Biological aging occurs heterogeneously across individuals and organs. However, current measures of biological age incompletely capture organ-specific differences in health and disease risk. Because chest CT visualizes multiple thoracic organs, it offers an opportunity to quantify structural aging across organ systems. Here, we developed MOSAIC-Age, a framework characterizing eight organ-specific aging clocks on chest CT. The clocks were developed and validated using 9,971 CT scans from CT-RATE and MIDRC, and subsequently locked and applied to two independent prospective cohorts with 35,293 participants from the National Lung Screening Trial and Genetic Epidemiology of COPD study. CT-derived biological age gaps (BAGs) were examined in relation to lifestyle and socioeconomic factors, prevalent comorbidities, incident chronic diseases, and all-cause and cause-specific mortality. Higher BAGs, indicating organs that appeared older on CT than expected for their chronological age, were broadly associated with adverse health characteristics, chronic disease burden, and increased mortality risk. Multiple disease outcomes were associated with aging across several organs, whereas in multivariable analyses including all eight organ-specific BAGs, the remaining associations were more organ specific. A greater number of markedly older-appearing organs and a faster pace of aging were each associated with higher mortality. Together, these findings demonstrate that routine chest CT captures both shared and organ-specific patterns of biological aging and establish CT-derived organ aging as a quantitative imaging biomarker for assessing multi-organ health and long-term disease risk. ### Competing Interest Statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: N. I. V. receives consulting fees from Regeneron, Amgen, Xencor, AstraZeneca, Tempus, Pfizer, Summit, OncoHost, Guardant, ImmunityBio, and research funding from EMD Serono, IDEAYA, Amgen, Summit, Regeneron, Sanofi, BMS, and OncoHost, outside the submitted work. M.C.B.G. has received research funding from Siemens Healthcare. T. C. reports speaker fees/honoraria (including travel/meeting expenses) from ASCO Post, AstraZeneca, Bio Ascend, Bristol Myers Squibb, Clinical Care Options, IDEOlogy Health, Medical Educator Consortium, Medscape, OncLive, PeerView, Physicians' Education Resource, Targeted Oncology; advisory role/consulting fees (including travel/meeting expenses) from AstraZeneca, Bristol Myers Squibb, Daiichi Sankyo, Genentech, Johnson & Johnson, Merck, Nuvalent, oNKo-innate, Pfizer, and RAPT Therapeutics; and institutional research funding from AstraZeneca, Bristol Myers Squibb and Merck. X. L. reports receiving consultant and advisory fees from Eli Lilly, AstraZeneca, EMD Serono, Daiichi Sankyo, Spectrum Therapeutics, Boehringer Ingelheim, Hengrui Therapeutics, Novartis, and research funding from Eli Lilly, Boehringer Ingelheim, all outside of the submitted work. M. A. reports research funding from Genentech, Nektar Therapeutics, Merck, GlaxoSmithKline, Novartis, Jounce Therapeutics, Bristol Myers Squibb, Eli Lilly, Adaptimmune, Shattuck Labs, Gilead, Verismo Therapeutics, and Lyell; advisory board roles for GlaxoSmithKline, Shattuck Labs, Bristol Myers Squibb, AstraZeneca, Insightec, Regeneron, Genprex, and Lyell; speaker fees from AstraZeneca, Nektar Therapeutics, SITC, and Regeneron; and participation on a safety review committee for Nanobiotix-MDA Alliance, Henlius, all outside of the submitted work. A.A.S. reports serving on the advisory board of DELFI Diagnostics and as an advisor to Droplet Biosciences, all outside of the submitted work. D.E.G. reports research funding from AstraZeneca, Karyopharm, and Novocure; stock ownership in Gilead and Medtronic; stock options in Early Marker, Inc. and OncoSeer Diagnostics, Inc.; consulting and advisory roles for AbbVie, AstraZeneca, Bayer, Catalyst Pharmaceuticals, EMD Serono, and GSK; service on data and safety monitoring boards for Daiichi Sankyo, Summit Therapeutics, and Taiho Oncology; royalties from Oxford University Press; and roles as co-founder and Chief Medical Officer of OncoSeer Diagnostics, Inc., all outside of the submitted work. J. Y. C. has received travel sponsorship from Accuray and Varian Medical Systems, and grants from Varian Medical Systems, outside the submitted work. D. L. G. reports