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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 ICB 1,2 . 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., Competing Interests: Competing interests F.S. reports consulting for AstraZeneca, Amgen, Revolution Medicines, Novartis, BridgeBio, Beigene, BergenBio, Guardant Health, Calithera Biosciences, Tango Therapeutics, Hookipa Pharma, Novocure, Merck Sharp & Dohme, Roche; grant or research support from Amgen, Mirati Therapeutics, Revolution Medicines, Pfizer, Novartis, Merck & Co; stockholder in BioNTech, Moderna; and honoraria from ESMO, Japanese Lung Cancer Society, Medscape, Intellisphere, VSPO McGill Universite de Montreal, RV Mais Promocao Eventos, MJH Life Sciences, IDEOlogy Health, MI&T, PER, CURIO, DAVA Oncology, the American Association for Cancer Research and the International Association for the Study of Lung Cancer. M.M. reports stockholder in Roche Holdings. N.J. reports shareholder and former employee of Pionyr Immunotherapeutics. J.M.J. reports stock of D2G Oncology. C.M., Y.Y., X.P. and A.H. report employee of Tango Therapeutics. K.C.A. reports personal fees from Sanofi Genzyme and other support from Revolution Medicines, Genentech, and Mirati outside the submitted work. N.V. receives consulting fees from Sanofi Genzyme, Oncocyte, Eli Lilly, Regeneron, and research funding to the institution from Mirati, Oncocyte, and Circulogene, outside the submitted work. P.D.P. reports advisory fees from AstraZeneca and Jazz Pharmaceuticals. M.E.M. reports research funding from Eli Lilly (Inst), AstraZeneca (Inst), Merck (Inst), Genentech (Inst); consulting role with AstraZeneca, Novocure, Boehringer Ingelheim, Janssen, Takeda, Blueprint Pharmaceuticals, Bayer, Bristol Myers Squibb, Ikena; honorarium from Thermo Fisher Scientific; and stock in Merck, Johnson & Johnson. M.M.A. reports grants and personal fees from Genentech, Bristol Myers Squibb and AstraZeneca; grants from Lilly; and personal fees from Maverick, Blueprint Medicine, Syndax, Nektar, Gritstone, ArcherDX, Mirati, NextCure, Novartis, EMD Serono and Panvaxal/NovaRX, outside of the submitted work. J.C.M. reports consulting or honoraria: MJH Life Sciences, Johnson & Johnson and Doximity; and research funding (to institution): Merck via the Conquer Cancer Foundation. J.F.G. reports served as a compensated consultant or received honoraria from Bristol Myers Squibb, Genentech (Roche), Takeda, Loxo (Lilly), Blueprint Medicine, Gilead, Moderna, AstraZeneca, Mariana Therapeutics, Mirati, Jounce, Merus Pharmacueticals, Nuvalent, Pfizer, Novocure, AI Proteins, Novartis, Merck, iTeos, Karyopharm and Silverback Therapeutics; research support from Novartis, Genentech (Roche) and Takeda; institutional research support from Bristol Myers Squibb, Palleon, Tesaro, Moderna, Blueprint, Jounce, Array Biopharma, Merck, Adaptimmune, Novartis and Alexo; has equity in AI Proteins; and has an immediate family member who is an employee with equity at Ironwood Pharmaceuticals. A.D. reports honoraria: Intellisphere, Roche (Genentech); ad board: TP Therapeutics, Guardant Health, AnHeart Therapeutics, ChromaCode; clinical trial support: Syntrix Pharmaceuticals, Novartis, Merck, AnHeart Therapeutics, Sorrento Therapeutics, Guardant Health and Philogen. C. M. Bestvina reports consulting or advisory role: AstraZeneca, Genentech, AbbVie, Curio Science, OncLive Clinical Congress Consultants, Seagen, Creative Educational Concepts, Takeda, Janssen, CVS, Bristol