumference, smoking status, alcohol intake, and education level, the H. pylori eradication group was associated with a lower risk of dyslipidemia than the persistent group (HR, 0.85; 95% CI, 0.77-0.95; p = 0.004). The association persisted after further adjustment for baseline levels of low-density and high-density lipoprotein cholesterol (HR, 0.87; 95% CI, 0.79-0.97; p = 0.014). CONCLUSIONS: H. pylori infection may play a pathophysiologic role in the development of dyslipidemia, whereas H. pylori eradication might decrease the risk of dyslipidemia.

Prognostic significance of sarcopenia in microsatellite-stable gastric cancer patients treated with programmed death-1 inhibitors.

BACKGROUND: Sarcopenia has been underscored as a significant predictor of poor prognosis in cancer patients undergoing immunotherapy with programmed death-1 (PD-1) inhibitors. We aimed to investigate the prognostic significance of computed tomography (CT)-determined sarcopenia in patients with microsatellite-stable (MSS) gastric cancer (GC) treated with PD-1 inhibitors. METHODS: We retrospectively assessed patients with MSS GC who had been treated with PD-1 inhibitors from March 2016 to June 2019. Pre-treatment sarcopenic status was determined by analyzing L3 skeletal muscle index with abdominal CT. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method, and the differences in survival probability according to sarcopenic status were compared using the log-rank test. Cox proportional hazards regression analyses were performed to identify predictors of PFS and OS. RESULTS: Of 149 patients with MSS GC (mean age, 57.0 ± 12.3 years; 93 men), 79 (53.0%) had sarcopenia. Patients with sarcopenia had significantly shorter PFS than patients without sarcopenia (median, 1.4 months vs. 2.6 months; P = 0.026). Sarcopenia was independently associated with shorter PFS (adjusted hazard ratio [HR], 1.79; 95% confidence interval [CI], 1.10-2.93; P = 0.020). Patients with sarcopenia had shorter OS than patients without sarcopenia (median, 3.6 months vs. 4.9 months; P = 0.052), but sarcopenia itself was not a significant prognostic factor for OS (adjusted HR, 1.01; 95% CI, 0.58-1.75; P = 0.974). CONCLUSIONS: CT-determined sarcopenia is an independent prognostic factor for PFS in patients with MSS GC treated with PD-1 inhibitors.

Computed Tomography Radiomics for Residual Positron Emission Tomography-Computed Tomography Uptake in Lymph Nodes after Treatment.

Although a substantial decrease in 2-[fluorine-18]fluoro-2-deoxy-d-glucose (FDG) uptake on positron emission tomography-computed tomography (PET-CT) indicates a promising metabolic response to treatment, predicting the pathologic status of lymph nodes (LN) remains challenging. We investigated the potential of a CT radiomics approach to predict the pathologic complete response of LNs showing residual uptake after neoadjuvant concurrent chemoradiotherapy (NeoCCRT) in patients with non-small cell lung cancer (NSCLC). Two hundred and thirty-seven patients who underwent NeoCCRT for stage IIIa NSCLC were included. Two hundred fifty-two CT radiomics features were extracted from LNs showing remaining positive FDG uptake upon restaging PET-CT. A multivariable logistic regression analysis of radiomics features and clinicopathologic characteristics was used to develop a prediction model. Of the 237 patients, 135 patients (185 nodes) met our inclusion criteria. Eighty-seven LNs were proven to be malignant (47.0%, 87/185). Upon multivariable analysis, metastatic LNs were significantly prevalent in females and patients with adenocarcinoma (odds ratio (OR) = 2.02, 95% confidence interval (CI) = 0.88-4.62 and OR = 0.39, 95% CI = 0.19-0.77 each). Metastatic LNs also had a larger maximal 3D diameter and higher cluster tendency (OR = 9.92, 95% CI = 3.15-31.17 and OR = 2.36, 95% CI = 1.22-4.55 each). The predictive model for metastasis showed a discrimination performance with an area under the receiver operating characteristic curve of 0.728 (95% CI = 0.654-0.801, p value < 0.001). The radiomics approach allows for the noninvasive detection of metastases in LNs with residual FDG uptake after the treatment of NSCLC patients.