honoraria for scientific advisory boards from AstraZeneca, Sanofi, Alethia Biotherapeutics, Menarini, Eli Lilly, 4D Pharma and Onconova, and research support from Janssen, Takeda, Astellas, Ribon Therapeutics, NGM Biopharmaceuticals, Boehringer Ingelheim, Mirati Therapeutics and AstraZeneca, all outside of the submitted work. C. C. W. reports research support from Medical Imaging and Data Resource Center from NIBIB/University of Chicago and royalties from Elsevier, outside of the submitted work. J. V. H. reports receiving advisory/consulting fees from AstraZeneca, Boehringer Ingeheim, Catalyst, Genentech, GlaxoSmithKline, Guardant Health, Foundation Medicine, Hengrui Therapeutics, Eli Lilly, Novartis, Spectrum, Sanofi, Takeda Pharmaceuticals, Mirati Therapeutics, Bristiol Myers Squibb, BrightPath Biotherapeutics, Janssen Global Services, Nexus Health Systems, EMD Serono, Pneuma Respiratory, Kairos Venture Investments, Leads Biolabs, RefleXion, and research funding from GlaxoSmithKline, AstraZeneca, Spectrum, all outside of the submitted work. J. Z. reports grants from Merck, Novartis, Johnson and Johnson; and personal fees from BMS, AZ, Novartis, Johnson and Johnson, GenePlus, Hengrui, Innovent, outside the submitted work. J. W. reports research funding from Siemens Healthcare. The remaining authors declare that they have no competing interests. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of The University of Texas MD Anderson Cancer Center gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes CT-RATE imaging data are available through the Hugging Face repository under the repository's access conditions for academic, research, and educational use (https://huggingface.co/datasets/ibrahimhamamci/CT-RATE). MIDRC imaging data are available through the MIDRC Data Commons to registered users under the applicable MIDRC data use agreement (https://data.midrc.org/). The NLST imaging data are available through The Cancer Imaging Archive (https://www.cancerimagingarchive.net/collection/nlst/), and NLST clinical data are available through the NCI Cancer Data Access System (https://cdas.cancer.gov/nlst/). The COPDGene phenotype and imaging data used in this study are available through controlled access via dbGaP under study accession numbers phs000179.v7.p2 and phs004023.v1.p2, respectively. The paired CT/PET-CT data are not publicly available owing to patient privacy considerations but are available for research purposes from the corresponding author upon reasonable request. The pretrained organ-clock model weights are also available from the corresponding author upon reasonable request. National Institutes of Health, R01CA262425, R01CA276178 Cancer Prevention and Research Institute of Texas, RP240117 Victory Houston Permanent Health Funds QIAC Partnership in Research Grant Rexanna's Foundation for Fighting Lung Cancer

Abstract Background: Immune checkpoint inhibitors (ICIs) have improved survival in non-small-cell lung cancer (NSCLC), yet only a subset of patients benefits, and biomarkers like PD-L1 remain limited. Here, we introduce a deep learning-based pathomics framework that utilizes routine H&E-stained slides to predict therapeutic response and survival outcomes in ICI-treated metastatic NSCLC. Methods: The study included 797 ICI-treated NSCLC patients from MD Anderson, with external validation in 280 patients from Mayo Clinic, Gustave Roussy, and the Phase III ICI-naïve LUSC Lung-MAP S1400I trial receiving nivolumab with or without ipilimumab. Path-IO (Pathology-Driven Immunotherapy Optimization) comprised of four major steps: (1) pathologist-verified tissue classifier segmented WSIs into eight compartments—Background, Bronchi, Immune, Lung, Necrosis, Stroma, Tumor, and Vessel—and was validated on TCGA and CPTAC dataset; (2) a survival prediction module generating patient-level risk scores in the MD Anderson cohort and validated across external datasets; (3) integration of Path-IO risk scores with radiomics and clinical features to improve prognostic accuracy; and (4) biological interpretability analyses correlating Path-IO risk immune contexture from multiplex immunofluorescence and transcriptomic signatures from NanoString profiling. Results: Path-IO effectively stratified patients into high and low-risk groups with significant survival differences. In the MD Anderson cohort, it achieved HR = 2.11 (p < 0.001) and HR = 2.51 (p < 0.001) for OS in the discovery and validation sets, and HR = 2.34 (p < 0.001) and HR = 1.87 (p < 0.001) for PFS, respectively. In the multicenter Phase III Lung-MAP S1400I trial, Path-IO achieved HR = 1.78 (p = 0.016) for OS and HR = 2.76 (p = 0.006) for PFS. In external validation, it predicted outcomes in the Gustave Roussy (OS = 1.97, p = 0.003; PFS = 1.51, p = 0.046) and Mayo Clinic (OS = 2.46, p = 0.007; PFS = 2.45, p = 0.027) cohorts. Path-IO outperformed PD-L1 and remained an independent predictor in multivariate analysis (p < 0.001). It enabled data-driven frontline selection between anti-PD-1 monotherapy and chemo-immunotherapy, offering guidance toward more personalized treatment decisions. Integration with radiomics and clinical features further improved predictive accuracy (OS 0.63→0.75; PFS C-index 0.58→0.70), while high Path-IO risk scores correlated with immune-cold phenotypes identified by multiplex immunofluorescence and NanoString transcriptomics. Conclusions: Path-IO demonstrates that deep learning models rooted in histopathologic architecture can generate interpretable and biologically informed survival predictions in NSCLC treated with ICIs. By integrating pathology, radiology, and clinical data, Path-IO provides complementary predictive value beyond established biomarkers such as PD-L1. Citation Format: Rukhmini Bandyopadhyay, Linghzi Hong, Mihaela Aldea, Shenduo Li, Lodovica Zullo, Frank R. Rojas, Maliazurina B. Saad, Maricel C. Marin, Muhammad Waqas, Jiexin Zhang, Eman Showkatian, Claudio A. Arrechedera, Xiaoyu Han, Yuliya Kitsel, Sherif Ismail, Muhammad Aminu, Bo Zhu, Carol C. Wu, Brett W. Carter, Joe Y Chang, Zhongxing Liao, Maria R. Ghigna, Davide Soldato, Hai T. Tran, Xiuning Le, Tina Cascone, Bingnan Zhang, Haniel A. Araujo, Mehmet Altan, Simon Heeke, David Jaffray, Don L. Gibbons, Ara Vaporciyan, J Jack Lee, Neda Kalhor, Cara Haymaker, Ignacio Wistuba, John V. Heymach, Yanyan Lou, Natalie Vokes, Luisa M. Solis Soto, Jianjun Zhang, Jia Wu. Path-IO: A deep learning pathomics framework for personalized immunotherapy selection and outcome prediction in metastatic non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4003.

This document provides a comprehensive overview of the evidence supporting different imaging modalities and techniques used for the staging and surveillance of patients with cutaneous, mucocutaneous, and ocular melanoma. Guidelines are provided based on nodal status, presence of metastases at baseline, suspected metastases, and local stage. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Immune checkpoint inhibitors (ICIs) benefit only a subset of patients with metastatic non-small cell lung cancer (NSCLC), but current selection relies on tissue PD-L1 immunohistochemistry (IHC), which is invasive and prone to sampling bias. We developed and validated SCENT (Scalable Ensemble Transformer), a CT-based deep learning model for noninvasive prediction of PD-L1 status and immunotherapy outcomes. In this retrospective study, 972 stage IV NSCLC patients treated with ICIs at MD Anderson were analyzed; SCENT was developed and validated in 640 patients with paired CT and PD-L1 IHC, and clinical applicability was assessed in an additional 332 CT-only patients. Generalizability was evaluated in independent cohorts from Mayo Clinic (n = 72) and the phase III LONESTAR trial (n = 116), where paired baseline and 3-month CT enabled longitudinal assessment. SCENT classified PD-L1 status (50% or higher vs lower) in the MD Anderson cohort with AUC 0.84 (95% CI 0.785 to 0.887), specificity 83.9%, and sensitivity 85.3%, outperforming clinical and radiomics models; external validation achieved AUC 0.80 (Mayo) and 0.78 (LONESTAR). SCENT-derived PD-L1 stratified progression-free survival (HR 1.49, p < 0.001) and overall survival (HR 1.40, p = 0.009), comparable to IHC, and provided complementary prognostic value when combined with IHC, with concordant low-low patients showing the poorest survival (OS HR 1.45, p = 0.008). In LONESTAR, serial SCENT-inferred PD-L1 status showed a borderline association with 3-month progression without paired post-treatment tissue confirmation. SCENT is a generalizable CT-based virtual biopsy for baseline PD-L1 prediction and complementary tissue IHC stratification, with longitudinal use requiring prospective validation.