Myers Squibb (Celgene), Jazz Pharmaceuticals, Novartis, Sanofi–Regeneron and Novocure; speakers’ bureau: Merck; research funding: Bristol Myers Squibb and AstraZeneca. C.A.S. reports advisory boards to AstraZeneca, Gilead, Janssen, Genentech, Mirati, Takeda and Arcus Biosciences. J.W.R. reports honoraria or consulting (self): Boehringer Ingelheim, BMS, Turning Point Roche (Genentech), Biodesix, EMD Serono, Daiichi Sankyo, Blueprint, Novartis, Regeneron, Sanofi, Janssen, Jazz Pharmaceuticals, Bayer, Beigene, Merus and Seagen; research support (to institution): ArriVent, Merck, Novartis, AstraZeneca, Spectrum, Revolution Medicines, IO Biotech and Kinnate. C. M. Blakely reports research funding: AstraZeneca, Novartis, Mirati, Spectrum, Takeda, Puma and Pfizer; and consulting: Janssen and Bayer. C.V.P. reports a founder and equity and intellectual property in EnFuego Therapeutics. F.T. reports speaker bureau or honoraria: Roche, AstraZeneca, Novartis and Takeda. M.S. reports institutional support: Dracen Pharmaceuticals; advisory board: Amgen, AstraZeneca, Boehringer Ingelheim, Janssen Pharmaceuticals, Novartis, Pfizer, Roche, Sanofi-Aventis, Siemens Healthineers, Takeda Pharmaceuticals and Bristol Myers Squibb; leadership or fiduciary role: ESMO and EORTC. S.D. reports independent image analysis for hospital-contracted clinical research trials programs for Merck, Pfizer, Bristol Myers Squibb, Novartis, Roche, Polaris, Cascadian, Abbvie, Gradalis, Bayer, Zai laboratories, Biengen, Resonance and Analise; research grants from Lunit, GE, Qure AI; and an honorarium from Siemens. M.J.M. reports AstraZeneca, Bristol Myers Squibb and Istari Oncology & Regeneron. A.G.S. reports AstraZeneca, Genentech (Roche) and Bristol Myers Squibb. A.N.S. reports serving as a consultant and/or advisory board member for Eli Lilly, Daiichi Sankyo and Zymeworks; and research funding from Eli Lilly, Novartis, Daiichi Sankyo, Genentech, AstraZeneca, Memgen, Mersana, Turning Point Therapeutics, BioAtla and Genmab to his institution. J.R. reports consulting fees or honoraria from AstraZeneca, Takeda, Sanofi Genzyme, Genentech, Guardant Health, Summit, G1 Therapeutics, BioAtla, Jazz, Amgen, and Janssen; contracted for research (institutional) with AstraZeneca, BioAtla, Blueprint Medicines, EpimAb Biotherapeutics, LOXO Oncology, Enliven, Redcloud and ORIC. T.S. reports advisory board or consulting fees for Seagen, Astellas and AstraZeneca; and research grant GRAIL. S.B.G. reports research funding from AstraZeneca, Boehringer Ingelheim and Mirati; and consulting or advisory board member for AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Genentech, Amgen, Blueprint Medicine, Sanofi Genzyme, Daiichi Sankyo, Takeda, Janssen, Summit Therapeutics, Merck and Regeneron. J.K. reports for Arterys. K.S. reports consultant, advisor or speaker for Clinica Alemana Santiago, Shattuck Labs, AstraZeneca, EMD Serono, Takeda, Torque/Repertoire Therapeutics, CSRlife, Agenus, Genmab, OnCusp, Parthenon Therapeutics, Bristol Myers Squibb, Roche, Molecular Templates, Abbvie, Sanofi, Merck, PeerView, PER and Forefront Collaborative; research funding from Navigate BP, Tesaro/GSK, Moderna, Takeda, Surface Oncology, Pierre-Fabre, Merck, Bristol Myers Squibb, AstraZeneca, Ribon Therapeutics, Eli Lilly, Boehringer Ingelheim, Genentech (Roche) and Akoya Biosciences. K.D.D. reports consulting fees or other remuneration from Rain Therapeutics. V.A. reports research funding to Johns