Imaging in pleural Mesothelioma: A review of the 16th International Conference of the International Mesothelioma Interest Group.

Imaging continues to gain a greater role in the assessment and clinical management of patients with mesothelioma. This communication summarizes the oral presentations from the imaging session at the 2023 International Conference of the International Mesothelioma Interest Group (iMig), which was held in Lille, France from June 26 to 28, 2023. Topics at this session included an overview of best practices for clinical imaging of mesothelioma as reported by an iMig consensus panel, emerging imaging techniques for surgical planning, radiologic assessment of malignant pleural effusion, a radiomics-based transfer learning model to predict patient response to treatment, automated assessment of early contrast enhancement, and tumor thickness for response assessment in peritoneal mesothelioma.

State of the art: radiomics and radiomics-related artificial intelligence on the road to clinical translation.

Radiomics and artificial intelligence carry the promise of increased precision in oncologic imaging assessments due to the ability of harnessing thousands of occult digital imaging features embedded in conventional medical imaging data. While powerful, these technologies suffer from a number of sources of variability that currently impede clinical translation. In order to overcome this impediment, there is a need to control for these sources of variability through harmonization of imaging data acquisition across institutions, construction of standardized imaging protocols that maximize the acquisition of these features, harmonization of post-processing techniques, and big data resources to properly power studies for hypothesis testing. For this to be accomplished, it will be critical to have multidisciplinary and multi-institutional collaboration.

Cardiovascular Substructure Dose and Cardiac Events following Proton- and Photon-Based Chemoradiotherapy for Non-Small Cell Lung Cancer.

Purpose: Radiation therapy (RT) plays a critical role in treating locally advanced non-small cell lung cancer but has been associated with deleterious cardiac effects. We hypothesized that RT dose to certain cardiovascular substructures may be higher among those who experience post-chemoradiation (CRT) cardiac events, and that dose to specific substructures-the great vessels, atria, ventricles, and left anterior descending coronary artery-may be lower with proton- versus photon-based RT. Methods and Materials: In this retrospective review, we selected 26 patients who experienced cardiac events after CRT for locally advanced non-small cell lung cancer and matched them to 26 patients who did not experience cardiac events after CRT. Matching was done based on RT technique (protons vs photons), age, sex, and cardiovascular comorbidity. For each patient, the whole heart and 10 cardiovascular substructures on the RT planning computerized tomography scan were manually contoured. Dosimetric comparisons were made between those who did and did not experience cardiac events and between the proton and photon groups. Results: There was no significant difference in heart or any cardiovascular substructure dose between those patients who experienced post-treatment cardiac events and those who did not (P > .05 for all). The mean heart dose in the patients receiving proton therapy was significantly lower than the mean heart dose in the patients receiving photon therapy (P = .032). The left ventricle, right ventricle, and the left anterior descending artery also had significantly lower doses (by multiple measures) when treated with protons (P = .0004, P < .0001, and P = .0002, respectively). Conclusions: Proton therapy may have a significant effect on decreasing dose to individual cardiovascular substructures compared with photon therapy. There was no significant difference in heart dose or dose to any cardiovascular substructure between patients who did and did not experience post-treatment cardiac events. Further research should be done to assess the association between cardiovascular substructure dose and post-treatment cardiac events.

KLF5 and p53 comprise an incoherent feed-forward loop directing cell-fate decisions following stress.

In response to stress, cells make a critical decision to arrest or undergo apoptosis, mediated in large part by the tumor suppressor p53. Yet the mechanisms of these cell fate decisions remain largely unknown, particularly in normal cells. Here, we define an incoherent feed-forward loop in non-transformed human squamous epithelial cells involving p53 and the zinc-finger transcription factor KLF5 that dictates responses to differing levels of cellular stress from UV irradiation or oxidative stress. In normal unstressed human squamous epithelial cells, KLF5 complexes with SIN3A and HDAC2 repress TP53, allowing cells to proliferate. With moderate stress, this complex is disrupted, and TP53 is induced; KLF5 then acts as a molecular switch for p53 function by transactivating AKT1 and AKT3, which direct cells toward survival. By contrast, severe stress results in KLF5 loss, such that AKT1 and AKT3 are not induced, and cells preferentially undergo apoptosis. Thus, in human squamous epithelial cells, KLF5 gates the response to UV or oxidative stress to determine the p53 output of growth arrest or apoptosis.