Abstract Introduction: Despite advances with next-generation tyrosine kinase inhibitors (TKIs), response and progression patterns in ALK-rearranged NSCLC vary widely, and currently no reliable biomarkers can predict individualized outcomes under alternative therapies. Digital-twin models offer a solution by integrating real-world evidence to reconstruct patient-specific counterfactual disease trajectories for single-arm trials and evaluation of escalation strategies. Methods: We integrated real-world and clinical-trial datasets of ALK-rearranged NSCLC treated with TKIs, including MDACC cohort (n = 103, GEMINI database) for model development, Phase III randomized ALTA-1L (n = 207) for external validation, and Phase II BrightStar (n = 32) for clinical application. Tumor burden was quantified at whole-body and organ levels using CT-derived 3D volumetrics, combined with longitudinal routine blood test and demographic variables. Two digital twin models were developed through machine learning: OncoTwin-2D, a parsimonious model based on serial sum of longest diameters (SLD) and demographics, and OncoTwin-3D, an advanced model integrating longitudinal 3D volumetric, blood, and demographic features. Models were evaluated on MDACC and ALTA-1L cohorts by hazard ratio (HR) and log-rank test. The calibrated OncoTwin-3D was further applied to the single-arm BrightStar trial to simulate a counterfactual brigatinib-only control arm and evaluate the added benefit of local consolidation therapy (LCT). Results: Early tumor response patterns and long-term outcomes differed by TKI generation, with second-generation TKIs showing greater overall and organ-level responses and longer median PFS (29 vs 10 months; HR = 0.45; p < 0.001) than first-generation TKI. For our digital twin framework, OncoTwin-2D achieved robust risk stratification (HR = 1.8, p = 0.034 in MDACC cohort; HR = 2.1, p < 0.001 for external ALTA-1L cohort). Furthermore, model predicted survival outcomes aligned consistently with observed outcomes in ALTA-1L for first- and second-generation TKI. The advanced OncoTwin-3D further improved prognostic accuracy with significant risk stratification in MDACC cohort (HR = 3.9, p < 0.0001) and separately for individual TKI subgroups (p = 0.006 and p < 0.001 for first- and second-generation TKI). In the prospective BrightStar phase II trial, OncoTwin-3D was applied to simulate a counterfactual brigatinib-only control arm, which revealed a significant benefit from adding LCT (median PFS 66 vs. 22 months; HR = 2.8, p = 0.002). Conclusion: We introduced OncoTwin, an AI-driven multimodal digital twin for individualized response prediction and novel escalation evaluation. This scalable framework extends beyond thoracic disease, offering a generalizable paradigm that bridges real-world data and clinical trials to accelerate precision oncology. Citation Format: Hui Xu, Yasir Y. Elamin, Lingzhi Hong, Kyle Concannon, Maliazurina Binti Saad, Xinyan Xu, Muneer Amgad, Hui Li, Kang Qin, Xiaoyu Han, Sherif Ismail, Yuliya Kitsel, Saumil Gandhi, Mara B. Antonoff, Carol C. Wu, Brett W. Carter, Girish S Shroff, Simon Heeke, Xiuning Le, Tina Cascone, Natalie Vokes, Mehmet Altan, Don L. Gibbons, David Jaffray, Joe Y Chang, Zhongxing Liao, David Rice, Ara Vaporciyan, Stephen G G. Swisher, J Jack Lee, Jianjun Zhang, John V. Heymach, Jia Wu, . OncoTwin: A multimodal digital twin framework for predicting treatment response and guiding trial design in ALK-rearranged non-small-cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6724.