Hopkins University from AstraZeneca and Personal Genome Diagnostics, has received research funding to Johns Hopkins University from Bristol Myers Squibb and Delfi Diagnostics in the past five years and is an advisory board member for Neogenomics and AstraZeneca. V.A. is an inventor on patent applications (63/276,525, 17/779,936, 16/312,152, 16/341,862, 17/047,006 and 17/598,690) submitted by Johns Hopkins University related to cancer genomic analyses, ctDNA therapeutic response monitoring and immunogenomic features of response to immunotherapy that have been licensed to one or more entities. Under the terms of these license agreements, the University and inventors are entitled to fees and royalty distributions. K.A.M. reports consulting or advisory fees from AstraZeneca, Amgen, Janssen, Mirati Therapeutics, Daiichi Sankyo (Lilly) and Puma Biotechnology, as well as honoraria from AstraZeneca. K.A.M. receives research funding to Johns Hopkins University from Bristol Myers Squibb and Mirati Therapeutics. P.M.F. reports consulting fee (for example, advisory board): Ascendis, AstraZeneca, BMS, Curevac, Novartis, Regeneron, G1, Genelux, Genentech, Gritstone, Merck, Janssen, F-Star, Sanofi, Amgen, Fosun, Teva, Synthekine, Flame, Iteos, Tavotek and Teva; contracted research: AstraZeneca, BMS, Novartis, Regeneron and BioNTech. B.R. reports advisory board or consultant: Regeneron, AstraZeneca, Amgen; honoraria: Targeted Oncology. E.M.V.A reports advisory or consulting: Tango Therapeutics, Genome Medical, Genomic Life, Enara Bio, Manifold Bio, Monte Rosa, Novartis Institute for Biomedical Research, Riva Therapeutics and Serinus Bio; research support: Novartis, BMS and Sanofi; equity: Tango Therapeutics, Genome Medical, Genomic Life, Syapse, Enara Bio, Manifold Bio, Microsoft, Monte Rosa, Riva Therapeutics and Serinus Bio; patents: institutional patents filed on chromatin mutations and immunotherapy response, and methods for clinical interpretation; intermittent legal consulting on patents for Foaley & Hoag; editorial boards: JCO Precision Oncology and Science Advances. A.L.C. reports consulting for AstraZeneca, Merus and Tempus; research funds from AstraZeneca, Genentech (Roche), Merck and Novartis. J.W.G. reports research grants: AstraZeneca, BMS, Eli Lilly, Genentech, Merck and Pfizer; consulting fees: AstraZeneca, BMS, Genentech, Gilead, Gritstone, Eli Lilly, Pfizer and Regeneron. C.A. reports consulting or advisory role: Genentech, Lilly, Celgene, Merck, AstraZeneca, Blueprint Genetics, Shionogi, Daiichi Sankyo–AstraZeneca, Sanofi/Regeneron, Eisai, BeiGene, Turning Point Therapeutics, Pfizer, Janssen, Boehringer Ingelheim; research funding: Genentech/Roche (Inst), Incyte (Inst), Macrogenics (Inst), Merck Sharp & Dohme (Inst) and AstraZeneca–MedImmune (Inst). L.R. reports honoraria: Merck, AstraZeneca, Genzyme, EMD Serono and Astellas Pharmas; consulting or advisory role: Amgen. J.W.N. reports honoraria: CME Matters, Clinical Care Options CME, Research to Practice CME, Medscape CME, Biomedical Learning Institute CME, MLI Peerview CME, Prime Oncology CME, Projects in Knowledge CME, Rockpointe CME, MJH Life Sciences CME, Medical Educator Consortium, HMP education consulting or advisory role: AstraZeneca, Genentech (Roche), Exelixis, Takeda Pharmaceuticals, Eli Lilly, Amgen, Iovance Biotherapeutics, Blueprint Pharmaceuticals, Regeneron Pharmaceuticals, Natera, Sanofi, D2G Oncology, Surface Oncology, Turning Point Therapeutics, Mirati Therapeutics, Gilead