Clinical Outcomes Associated With Pembrolizumab Monotherapy Among Adults With Diffuse Malignant Peritoneal Mesothelioma.

Importance: Diffuse malignant peritoneal mesothelioma (DMPM) represents a rare and clinically distinct entity among malignant mesotheliomas. Pembrolizumab has activity in diffuse pleural mesothelioma but limited data are available for DMPM; thus, DMPM-specific outcome data are needed. Objective: To evaluate outcomes after the initiation of pembrolizumab monotherapy in the treatment of adults with DMPM. Design, Setting, and Participants: This retrospective cohort study was conducted in 2 tertiary care academic cancer centers (University of Pennsylvania Hospital Abramson Cancer Center and Memorial Sloan Kettering Cancer Center). All patients with DMPM treated between January 1, 2015, and September 1, 2019, were retrospectively identified and followed until January 1, 2021. Statistical analysis was performed between September 2021 and February 2022. Exposures: Pembrolizumab (200 mg or 2 mg/kg every 21 days). Main Outcomes and Measures: Median progression-free survival (PFS) and median overall survival (OS) were assessed using Kaplan-Meier estimates. The best overall response was determined using RECIST (Response Evaluation Criteria in Solid Tumors) criteria, version 1.1. The association of disease characteristics with partial response was evaluated using the Fisher exact test. Results: This study included 24 patients with DMPM who received pembrolizumab monotherapy. Patients had a median age of 62 years (IQR, 52.4-70.6 years); 14 (58.3%) were women, 18 (75.0%) had epithelioid histology, and most (19 [79.2%]) were White. A total of 23 patients (95.8%) received systemic chemotherapy prior to pembrolizumab, and the median number of lines of prior therapy was 2 (range, 0-6 lines). Of the 17 patients who underwent programmed death ligand 1 (PD-L1) testing, 6 (35.3%) had positive tumor PD-L1 expression (range, 1.0%-80.0%). Of the 19 evaluable patients, 4 (21.0%) had a partial response (overall response rate, 21.1% [95% CI, 6.1%-46.6%]), 10 (52.6%) had stable disease, and 5 (26.3%) had progressive disease (5 of 24 patients [20.8%] were lost to follow-up). There was no association between a partial response and the presence of a BAP1 alteration, PD-L1 positivity, or nonepithelioid histology. With a median follow-up of 29.2 (95% CI, 19.3 to not available [NA]) months, the median PFS was 4.9 (95% CI, 2.8-13.3) months and the median OS was 20.9 (95% CI, 10.0 to NA) months from pembrolizumab initiation. Three patients (12.5%) experienced PFS of more than 2 years. Among patients with nonepithelioid vs epithelioid histology, there was a numeric advantage in median PFS (11.5 [95% CI, 2.8 to NA] vs 4.0 [95% CI, 2.8-8.8] months) and median OS (31.8 [95% CI, 8.3 to NA] vs 17.5 [95% CI, 10.0 to NA] months); however, this did not reach statistical significance. Conclusions and Relevance: The results of this retrospective dual-center cohort study of patients with DMPM suggest that pembrolizumab had clinical activity regardless of PD-L1 status or histology, although patients with nonepithelioid histology may have experienced additional clinical benefit. The partial response rate of 21.0% and median OS of 20.9 months in this cohort with 75.0% epithelioid histology warrants further investigation to identify those most likely to respond to immunotherapy.

Considerations for Imaging of Malignant Pleural Mesothelioma: A Consensus Statement from the International Mesothelioma Interest Group.

Malignant pleural mesothelioma (MPM) is an aggressive primary malignancy of the pleura that presents unique radiologic challenges with regard to accurate and reproducible assessment of disease extent at staging and follow-up imaging. By optimizing and harmonizing technical approaches to imaging MPM, the best quality imaging can be achieved for individual patient care, clinical trials, and imaging research. This consensus statement represents agreement on harmonized, standard practices for routine multimodality imaging of MPM, including radiography, computed tomography, 18F-2-deoxy-D-glucose positron emission tomography, and magnetic resonance imaging, by an international panel of experts in the field of pleural imaging assembled by the International Mesothelioma Interest Group. In addition, modality-specific technical considerations and future directions are discussed. A bulleted summary of all technical recommendations is provided.

Resampling and harmonization for mitigation of heterogeneity in image parameters of baseline scans.