Importance:The optimal configuration of a smoking cessation intervention in a lung cancer screening (LCS) setting has not yet been established. Objective:To evaluate the efficacy of 3 tobacco treatment strategies of increasing integration and intensity in the LCS setting. Design, Setting, and Participants:In this randomized clinical trial, LCS-eligible current smokers were randomized into 3 treatments: quitline (QL), QL plus (QL+), or integrated care (IC). The study was conducted from July 2017 to June 2022 at a hospital-based tobacco treatment clinic in Houston, Texas. Interventions:The QL intervention group had quitline referral and 12-week nicotine replacement therapy (NRT). The QL+ group had quitline referral plus 12-week NRT or pharmacotherapy prescribed by the LCS clinician. The IC group had 12-week NRT or prescription pharmacotherapy and counseling provided by tobacco treatment specialists within the LCS health care environment. Main Outcomes and Measures:The original primary outcome was biochemically verified 7-day point prevalence abstinence at 6 months; however, this was changed to self-reported abstinence during the conduct of the study due to COVID-19 pandemic restrictions. Results:Of 630 participants, 320 (50.8%) were male, and the median (IQR) age was 59 (55-64) years. Participants smoked a median (IQR) of 20 (15-25) cigarettes per day. Each cohort (QL, QL+, and IC) was composed of 210 participants. The median (IQR) number of counseling sessions was 4 (2-5) sessions for both QL and QL+ and 8 (7-9) sessions for IC. At 3 months, 53 participants (25.2%) in QL, 57 (27.1%) in QL+, and 78 (37.1%) in IC reported abstinence. IC outperformed both QL (odds ratio [OR], 1.75 [95% CI, 1.15-2.66]; P = .01) and QL+ (OR, 1.58 [95% CI, 1.05-2.40]; P = .03). At 6 months, IC maintained the highest rate of abstinence with 68 individuals (32.4%), followed by QL+ at 58 (27.6%) and QL at 43 (20.5%). IC outperformed QL at this time point (OR, 1.86 [95% CI, 1.19-2.89]; P = .01). In the bayesian analysis, IC demonstrated a higher probability of positive absolute risk differences (ARDs) in abstinence at 3 months vs QL (ARD, 0.12) with 99% probability of positive ARD, and QL+ (ARD, 0.10) with 98% probability of positive ARD. This advantage was maintained at 6 months with ARDs of 0.12 for QL (probability of positive ARD, 99%) and 0.05 for QL+ (probability of positive ARD, 86%). Conclusions and Relevance:In this randomized clinical trial, IC involving medication and intensive counseling provides the best opportunity for smoking cessation relative to QL counseling, with or without LCS clinician-managed medication. Although IC consistently outperformed QL and QL+, differences with QL+ were reduced at 6 months, suggesting QL+ could be considered in low-resource settings. Trial Registration:ClinicalTrials.gov Identifier: NCT03059940.

Background: Non-smokers and individuals with minimal smoking history represent a significant proportion of lung cancer cases but are often overlooked in current risk assessment models. Pulmonary nodules are commonly detected incidentally—appearing in approximately 24–31% of all chest CT scans regardless of smoking status. However, most established risk models, such as the Brock model, were developed using cohorts heavily enriched with individuals who have substantial smoking histories. This limits their generalizability to non-smoking and light-smoking populations, highlighting the need for more inclusive and tailored risk prediction strategies. Purpose: We aimed to develop a longitudinal radiomics-based approach for lung cancer risk prediction, integrating time-varying radiomic modeling to enhance early detection in USPSTF-ineligible patients. Methods: Unlike conventional models that rely on a single scan, we conducted a longitudinal analysis of 122 patients who were later diagnosed with lung cancer, with a total of 622 CT scans analyzed. Of these patients, 69% were former smokers, while 30% had never smoked. Quantitative radiomic features were extracted from serial chest CT scans to capture temporal changes in nodule evolution. A time-varying survival model was implemented to dynamically assess lung cancer risk. Additionally, we evaluated the integration of handcrafted radiomic features and the deep learning-based Sybil model to determine the added value of combining local nodule characteristics with global lung assessments. Results: Our radiomic analysis identified specific CT patterns associated with malignant transformation, including increased nodule size, voxel intensity, textural entropy, as indicators of tumor heterogeneity and progression. Integrating radiomics, delta-radiomics, and longitudinal imaging features resulted in the optimal predictive performance during cross-validation (concordance index [C-index]: 0.69), surpassing that of models using demographics alone (C-index: 0.50) and Sybil alone (C-index: 0.54). Compared to the Brock model (67% accuracy, 100% sensitivity, 33% specificity), our composite risk model achieved 78% accuracy, 89% sensitivity, and 67% specificity, demonstrating improved early cancer risk stratification. Kaplan–Meier curves and individualized cancer development probability functions further validated the model’s ability to track dynamic risk progression for individual patients. Visual analysis of longitudinal CT scans confirmed alignment between predicted risk and evolving nodule characteristics. Conclusions: Our study demonstrates that integrating radiomics, sybil, and clinical factors enhances future lung cancer risk prediction in USPSTF-ineligible patients, outperforming existing models and supporting personalized screening and early intervention strategies.