Sciences, Abbive, Summit Therapeutics, Novartis, Novocure, Janssen Oncology, Anheart Therapeutics Research Funding: Genentech (Roche), Merck, Novartis, Boehringer Ingelheim, Exelixis, Nektart Therapeutics, Takeda Pharmaceuticals, Adaptimmune, GlaxoSmithKline, Janssen Pharmaceuticals, Abbvie Pharmaceuticals and Novocure. Y.Y.E. reports honoraria: CME Matters, Clinical Care Options CME, Research to Practice CME, Medscape CME, Biomedical Learning Institute CME, MLI Peerview CME, Prime Oncology CME, Projects in Knowledge CME, Rockpointe CME, MJH Life Sciences CME, Medical Educator Consortium, HMP education consulting or advisory role: AstraZeneca, Genentech (Roche), Exelixis, Takeda Pharmaceuticals, Eli Lilly, Amgen, Iovance Biotherapeutics, Blueprint Pharmaceuticals, Regeneron Pharmaceuticals, Natera, Sanofi, D2G Oncology, Surface Oncology, Turning Point Therapeutics, Mirati Therapeutics, Gilead Sciences, Abbive, Summit Therapeutics, Novartis, Novocure, Janssen Oncology, Anheart Therapeutics Reserach Funding: Genentech/Roche, Merck, Novartis, Boehringer Ingelheim, Exelixis, Nektart Therapeutics, Takeda Pharmaceuticals, Adaptimmune, GlaxoSmithKline, Janssen Pharmaceuticals, Abbvie Pharmaceuticals and Novocure. M.V.N. reports research funding to institution: Mirati, Novartis, Checkmate (ended), Alaunos, AstraZeneca, Pfizer, Genentech, Navire; consultant or advisory board: Mirati, Merck/MSD, Novartis and Genentech. X.L. reports receiving consulting or advisory fees from EMD Serono (Merck KGaA), AstraZeneca, Spectrum Pharmaceutics, Novartis, Eli Lilly, Boehringer Ingelheim, Hengrui Therapeutics, Janssen, Blueprint Medicines, Regeneron, Sensei Biotherapeutics, Abion and Abbvie, and research funding from Eli Lilly, EMD Serono, Janssen, ArriVent, Teligene, BlackDiamond, Regeneron and Boehringer Ingelheim. V.K.L. reports consultant or advisory role for Takeda, Seattle Genetics, BMS, AstraZeneca, Guardant Health and AnHeart Therapeutics and has received research funding from GSK, BMS, AstraZeneca, Merck and Seattle Genetics. W.E.L. reports advisory boards: Eisai and Takeda, Grand Rounds and BMS for repotrectinib. J.A.R. reports consultancy, stock, Genprex; patents issued and pending. T.C. reports speaker fees or honoraria from the Society for Immunotherapy of Cancer (SITC), Mark Foundation for Cancer Research, Bristol Myers Squibb, Roche, Medscape, IDEOlogy Health, Physicians’ Education Resource (PER), OncLive and PeerView; travel and/or food/beverage expenses from SITC, International Association for the Study of Lung Cancer, Parker Institute for Cancer Immunotherapy, PER, Dava Oncology, IDEOlogy Health, AstraZeneca and Bristol Myers Squibb; advisory role or consulting fees from MedImmune–AstraZeneca, Bristol Myers Squibb, Merck, Genentech, Arrowhead Pharmaceuticals, Pfizer and Regeneron; and institutional research funding from MedImmune–AstraZeneca, Bristol Myers Squibb and EMD Serono. J.Z. reports grants from Merck, grants and personal fees from Johnson & Johnson and Novartis, personal fees from Bristol Myers Squibb, AstraZeneca, GenePlus, Innovent and Hengrui outside the submitted work. G.R.B. reports grant or contract for clinical trial from MacroGenics; grants from Amgen, Bayer, Adaptimmune, Exelixis, Daiichi Sankyo, GlaxoSmithKline, Immatics, Immunocore, Incyte, Kite Pharma, MacroGenics, Torque, AstraZeneca, Bristol Myers Squibb, Celgene, Genentech, MedImmune, Merck, Novartis, Roche, Sanofi, Xcovery, Tmunity Therapeutics, Regeneron, BeiGene, Repertoire