Deep learning method for comet segmentation and comet assay image analysis.

Comet assay is a widely used method, especially in the field of genotoxicity, to quantify and measure DNA damage visually at the level of individual cells with high sensitivity and efficiency. Generally, computer programs are used to analyze comet assay output images following two main steps. First, each comet region must be located and segmented, and next, it is scored using common metrics (e.g., tail length and tail moment). Currently, most studies on comet assay image analysis have adopted hand-crafted features rather than the recent and effective deep learning (DL) methods. In this paper, however, we propose a DL-based baseline method, called DeepComet, for comet segmentation. Furthermore, we created a trainable and testable comet assay image dataset that contains 1037 comet assay images with 8271 manually annotated comet objects. From the comet segmentation test results with the proposed dataset, the DeepComet achieves high average precision (AP), which is an essential metric in image segmentation and detection tasks. A comparative analysis was performed between the DeepComet and the state-of-the-arts automatic comet segmentation programs on the dataset. Besides, we found that the DeepComet records high correlations with a commercial comet analysis tool, which suggests that the DeepComet is suitable for practical application.

Clinical T category for lung cancer staging: A pragmatic approach for real-world practice.

BACKGROUND: To determine which components should be measured and which window settings are appropriate for computerized tomography (CT) size measurements of lung adenocarcinoma (ADC) and to explore interobserver agreement and accuracy according to the eighth edition of TNM staging. METHODS: A total of 165 patients with surgically resected lung ADC earlier than stage 3A were included in this study. One radiologist and two pulmonologists independently measured the total and solid sizes of components of tumors on different window settings and assessed solidity. CT measurements were compared with pathologic size measurements. RESULTS: In categorizing solidity, 25% of the cases showed discordant results among observers. Measuring the total size of a lung adenocarcinoma predicted pathologic invasive components to a degree similar to measuring the solid component. Lung windows were more accurate (intraclass correlation [ICC] = 0.65-0.81) than mediastinal windows (ICC = 0.20-0.72) at predicting pathologic invasive components, especially in a part-solid nodule. Interobserver agreements for measurement of solid components were good with little significant difference (lung windows, ICC = 0.89; mediastinal windows, ICC = 0.91). A high level of interobserver agreement was seen between the radiologist and pulmonologists and between residents (from the division of pulmonology and critical care) versus a fellow (from the division of pulmonology and critical care) on different windows. CONCLUSIONS: A considerable percentage (25%) of discrepancies was encountered in categorizing the solidity of lesions, which may decrease the accuracy of measurements. Lung window settings may be superior to mediastinal windows for measuring lung ADCs, with comparable interobserver agreement and moderate accuracy for predicting pathologic invasive components. KEY POINTS: SIGNIFICANT FINDINGS OF THE STUDY: Lung window settings are better for evaluating part-solid lung adenocarcinoma (ADC), with comparable interobserver agreement and moderate accuracy for predicting pathologic invasive components. The considerable percentage (25%) of discrepancies in categorizing solidity of the lesions may also have decreased the accuracy of measurements. WHAT THIS STUDY ADDS: For accurate measurement and categorization of lung ADC, robust quantitative analysis is needed rather than a simple visual assessment.

Outcomes of Inhaled Amikacin and Clofazimine-Containing Regimens for Treatment of Refractory Mycobacterium avium Complex Pulmonary Disease.