Accurate staging of primary lung cancer is crucial for optimizing therapeutic strategies but remains challenging in clinical practice. We aimed to develop a nomogram incorporating clinical characteristics with CT and PET findings to predict lymph node metastasis (LNM) in primary lung cancer. We retrospectively analyzed patients with primary lung cancer and mediastinal and hilar LNs from a tertiary care cancer center. All patients underwent endobronchial ultrasound-guided transbronchial needle aspiration, with diagnostic chest CT and PET-CT. Cytological confirmation of transbronchial needle aspiration samples served as the gold standard for diagnosing LNM. We employed an LN-level modeling approach and constructed five models for independent prediction of LNM: (1) Clinical-CT-PET model, (2) Clinical-CT model, (3) PET model, (4) Clinical-PET model, and (5) CT-PET model. Their performance was further evaluated in the subgroup of LNs < 1 cm. This study included 455 patients (mean age 70 ± 10 years; 55.4

The sensitivity of reverse-transcription polymerase chain reaction (RT-PCR) is limited for diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The sensitivity and positive predictive values of the assessment by artificial intelligence were 96.8% and 90.9%, respectively, while the normal chest radiographs were closely correlated with the likelihood of normal chest radiographs by the artificial intelligence prediction. The model was validated with the first, second, and third external data. The assessment method by artificial intelligence identified suspicious lung lesions on chest radiographs. This novel approach can identify patients with early signs of COVID-19 pneumonia. Background: The sensitivity of reverse-transcription polymerase chain reaction (RT-PCR) is limited for diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Chest computed tomography (CT) is reported to have high sensitivity; however, given the limited availability of chest CT during a pandemic, the assessment of more readily available imaging, such as chest radiographs, augmented by artificial intelligence may substitute for the detection of the features of coronavirus disease 2019 (COVID-19) pneumonia. Methods: We trained a deep convolutional neural network to detect SARS-CoV-2 pneumonia using publicly available chest radiography imaging data including 8,851 normal, 6,045 pneumonia, and 200 COVID-19 pneumonia radiographs. The entire cohort was divided into training (n = 13,586) and test groups (n = 1510). We assessed the accuracy of prediction with independent external data. Results: The sensitivity and positive predictive values of the assessment by artificial intelligence were 96.8% and 90.9%, respectively. In the first external validation of 204 chest radiographs among 107 patients with confirmed COVID19, the artificial intelligence algorithm correctly identified 174 (85%) chest radiographs as COVID-19 pneumonia among 97 (91%) patients. In the second external validation with 50 immunocompromised patients with leukemia, the higher probability of the artificial intelligence assessment for COVID-19 was correlated with suggestive features of COVID19, while the normal chest radiographs were closely correlated with the likelihood of normal chest radiographs by the artificial intelligence prediction. Conclusions: The assessment method by artificial intelligence identified suspicious lung lesions on chest radiographs. This novel approach can identify patients for confirmatory chest CT before the progression of COVID-19 pneumonia.

Immune checkpoint inhibitors (ICIs), either as monotherapy (ICI-Mono) or combined with chemotherapy (ICI-Chemo), improves survival in advanced non-small cell lung cancer (NSCLC). However, prospective guidance for choosing between these options remains limited, and single-feature biomarkers like PD-L1 prove inadequate. We develop a machine learning model using clinicogenomic data from four cohorts (MD Anderson n = 750; Mayo Clinic n = 80; Dana-Farber n = 1077; Stand Up To Cancer n = 393) to predict individual benefit from adding chemotherapy. Benefit scores are calculated using five distinct functions derived from 28 genomic and 6 clinical features. Our integrated model, A-STEP (Attention-based Scoring for Treatment Effect Prediction), estimates heterogeneous treatment effects and achieves the largest reduction in 3-month progression risk, improving weighted risk reduction by 13-23% over stand-alone models. A-STEP recommends treatment changes for over 50% of patients, most often favoring ICI-Chemo. In simulation on external cohort, patients treated in accordance with A-STEP recommendations show improved 2-year progression-free survival (HR = 0.60 for ICI-Mono treatment arm; HR = 0.58 for ICI-Chemo treatment arm). Predictive features include FBXW7, APC, and PD-L1. In this study, we demonstrate how machine learning can fill critical gaps in immunotherapy selection for NSCLC, by modeling treatment heterogeneity with real-world clinicogenomic data, driving precision medicine beyond conventional biomarker boundaries.