Immune Medicines, Verastem, CytomX Therapeutics and Duality Biologics; consulting fees from AbbVie, Adicet, Amgen, Ariad, Bayer, Clovis Oncology, AstraZeneca, Bristol Myers Squibb, Celgene, Daiichi Sankyo, Instil Bio, Genentech, Genzyme, Gilead, Eli Lilly, Janssen, MedImmune, Merck, Novartis, Roche, Sanofi, Tyme Oncology, Xcovery, Virogin Biotech, Maverick Therapeutics, BeiGene, Regeneron, Cytomx Therapeutics, Intervenn Biosciences and Onconova Therapeutics; participation in advisory boards at Virogin Biotech and Maverick Therapeutics; stock in Virogin Biotech; and employment of family member at Johnson & Johnson/Janssen. C.G. reports member of the advisory board at Jazz Pharmaceuticals, AstraZeneca and Bristol Myers Squibb and served as speaker for AstraZeneca and BeiGene. L.A.B. reports consulting fees and research funding from AstraZeneca, GenMab and Sierra Oncology and research funding from Tolero Pharmaceuticals and served as advisor or consultant for PharmaMar, AbbVie, Bristol Myers Squibb, Alethia, Merck, Pfizer, Jazz Pharmaceuticals, Genentech and Debiopharm Group. D.L.G. reports AstraZeneca, Eli Lilly, Menarini Richerche, 4D Pharma, Onconova Therapeutics and Sanofi. A.T. reports Ariad, AstraZeneca, Bristol Myers Squibb, Boehringer Ingelheim, Eli Lilly, EMD Serono, Genentech, GlaxoSmithKline, Merck, Novartis, Pfizer, Roche, Seattle Genetics, Gilead Sciences and Summit Therapeutics. T.G.B. reports Chan-Zuckerberg Biohub. D.R.C. reports CoI Company sponsored trials at institution (PI roles): AZ, Roche (Genentech) advisory role: ad hoc advisory boards or consultations 2023: Abbvie (steering committee), Anheart, Beigene (IDMC), Eli Lilly (IDMC), Imagene, Immunocore, Janssen, Mirati (IDMC), Prelude, Seattle Genetics, Valencee; 2022: Abbvie, Anheart, Appolomics (SRC), AstraZeneca–Daiichi (ILD adjudication committee), Beigene (DSMB) Dizal, EMD Serono, Elevation, Hengrui, (DSMB), Hummingbird, Janssen, Medtronic, Mersana (ILD adjudication committee), Mirati, Nalo Therapeutics, Onkure, Regeneron, Roche, Sanofi, Takeda, Theseus, Xcovery; 2021: Abbvie, Amgen, Anheart, Apollomics (SRC), AstraZeneca (SRC/SC), Beigene (DSMC), Bio-Thera (DSMB), Blueprint, Daiichi Sankyo (ILD adjudication committee), Elevation (SRC), Eli Lilly (DSMB and NCCN), EMD Serono, Helsinn (DSMB), Hengrui (DSMC), Janssen, Kestrel (SAB, Shares), Mersana, Nuvalent (SAB), Puma (NCCN), Ribon, Roche (Genentech), Sanofi, Seattle Genetics, Takeda and Turning Point. J.E.G. reports honoraria: Jazz Pharmaceuticals, Merck and OncoCyte; consulting or advisory role: AstraZeneca, Blueprint Medicines, Bristol Myers Squibb, EMD Serono, Lilly, Sanofi, Merck Sharp & Dohme, Loxo, Jazz Pharmaceuticals, Novartis, AstraZeneca–MedImmune, Janssen Scientific Affairs, National Comprehensive Cancer Network, AbbVie and Regeneron; research funding: Merck (Inst), AstraZeneca (Inst), Bristol Myers Squibb (Inst), Boehringer Ingelheim (Inst), Genentech (Roche) (Inst), G1 Therapeutics (Inst), Novartis (Inst), Pfizer (Inst), Ludwig Institute for Cancer Research (Inst), SWOG (Inst), Array BioPharma (Inst) and ECOG-ACRIN (Inst). N.L. reports CME and research funding: AstraZeneca. B.L. reports Genentech (Roche), Eli Lilly, Merck, Pfizer, Janssen, Daiichi Sankyo, Takeda, Mirati, Novartis and Guardant 360. J.R.B. reports grant funding: AstraZeneca; advisory board: AstraZeneca, Amgen, Novartis, Incyte, Bristol Myers Squibb, Genentech, Janssen, Sanofi, Regeneron, Merck, GlaxoSmithKline; DSMB: Sanofi, Janssen