Limited data are available regarding optimal treatment for refractory Mycobacterium avium complex-pulmonary disease (MAC-PD). We evaluated outcomes of inhaled amikacin (AMK) with clofazimine (CFZ) regimens as an add-on salvage therapy for refractory MAC-PD. We retrospectively analyzed 52 patients with refractory MAC-PD, characterized by persistently positive sputum cultures despite >6 months of treatment. Thirty-five (67%) patients had M. intracellulare-PD, and 17 (33%) patients had M. avium-PD. Twenty-seven (52%) patients received the salvage therapy for ≥12 months, whereas 25 (48%) patients were treated for <12 months due to adverse effects or other reasons. Seventeen (33%) patients had culture conversion: 10 (10/27) in the ≥12-month treatment group and seven (7/25) in the <12-month treatment group (p = 0.488). Microbiological cure, defined as maintenance of culture negativity, was achieved in 12 (23%) patients; six (6/12) with accompanying symptomatic improvement were considered to have reached cure. Clinical cure, defined as symptomatic improvement with <3 consecutive negative cultures, was achieved in three (6%) patients. Overall, 15 (29%) patients achieved favorable outcomes, including microbiological cure, cure, and clinical cure. Inhaled AMK with CFZ may provide favorable outcomes in some patients with refractory MAC-PD. However, given the adverse effects, more effective strategies are needed to maintain these therapeutic regimens.

Spontaneous mutations in the single TTN gene represent high tumor mutation burden.

Tumor mutation burden (TMB) is an emerging biomarker, whose calculation requires targeted sequencing of many genes. We investigated if the measurement of mutation counts within a single gene is representative of TMB. Whole-exome sequencing (WES) data from the pan-cancer cohort (n = 10,224) of TCGA, and targeted sequencing (tNGS) and TTN gene sequencing from 24 colorectal cancer samples (AMC cohort) were analyzed. TTN was identified as the most frequently mutated gene within the pan-cancer cohort, and its mutation number best correlated with TMB assessed by WES (rho = 0.917, p < 2.2e-16). Colorectal cancer was one of good candidates for the application of this diagnostic model of TTN-TMB, and the correlation coefficients were 0.936 and 0.92 for TMB by WES and TMB by tNGS, respectively. Higher than expected TTN mutation frequencies observed in other FLAGS (FrequentLy mutAted GeneS) are associated with late replication time. Diagnostic accuracy for high TMB group did not differ between TTN-TMB and TMB assessed by tNGS. Classification modeling by machine learning using TTN-TMB for MSI-H diagnosis was constructed, and the diagnostic accuracy was 0.873 by area under the curve in external validation. TTN mutation was enriched in samples possessing high immunostimulatory signatures. We suggest that the mutation load within TTN represents high TMB status.

Prognostication of a 13-immune-related-gene signature in patients with early triple-negative breast cancer.

PURPOSE: We investigated the expression profiles of immune genes in patients with triple-negative breast cancer (TNBC) to identify the prognostic value of immune genes and their clinical implications. METHODS: NanoString nCounter Analysis of 770 immune-related genes was used to measure immune gene expression in patients with TNBC who underwent curative surgery followed by adjuvant chemotherapy at Samsung Medical Center between 2000 and 2004. Statistical analyses were conducted to identify the associations between gene expression and distant recurrence-free survival (DRFS). RESULTS: Of 1189 patients who underwent curative BC surgery, 200 TNBC patients were included and stage was the only clinical factor predictive of DRFS. In terms of immune genes, 155 of 770 genes were associated with DRFS (p < 0.01). Further multivariate analysis revealed that 13 genes, CD1B, CD53, CT45A1, GTF3C1, IL11RA, IL1RN, LRRN3, MAPK1, NEFL, PRKCE, PTPRC, SPACA3 and TNFSF11, were associated with patient prognosis (p < 0.05). The prognostic value of stage and expression levels of 13 immune genes was analyzed and the area under the receiver operating characteristic curve (AUC) was 0.923. Based on the AUC, we divided patients into three genetic risk groups and DRFS rate was significantly different according to genetic risk groups, even in the same stage (p < 0.001). CONCLUSIONS: In this study, a 13-gene expression profile in combination with stage precisely predicted distant recurrence of early TNBC. Therefore, this 13-immune-gene signature could help predict TNBC prognosis and provide guidance for treatment as well as the opportunity to develop new targets for immunotherapy in TNBC patients.