ImportanceThe optimal configuration of a smoking cessation intervention in a lung cancer screening (LCS) setting has not yet been established.ObjectiveTo evaluate the efficacy of 3 tobacco treatment strategies of increasing integration and intensity in the LCS setting.Design, Setting, and ParticipantsIn this randomized clinical trial, LCS-eligible current smokers were randomized into 3 treatments: quitline (QL), QL plus (QL+), or integrated care (IC). The study was conducted from July 2017 to June 2022 at a hospital-based tobacco treatment clinic in Houston, Texas.InterventionsThe QL intervention group had quitline referral and 12-week nicotine replacement therapy (NRT). The QL+ group had quitline referral plus 12-week NRT or pharmacotherapy prescribed by the LCS clinician. The IC group had 12-week NRT or prescription pharmacotherapy and counseling provided by tobacco treatment specialists within the LCS health care environment.Main Outcomes and MeasuresThe original primary outcome was biochemically verified 7-day point prevalence abstinence at 6 months; however, this was changed to self-reported abstinence during the conduct of the study due to COVID-19 pandemic restrictions.ResultsOf 630 participants, 320 (50.8%) were male, and the median (IQR) age was 59 (55-64) years. Participants smoked a median (IQR) of 20 (15-25) cigarettes per day. Each cohort (QL, QL+, and IC) was composed of 210 participants. The median (IQR) number of counseling sessions was 4 (2-5) sessions for both QL and QL+ and 8 (7-9) sessions for IC. At 3 months, 53 participants (25.2%) in QL, 57 (27.1%) in QL+, and 78 (37.1%) in IC reported abstinence. IC outperformed both QL (odds ratio [OR], 1.75 [95% CI, 1.15-2.66]; P = .01) and QL+ (OR, 1.58 [95% CI, 1.05-2.40]; P = .03). At 6 months, IC maintained the highest rate of abstinence with 68 individuals (32.4%), followed by QL+ at 58 (27.6%) and QL at 43 (20.5%). IC outperformed QL at this time point (OR, 1.86 [95% CI, 1.19-2.89]; P = .01). In the bayesian analysis, IC demonstrated a higher probability of positive absolute risk differences (ARDs) in abstinence at 3 months vs QL (ARD, 0.12) with 99% probability of positive ARD, and QL+ (ARD, 0.10) with 98% probability of positive ARD. This advantage was maintained at 6 months with ARDs of 0.12 for QL (probability of positive ARD, 99%) and 0.05 for QL+ (probability of positive ARD, 86%).Conclusions and RelevanceIn this randomized clinical trial, IC involving medication and intensive counseling provides the best opportunity for smoking cessation relative to QL counseling, with or without LCS clinician–managed medication. Although IC consistently outperformed QL and QL+, differences with QL+ were reduced at 6 months, suggesting QL+ could be considered in low-resource settings.Trial RegistrationClinicalTrials.gov Identifier: NCT03059940

BackgroundNeoadjuvant immune checkpoint inhibitors (ICIs) have improved survival outcomes compared with chemotherapy in resectable non-small cell lung cancer (NSCLC). However, the impact of actionable genomic alterations (AGAs) on the efficacy of neoadjuvant ICIs remains unclear. We report the influence of AGAs on treatment failure (TF) in patients with resectable NSCLC treated with neoadjuvant ICIs.MethodsTumor molecular profiles were obtained from patients with stage I–IIIA resectable NSCLC (American Joint Committee on Cancer seventh edition) treated with either neoadjuvant nivolumab (N, n=23) or nivolumab+ipilimumab (NI, n=21) followed by surgery in a previously reported phase-2 randomized study (NCT03158129). TF was defined as any progression of primary lung cancer after neoadjuvant ICI therapy in patients without surgery, radiographic and/or biopsy-proven primary lung cancer recurrence after surgery, or death from possibly treatment-related complications or from primary lung cancer since randomization. Tumors with AGAs (n=12) were compared with tumors without AGAs and non-profiled squamous cell carcinomas (non-AGAs+NP SCC, n=20).ResultsWith a median follow-up of 60.2 months, the overall TF rate was 34.1% (15/44). Tumor molecular profiling was retrospectively obtained in 47.7% (21/44) of patients and select AGAs were identified in 12 patients: 5 epidermal growth factor receptor(EGFR), 2KRAS, 1ERBB2, and 1BRAFmutations, 2 anaplastic lymphoma kinase(ALK)and 1RETfusions. The median time to TF in patients with AGAs was 24.7 months (95% CI: 12.6 to 