Our study investigates the effects of heterogeneity in image parameters on the reproducibility of prognostic performance of models built using radiomic biomarkers. We compare the prognostic performance of models derived from the heterogeneity-mitigated features with that of models obtained from raw features, to assess whether reproducibility of prognostic scores improves upon application of our methods. We used two datasets: The Breast I-SPY1 dataset-Baseline DCE-MRI scans of 156 women with locally advanced breast cancer, treated with neoadjuvant chemotherapy, publicly available via The Cancer Imaging Archive (TCIA); The NSCLC IO dataset-Baseline CT scans of 107 patients with stage 4 non-small cell lung cancer (NSCLC), treated with pembrolizumab immunotherapy at our institution. Radiomic features (n = 102) are extracted from the tumor ROIs. We use a variety of resampling and harmonization scenarios to mitigate the heterogeneity in image parameters. The patients were divided into groups based on batch variables. For each group, the radiomic phenotypes are combined with the clinical covariates into a prognostic model. The performance of the groups is assessed using the c-statistic, derived from a Cox proportional hazards model fitted on all patients within a group. The heterogeneity-mitigation scenario (radiomic features, derived from images that have been resampled to minimum voxel spacing, are harmonized using the image acquisition parameters as batch variables) gave models with highest prognostic scores (for e.g., IO dataset; batch variable: high kernel resolution-c-score: 0.66). The prognostic performance of patient groups is not comparable in case of models built using non-heterogeneity mitigated features (for e.g., I-SPY1 dataset; batch variable: small pixel spacing-c-score: 0.54, large pixel spacing-c-score: 0.65). The prognostic performance of patient groups is closer in case of heterogeneity-mitigated scenarios (for e.g., scenario-harmonize by voxel spacing parameters: IO dataset; thin slice-c-score: 0.62, thick slice-c-score: 0.60). Our results indicate that accounting for heterogeneity in image parameters is important to obtain more reproducible prognostic scores, irrespective of image site or modality. For non-heterogeneity mitigated models, the prognostic scores are not comparable across patient groups divided based on batch variables. This study can be a step in the direction of constructing reproducible radiomic biomarkers, thus increasing their application in clinical decision making.

Improved generalized ComBat methods for harmonization of radiomic features.

Radiomic approaches in precision medicine are promising, but variation associated with image acquisition factors can result in severe biases and low generalizability. Multicenter datasets used in these studies are often heterogeneous in multiple imaging parameters and/or have missing information, resulting in multimodal radiomic feature distributions. ComBat is a promising harmonization tool, but it only harmonizes by single/known variables and assumes standardized input data are normally distributed. We propose a procedure that sequentially harmonizes for multiple batch effects in an optimized order, called OPNested ComBat. Furthermore, we propose to address bimodality by employing a Gaussian Mixture Model (GMM) grouping considered as either a batch variable (OPNested + GMM) or as a protected clinical covariate (OPNested - GMM). Methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography (CT) datasets. We found that OPNested ComBat improved harmonization performance over standard ComBat. OPNested + GMM ComBat exhibited the best harmonization performance but the lowest predictive performance, while OPNested - GMM ComBat showed poorer harmonization performance, but the highest predictive performance. Our findings emphasize that improved harmonization performance is no guarantee of improved predictive performance, and that these methods show promise for superior standardization of datasets heterogeneous in multiple or unknown imaging parameters and greater generalizability.

A single-center retrospective cohort study of perioperative systemic chemotherapy in diffuse malignant peritoneal mesothelioma.