Parallel comparison and combining effect of radiomic and emerging genomic data for prognostic stratification of non-small cell lung carcinoma patients.

BACKGROUND: A single institution retrospective analysis of 124 non-small cell lung carcinoma (NSCLC) patients was performed to identify whether disease-free survival (DFS) achieves incremental values when radiomic and genomic data are combined with clinical information. METHODS: Using the least absolute shrinkage and selection operator (LASSO) Cox regression method, radiomic and genetic features were reduced in number for selection of the most useful prognostic feature. We created four models using only baseline clinical data, clinical data with selected genetic features, clinical data with selected radiomic features, and clinical data with selected genetic and radiomic features together. Multivariate Cox proportional hazards analysis was performed to determine predictors of DFS. Receiver operating characteristic (ROC) calculation was made to compare the discriminative performance for DFS prediction by four constructed models at the five-year time point. RESULTS: On precontrast scan, improved discrimination performance was obtained in a merging of selected radiomics and genetics (AUC = 0.8638), compared with clinical data only (AUC = 0.7990), selected genetic features (AUC = 0.8497), and selected radiomic features (AUC = 0.8355). On post-contrast scan, discrimination performance was improved (AUC = 0.8672) compared with the clinical variables (AUC = 0.7913), and selected genetic features (AUC = 0.8376) and selected radiomic features (AUC = 0.8399) were considered. CONCLUSIONS: The combination of selected radiomic and genomic features improved stratification of NSCLC patients upon survival. Thus, integrating clinicopathologic model with radiomic and genomic features may lead to improved prognostic accuracy compared to conventional clinicopathological data alone. KEY POINTS: SIGNIFICANT FINDINGS OF THE STUDY: Receiver operating characteristic (ROC) calculation was made to compare the discriminative performance for disease-free survival (DFS). The discriminative performance for DFS was better when combining radiomic and genetic features compared to clinical data only, selected genetic features, and selected radiomic features. WHAT THIS STUDY ADDS: The combination of selected radiomic and genomic features improved stratification of NSCLC patients upon survival. Thus, integrating a clinicopathological model with radiomic and genomic features may lead to improved prognostic accuracy compared to conventional clinicopathological data alone.

Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics.

BACKGROUND: Because shape or irregularity along the tumor perimeter can result from interactions between the tumor and the surrounding parenchyma, there could be a difference in tumor growth rate according to tumor margin or shape. However, no attempt has been made to evaluate the correlation between margin or shape features and tumor growth. METHODS: We evaluated 52 lung adenocarcinoma (ADC) patients who had at least two computed tomographic (CT) examinations before curative resection. Volume-based doubling times (DTs) were calculated based on CT scans, and patients were divided into two groups according to the growth pattern (GP) of their ADCs (gradually growing tumors [GP I] vs. growing tumors with a temporary decrease in DT [GP II]). CT radiomic features reflecting margin characteristics were extracted, and radiomic features reflective of tumor DT were selected. RESULTS: Among the 52 patients, 41 (78.8%) were assigned to GP I and 11 (21.2%) to GP II. Of the 94 radiomic features extracted, eccentricity, surface-to-volume ratio, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5) were ultimately selected for tumor DT prediction. Selected radiomic features in GP I were surface-to-volume ratio, contrast, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5), similar to those for total subjects, whereas the radiomic features in GP II were solidity, energy, and busyness. CONCLUSIONS: This study demonstrated the potential of margin-related radiomic features to predict tumor DT in lung ADCs. KEY POINTS: SIGNIFICANT FINDINGS OF THE STUDY: We found a relationship between margin-related radiomic features and tumor doubling time. WHAT THIS STUDY ADDS: Margin-related radiomic features can potentially be used as noninvasive biomarkers to predict tumor doubling time in lung adenocarcinoma and inform treatment strategies.