40.4), compared with not reached (95% CI: not evaluable (NE)–NE) in the non-AGAs+NP SCC group. The TF risk was higher in AGAs (HR: 5.51, 95% CI: 1.68 to 18.1), and lower in former/current smokers (HR: 0.24, 95% CI: 0.08 to 0.75). The odds of major pathological response were 4.71 (95% CI: 0.49 to 45.2) times higher in the non-AGAs+NP SCC group, and the median percentage of residual viable tumor was 72.5% in AGAs compared with 33.0% in non-AGS+NP SCC tumors.ConclusionsPatients with NSCLC harboring select AGAs, includingEGFRandALKalterations, have a higher risk for TF, shorter median time to TF, and diminished pathological regression after neoadjuvant ICIs. The suboptimal efficacy of neoadjuvant chemotherapy-sparing, ICI-based regimens in this patient subset underscores the importance of tumor molecular testing prior to initiation of neoadjuvant ICI therapy in patients with resectable NSCLC.

For patients with advanced non-small-cell lung cancer (NSCLC), dual immune checkpoint blockade (ICB) with CTLA4 inhibitors and PD-1 or PD-L1 inhibitors (hereafter, PD-(L)1 inhibitors) is associated with higher rates of anti-tumour activity and immune-related toxicities, when compared with treatment with PD-(L)1 inhibitors alone. However, there are currently no validated biomarkers to identify which patients will benefit from dual ICB1,2. Here we show that patients with NSCLC who have mutations in the STK11 and/or KEAP1 tumour suppressor genes derived clinical benefit from dual ICB with the PD-L1 inhibitor durvalumab and the CTLA4 inhibitor tremelimumab, but not from durvalumab alone, when added to chemotherapy in the randomized phase III POSEIDON trial(3). Unbiased genetic screens identified loss of both of these tumour suppressor genes as independent drivers of resistance to PD-(L)1 inhibition, and showed that loss of Keap1 was the strongest genomic predictor of dual ICB efficacy-a finding that was confirmed in several mouse models of Kras-driven NSCLC. In both mouse models and patients, KEAP1 and STK11 alterations were associated with an adverse tumour microenvironment, which was characterized by a preponderance of suppressive myeloid cells and the depletion of CD8(+) cytotoxic T cells, but relative sparing of CD4(+) effector subsets. Dual ICB potently engaged CD4(+) effector cells and reprogrammed the tumour myeloid cell compartment towards inducible nitric oxide synthase (iNOS)-expressing tumoricidal phenotypes that-together with CD4(+) and CD8(+) T cells-contributed to anti-tumour efficacy. These data support the use of chemo-immunotherapy with dual ICB to mitigate resistance to PD-(L)1 inhibition in patients with NSCLC who have STK11 and/or KEAP1 alterations.

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection. A search for the underlying cause of infection typically includes radiological imaging as part of this investigation. This document focuses on thoracic and abdominopelvic causes of sepsis. In 2017, the global incidence of sepsis was estimated to be 48.9 million cases, with 11 million sepsis-related deaths (accounting for nearly 20% of all global deaths); therefore, understanding which imaging modalities and types of studies are acceptable or not acceptable is imperative. The 5 variants provided include the most commonly encountered scenarios in the setting of sepsis along with recommendations and data for each imaging study.The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

The fusion of cutting-edge imaging technologies with radiation therapy (RT) has catalyzed transformative breakthroughs in cancer treatment in recent decades. It is critical for us to review our achievements and preview into the next phase for future synergy between imaging and RT. This paper serves as a review and preview for fostering collaboration between these two domains in the forthcoming decade. Firstly, it delineates ten prospective directions ranging from technological innovations to leveraging imaging data in RT planning, execution, and preclinical research. Secondly, it presents major directions for infrastructure and team development in facilitating interdisciplinary synergy and clinical translation. We envision a future where seamless integration of imaging technologies into RT will not only meet the demands of RT but also unlock novel functionalities, enhancing accuracy, efficiency, safety, and ultimately, the standard of care for patients worldwide.