BACKGROUND: Diffuse malignant peritoneal mesothelioma (DMPM) is a rare variant of malignant mesothelioma, representing 10-15% of malignant mesothelioma cases. The preferred therapeutic approach is cytoreductive surgery (CRS) accompanied by hyperthermic intraperitoneal chemotherapy (HIPEC); the role of systemic chemotherapy is not well established. While some limited retrospective studies report worse outcomes with neoadjuvant chemotherapy, our institution has favored the use of neoadjuvant chemotherapy for symptom relief and surgical optimization. The aim of our study was to assess the outcomes of patients receiving neoadjuvant chemotherapy, compared to those receiving adjuvant or no perioperative chemotherapy. PATIENTS AND METHODS: We conducted a single-center retrospective cohort study of treatment-naïve, non-papillary DMPM patients seen at our institution between 1/1/2009 and 9/1/2019. We explored the effect of type of systemic therapy on clinical outcomes and estimated median overall survival (mOS) using Kaplan-Meier curves. Hazard ratios (HR) calculated by Cox proportional hazard model were used to estimate effect of the exposures on overall survival. RESULTS: 47 patients were identified with DMPM (median age at diagnosis 61.2 years, 76.6% epithelioid histology, 74.5% white race, 55.3% known asbestos exposure). CRS was performed in 53.2% of patients (25/47); 76.0% of surgical patients received HIPEC (19/25). The majority received systemic chemotherapy (37/47, 78.7%); among patients receiving both CRS and chemotherapy, neoadjuvant chemotherapy was more common than adjuvant chemotherapy (12 neoadjuvant, 8 adjuvant). Overall mOS was 84.1 months. Among neoadjuvant patients, 10/12 underwent surgery, and 2 were lost to follow-up; the majority (9/10) had clinically stable or improved disease during the pre-operative period. There were numerical more issues with chemotherapy with the adjuvant patients (4/8: 2 switches in platinum agent, 2 patients stopped therapy) than with the neoadjuvant patients (2/10: 1 switch in platinum agent, 1 delay due to peri-procedural symptoms). Neoadjuvant chemotherapy was not associated with worse mOS compared to adjuvant chemotherapy (mOS NR vs 95.1 mo, HR 0.89, 95% CI 0.18-4.5, p = 0.89). CONCLUSIONS: When used preferentially, the use of neoadjuvant chemotherapy in DMPM patients was not associated with worse outcomes compared to adjuvant chemotherapy. It was well-tolerated and did not prevent surgical intervention.

Development of a robust radiomic biomarker of progression-free survival in advanced non-small cell lung cancer patients treated with first-line immunotherapy.

We aim to determine the feasibility of a novel radiomic biomarker that can integrate with other established clinical prognostic factors to predict progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) undergoing first-line immunotherapy. Our study includes 107 patients with stage 4 NSCLC treated with pembrolizumab-based therapy (monotherapy: 30%, combination chemotherapy: 70%). The ITK-SNAP software was used for 3D tumor volume segmentation from pre-therapy CT scans. Radiomic features (n = 102) were extracted using the CaPTk software. Impact of heterogeneity introduced by image physical dimensions (voxel spacing parameters) and acquisition parameters (contrast enhancement and CT reconstruction kernel) was mitigated by resampling the images to the minimum voxel spacing parameters and harmonization by a nested ComBat technique. This technique was initialized with radiomic features, clinical factors of age, sex, race, PD-L1 expression, ECOG status, body mass index (BMI), smoking status, recurrence event and months of progression-free survival, and image acquisition parameters as batch variables. Two phenotypes were identified using unsupervised hierarchical clustering of harmonized features. Prognostic factors, including PDL1 expression, ECOG status, BMI and smoking status, were combined with radiomic phenotypes in Cox regression models of PFS and Kaplan Meier (KM) curve-fitting. Cox model based on clinical factors had a c-statistic of 0.57, which increased to 0.63 upon addition of phenotypes derived from harmonized features. There were statistically significant differences in survival outcomes stratified by clinical covariates, as measured by the log-rank test (p = 0.034), which improved upon addition of phenotypes (p = 0.00022). We found that mitigation of heterogeneity by image resampling and nested ComBat harmonization improves prognostic value of phenotypes, resulting in better prediction of PFS when added to other prognostic variables.

CT Features of Lymphatic Plastic Bronchitis in Adults: Correlation with Multimodality Lymphatic Imaging.

Purpose: To distinguish CT patterns of lymphatic and nonlymphatic causes of plastic bronchitis (PB) through comparison with lymphatic imaging. Materials and Methods: In this retrospective study, chest CT images acquired prior to lymphatic workup were assessed in 44 patients with PB from January 2014 to August 2020. The location and extent of ground-glass opacity (GGO) was compared with symptoms and lymphatic imaging. Statistical analysis was performed using descriptive statistics, logistic regression, Pearson correlation coefficient, and unweighted κ coefficient for interobserver agreement. Sensitivity and specificity of GGO for lymphatic PB were calculated. Results: Lymphatic imaging was performed in 44 patients (median age, 52 years ± 21 [IQR]; 23 women): 35 with lymphatic PB and nine with nonlymphatic PB. GGO was more frequently observed in patients with lymphatic PB than in those with nonlymphatic PB (91% [32 of 35] vs 33% [three of nine]; P < .001). Univariate logistic regression confirmed this result by showing that GGO was a significant predictor of lymphatic PB (odds ratio, 21 (95% CI: 3.8, 159.7). The model areas under the receiver operating characteristic curve (AUCs) of GGO unadjusted and adjusted for demographics were 0.79 and 0.86, respectively. The location of GGO correlated with lymphatic imaging and bronchoscopic findings. Overall sensitivity and specificity of GGO for lymphatic PB were 91% (32 of 35; 95% CI: 76, 98) and 67% (six of nine; 95% CI: 30, 93), respectively. Conclusion: Patients with lymphatic PB predominantly had multifocal GGO with or without a "crazy paving" pattern; identification of GGO should prompt lymphatic workup in this frequently misdiagnosed condition.Keywords: Lymphography, Lymphatic, CT, Tracheobronchial Tree, Thorax© RSNA, 2022See also commentary by Kligerman and White in this issue.

Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects.

Radiomic features have a wide range of clinical applications, but variability due to image acquisition factors can affect their performance. The harmonization tool ComBat is a promising solution but is limited by inability to harmonize multimodal distributions, unknown imaging parameters, and multiple imaging parameters. In this study, we propose two methods for addressing these limitations. We propose a sequential method that allows for harmonization of radiomic features by multiple imaging parameters (Nested ComBat). We also employ a Gaussian Mixture Model (GMM)-based method (GMM ComBat) where scans are split into groupings based on the shape of the distribution used for harmonization as a batch effect and subsequent harmonization by a known imaging parameter. These two methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography datasets. We found that Nested ComBat exhibited similar performance to standard ComBat in reducing the percentage of features with statistically significant differences in distribution attributable to imaging parameters. GMM ComBat improved harmonization performance over standard ComBat (- 11%, - 10% for Lung3/CAPTK, Lung3/PyRadiomics harmonizing by kernel resolution). Features harmonized with a variant of the Nested method and the GMM split method demonstrated similar c-statistics and Kaplan-Meier curves when used in survival analyses.

Radiomic Phenotypes for Improving Early Prediction of Survival in Stage III Non-Small Cell Lung Cancer Adenocarcinoma after Chemoradiation.

We evaluate radiomic phenotypes derived from CT scans as early predictors of overall survival (OS) after chemoradiation in stage III primary lung adenocarcinoma. We retrospectively analyzed 110 thoracic CT scans acquired between April 2012-October 2018. Patients received a median radiation dose of 66.6 Gy at 1.8 Gy/fraction delivered with proton (55.5%) and photon (44.5%) beam treatment, as well as concurrent chemotherapy (89%) with carboplatin-based (55.5%) and cisplatin-based (36.4%) doublets. A total of 56 death events were recorded. Using manual tumor segmentations, 107 radiomic features were extracted. Feature harmonization using ComBat was performed to mitigate image heterogeneity due to the presence or lack of intravenous contrast material and variability in CT scanner vendors. A binary radiomic phenotype to predict OS was derived through the unsupervised hierarchical clustering of the first principal components explaining 85% of the variance of the radiomic features. C-scores and likelihood ratio tests (LRT) were used to compare the performance of a baseline Cox model based on ECOG status and age, with a model integrating the radiomic phenotype with such clinical predictors. The model integrating the radiomic phenotype (C-score = 0.69, 95% CI = (0.62, 0.77)) significantly improved (p<0.005) upon the baseline model (C-score = 0.65, CI = (0.57, 0.73)). Our results suggest that harmonized radiomic phenotypes can significantly improve OS prediction in stage III NSCLC after chemoradiation.

Imaging in pleural mesothelioma: A review of the 15th International Conference of the International Mesothelioma Interest Group.

Imaging of mesothelioma plays a role in all aspects of patient management, including disease detection, staging, evaluation of treatment options, response assessment, pre-surgical evaluation, and surveillance. Imaging in this disease impacts a wide range of disciplines throughout the healthcare enterprise. Researchers and clinician-scientists are developing state-of-the-art techniques to extract more of the information contained within these medical images and to utilize it for more sophisticated tasks; moreover, image-acquisition technology is advancing the inherent capabilities of these images. This paper summarizes the imaging-based topics presented orally at the 2021 International Conference of the International Mesothelioma Interest Group (iMig), which was held virtually from May 7-9, 2021. These topics include an update on the mesothelioma staging system, novel molecular targets to guide therapy in mesothelioma, special considerations and potential pitfalls in imaging mesothelioma in the immunotherapy setting, tumor measurement strategies and their correlation with patient survival, tumor volume measurement in MRI and CT, CT-based texture analysis for differentiation of histologic subtype, diffusion-weighted MRI for the assessment of biphasic mesothelioma, and the prognostic significance of skeletal muscle loss with chemotherapy.