and GlaxoSmithKline. M.C.G. reports AstraZeneca, Abion, MSD International, Bayer, BMS, Boehringer Ingelheim Italia S.p.A, Celgene, Eli Lilly, Incyte, Novartis, Pfizer, Roche, Takeda, Seattle Genetics, Mirati, Daiichi Sankyo, Regeneron, Merck, Blueprint, Janssen, Sanofi, AbbVie, BeiGenius, Oncohost and Medscape. D.R.G. reports institutional research grants from Amgen, AstraZeneca, Genentech and Merck; consultant or advisory board for Adagene, AstraZeneca, Roche (Genentech), Guardant, IO Biotech, Oncocyte, OncoHost, Lilly, Merck and Novartis. E.B.G. reports consulting or advisory role: Novartis, GlaxoSmithKline, Merck, Boehringer Ingelheim, Shionogi, Eisai, Bristol Myers Squibb, ABL Bio, Xilio Therapeutics, Natera, Sanofi–Regeneron, Lilly, Personalis, Gilead Sciences, AstraZeneca, AbbVie–Abbott, Arcus Biosciences, Seagan and Summit Therapeutics; research funding: Merck (Inst), Genentech (Inst), AstraZeneca (Inst), Novartis (Inst), Lilly (Inst), Bristol Myers Squibb (Inst), Mirati Therapeutics (Inst), Dynavax Technologies (Inst), Iovance Biotherapeutics (Inst), Neon Therapeutics (Inst), EMD Serono (Inst), ABL Bio (Inst) and Daiichi Sankyo–UCB Japan (Inst); patents, royalties, other intellectual property: diagnosistic and therapeutic use of ‘Motif Neoepitopes’ as defined by Cummings et al.76. N.A.R. reports an employee at Synthekine and holds equity at Synthekine and Gritstone. G.V.S. reports honoraria, research funding and personal fees from AstraZeneca, Bayer, BeiGene Switzerland, F. Hoffman–La Roche, Merck Sharpe & Dohme, Eli Lilly, Johnson & Johnson, Pfizer, Takeda Oncology, Tesaro and Verastem outside the submitted work. J.W. reports advisory boards and lecture fees: Amgen, AstraZeneca, Bayer, Blueprint, BMS, Boehringer Ingelheim, Chugai, Daiichi Sankyo, Ignyta, Janssen, Lilly, Loxo, Merck, Mirati, MSD (Merck Sharp & Dohme), Novartis, Nuvalent, Pfizer, Roche, Seattle Genetics, Takeda and Turning Point; research support (to institution): BMS, Janssen, Novartis, Pfizer and AstraZeneca. B.B. reports receiving grants from AbbVie, Amgen, AstraZeneca, Chugai, Daiichi Sankyo, Ellipse, EISAI, Genmab, Genzyme Corporation, Hedera Dx, Inivata, IPSEN, Janssen, MSD, PharmaMar, Roche (Genentech), Sanofi, Socar Research, Tahio Oncology and Turning Point Therapeutics. R.S.H. reports consulting roles with AbbVie Pharmaceuticals, ARMO Biosciences, AstraZeneca, Biodesix, Bolt Biotherapeutics, Bristol Myers Squibb, Cybrexa Therapeutics, eFFECTOR Therapeutics, Eli Lilly, EMD Serono, Genentech (Roche), Genmab, Halozyme Therapeutics, Heat Biologics, I-Mab Biopharma, Immunocore, Infinity Pharmaceuticals, Loxo Oncology, Merck, Mirati Therapeutics, Nektar, Neon Therapeutics, NextCure, Novartis, Oncternal Therapeutics, Pfizer, Sanofi, Seattle Genetics, Shire, Spectrum Pharmaceuticals, STCube Pharmaceuticals, Symphogen, Takeda, Tesaro, Tocagen and WindMIL Therapeutics; advisory board roles with AstraZeneca, Bolt Biotherapeutics, Cybrexa Therapeutics, EMD Serono, I-Mab Biopharma, Immunocore, Infinity Pharmaceuticals, Neon Therapeutics, Novartis and STCube Pharmaceuticals; research support from AstraZeneca, Eli Lilly, Genentech (Roche) and Merck; and non-executive board membership for Junshi Pharmaceuticals and Immunocore. H.A.W. reports grants or contracts from any entity: Bayer, Arrys Therapeutics, AstraZeneca–Medimmune, BMS, Clovis Oncology, Genentech (Roche), Merck, Novartis, Seagen, Xcovery and Helsinn; advisory board: Mirati, Merck and Genentech (Roche); leadership: International Association for the Study of Lung Cancer (IASLC), President; ECOG-ACRIN, executive committee. N.A.P. reports an employee at Synthekine and holds equity at Synthekine and Gritstone. A.T.S. is currently an employee of Novartis. P.A.J. reports stock and other ownership interests: Gatekeeper Pharmaceuticals and Loxo; consulting or advisory role: Pfizer, Boehringer Ingelheim, AstraZeneca, Merrimack, Chugai, Roche (Genentech), Loxo, Mirati Therapeutics, Araxes Pharma, Ignyta, Lilly, Takeda, Novartis, Biocartis, Voronoi Health Analytics, SFJ Pharmaceuticals Group, Sanofi, Daiichi Sankyo, Silicon Therapeutics, Nuvalent, Eisai, Bayer, Syndax, AbbVie, Allorion Therapeutics, Accutar Biotech, Transcenta, Monte Rosa Therapeutics, Scorpion Therapeutics, Merus, Frontier Medicines, Hongyun Biotech and Duality Biologics; research funding: AstraZeneca (Inst), Astellas Pharma (Inst), Daiichi Sankyo (Inst), Lilly (Inst), Boehringer Ingelheim (Inst), Puma Biotechnology (Inst), Takeda (Inst) and Revolution Medicines (Inst); patents, royalties, other intellectual property: I am a co-inventor on a DFCI owned patent on EGFR mutations licensed to Lab Corp. I receive post-marketing royalties from this invention. D.P.C. reports advisory boards or consulting: Abbvie, Arcus Biosciences, AstraZeneca, BMS Israel, G1 Therapeutics, Genentech, GlaxoSmithKline, InThought, Iovance Biotherapeutics, Janssen, Jazz, JNJ, Merck–EMD Serono, Merck KGaA, Mirati, MSD, Novartis, Novocure, OncoHost, Pfizer Egypt, Regeneron, Roche and Sanofi; KOL/presentation/education/forum: AstraZeneca, Curio Science, Intellisphere, Merck US, OncLive, Pfizer, PPD Development and Roche Taiwan. M.D.H. is an employee of and stockholder in AstraZeneca. C.M.R. reports consulted regarding oncology drug development with AbbVie, Amgen, AstraZeneca, D2G, Daiichi Sankyo, Epizyme, Genentech (Roche), Ipsen, Jazz, Kowa, Lilly, Merck and Syros. He serves on the scientific advisory boards of Auron, Bridge Medicines, DISCO, Earli and Harpoon Therapeutics. L.A. is an employee of Foundation Medicine and a stockholder of Roche Holding AG. H.M. reports full-time employee of AstraZeneca and stock/shares. Z.Z. reports employee of AstraZeneca as well as stockholder of AstraZeneca and Pfizer. Z.L. reports full-time employee and stock owner of AstraZeneca. R.S. reports other support from AstraZeneca during the conduct of the study, as well as other support from Pfizer outside the submitted work. S.P. reports consultation or advisory role: AbbVie, Amgen, Arcus, AstraZeneca, Bayer, Beigene, BerGenBio, Biocartis, BioInvent, Blueprint Medicines, Boehringer Ingelheim, Bristol Myers Squibb, Clovis, Daiichi Sankyo, Debiopharm, Eli Lilly, F-Star, Fishawack, Foundation Medicine, Genzyme, Gilead, GSK, Hutchmed, Illumina, Incyte, Ipsen, iTeos, Janssen, Merck Sharp and Dohme, Merck Serono, Merrimack, Mirati, Nykode Therapeutics, Novartis, Novocure, PharmaMar, Promontory Therapeutics, Pfizer, Regeneron, Roche (Genentech), Sanofi, Seattle Genetics and Takeda; Board of Directors position: Galenica; talk in a company’s organized public event: AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Foundation Medicine, GSK, Illumina, Ipsen, Merck Sharp and Dohme, Mirati, Novartis, Pfizer, Roche (Genentech), Sanofi and Takeda; receipt of grants or research support: principal investigator in trials (institutional financial support for clinical trials) sponsored by Amgen, Arcus, AstraZeneca, Beigene, Bristol Myers