Comparative Evaluation of Choose Your Own Adventure and Traditional Linear Case Formats in Radiology Small Group Teaching.

RATIONALE AND OBJECTIVES: We aim to compare Choose Your Own Adventure (CYOA) presentation format with linear case format as educational methods for teaching a radiology small group session to medical students. MATERIALS AND METHODS: A radiology small group session was held for preclinical second-year medical students in the pulmonary course, whereby eight classrooms of students and eight radiology facilitators were each randomized to do either the linear case format or the nonlinear CYOA presentation format. All students in attendance were administered a survey at the end of the session, which assessed students' perceptions using five-point Likert-type questions. The survey also contained a four-question knowledge quiz on chest radiology. The facilitators were administered a qualitative survey as well. Between-group analyses were performed using Student's t-test. RESULTS: Of the 144 students who attended the small group sessions, 143 students completed the survey (99.3%). The CYOA format group reported significantly greater engagement in the cases (4.5 ± 0.7 vs. 3.8 ± 0.7, p < 0.001), satisfaction with the format (4.6 ± 0.6 vs. 3.7 ± 0.9, p < 0.001), and enhancement of clinical decision making skills (4.5 ± 0.6 vs. 3.5 ± 0.9, p < 0.001). The linear format group reported a greater role for the facilitator to add value (4.6 ± 0.5 vs. 4.3 ± 1.1, p = 0.033). There was no significant difference between groups in performance on the knowledge quiz. CONCLUSION: Medical students reported higher satisfaction, engagement, and enhanced clinical decision making skills with the CYOA presentation method compared to linear case format for radiology small group learning.

Multimodality imaging of Surgicel®, an important mimic of post-operative complication in the thorax.

Absorbable hemostatic agents such as Surgicel are hemostatic materials composed of an oxidized cellulose polymer used to control post-surgical bleeding and cause coagulation. This material is sometimes purposefully left in situ where it slowly degrades over time and can produce an imaging appearance that mimics serious post-operative complications such as gangrenous infections and anastomotic leaks as well as potentially mimicking disease recurrence in later stages. In this article, we review the multimodality imaging appearance of this material in situ longitudinally in the range of post-operative settings, in order to promote awareness of this entity when interpreting post-operative imaging. We present this as a pictorial review focusing primarily but not exclusively on the chest noting that the thoracic imaging appearance of Surgicel® is less well reported in the published literature. An understanding of this entity may help to minimize erroneous diagnosis of a postoperative complication leading to unnecessary interventions.

Impact of Interobserver Variability in Manual Segmentation of Non-Small Cell Lung Cancer (NSCLC) Applying Low-Rank Radiomic Representation on Computed Tomography.

This study tackles interobserver variability with respect to specialty training in manual segmentation of non-small cell lung cancer (NSCLC). Four readers included for segmentation are: a data scientist (BY), a medical student (LS), a radiology trainee (MH), and a specialty-trained radiologist (SK) for a total of 293 patients from two publicly available databases. Sørensen-Dice (SD) coefficients and low rank Pearson correlation coefficients (CC) of 429 radiomics were calculated to assess interobserver variability. Cox proportional hazard (CPH) models and Kaplan-Meier (KM) curves of overall survival (OS) prediction for each dataset were also generated. SD and CC for segmentations demonstrated high similarities, yielding, SD: 0.79 and CC: 0.92 (BY-SK), SD: 0.81 and CC: 0.83 (LS-SK), and SD: 0.84 and CC: 0.91 (MH-SK) in average for both databases, respectively. OS through the maximal CPH model for the two datasets yielded c-statistics of 0.7 (95% CI) and 0.69 (95% CI), while adding radiomic and clinical variables (sex, stage/morphological status, and histology) together. KM curves also showed significant discrimination between high- and low-risk patients (p-value < 0.005). This supports that readers' level of training and clinical experience may not significantly influence the ability to extract accurate radiomic features for NSCLC on CT. This potentially allows flexibility in the training required to produce robust prognostic imaging biomarkers for potential clinical translation.