Squibb, GSK, iTeos, Merck Sharp and Dohme, Mirati, PharmaMar, Promontory Therapeutics, Roche (Genentech) and Seattle Genetics. J.M.J. and M.J.R. are employees and shareholders of D2G Oncology. I.P.W. is a co-founder, employee and shareholder of D2G Oncology. M.M.W. is a co-founder, shareholder, member of the board of directors and compensated scientific advisor of D2G Oncology. I.P.W. and M.M.W. are co-inventors of patents relating to technologies for an autochthonous mouse model of human cancer, which D2G Oncology has exclusively licensed from Stanford University. V.A.P. reports Pfizer employee and stockholder. K.K.W. is a founder and equity holder of G1 Therapeutics and has sponsored research agreements with Takeda, TargImmune, Bristol Myers Squibb, Mirati, Merus and Alkermes, and consulting and sponsored research agreements with AstraZeneca, Janssen, Pfizer, Novartis, Merck, Zentalis, BridgeBio and Blueprint. P.J. reports employee and stock or stock options of AstraZeneca. I.I.W. reports honoraria from Genentech (Roche), Bayer, Bristol Myers Squibb, AstraZeneca, Pfizer, Merck, Guardant Health, Flame, Novartis, Sanofi, Daiichi Sankyo, Amgen, Janssen, Merus, G1 Therapeutics, Abbvie, Catalyst Therapeutics, Regeneron, Oncocyte, Medscape, Platform Health and Physicians’ Education Resources; research support from Genentech, Merck, Bristol Myers Squibb, Medimmune, Adaptive, Adaptimmune, EMD Serono, Pfizer, Takeda, Amgen, Karus, Johnson & Johnson, Bayer, Iovance, 4D, Novartis and Akoya. A.G.P. reports Pfizer. J.V.H. reports Advisory Committees: Genentech, Mirati Therapeutics, Eli Lilly, Janssen, Boehringer Ingelheim, Regeneron, Takeda, BerGenBio, Jazz, Curio Science, Novartis, AstraZeneca, BioAlta, Sanofi, Spectrum, GlaxoSmithKline, EMD Serono, BluePrint Medicine and Chugai; research support: AstraZeneca, Boehringer Ingelheim, Spectrum, Mirati, Bristol Myers Squibb and Takeda; licensing or royalties: Spectrum. H.A.A., M.T.D., A.G.C., Y.Q., R.P., D.M., D.H.O., R.M., J.A.H., L.M., S.C.M.L., R.P.J., C.L., Z.W., M.G.W., D.V., H.S., M.K., S.K., Y.N., J.T.A., J.S., L.L., H.N.K., B.C., S.S., R.L., T.Z., J.J.L., D.P., M.L.J. T.T., A.P., Y.K. and J.L.: no disclosures were reported by these authors., (© 2024. The Author(s).)

Fibrosis is a pathological process involving the abnormal deposition of connective tissue, resulting from improper tissue repair in response to sustained injury caused by hypoxia, infection, or physical damage. It can impact any organ, leading to their dysfunction and eventual failure. Additionally, tissue fibrosis plays an important role in carcinogenesis and the progression of cancer.Early and accurate diagnosis of organ fibrosis, coupled with regular surveillance, is essential for timely disease-modifying interventions, ultimately reducing mortality and enhancing quality of life. While extensive research has already been carried out on the topics of aberrant wound healing and fibrogenesis, we lack a thorough understanding of how their relationship reveals itself through modern imaging techniques.This paper focuses on fibrosis of the genito-urinary system, detailing relevant imaging technologies used for its detection and exploring future directions., (© 2024. The Author(s).)

Sustained injury from factors such as hypoxia, infection, or physical damage may provoke improper tissue repair and the anomalous deposition of connective tissue that causes fibrosis. This phenomenon may take place in any organ, ultimately leading to their dysfunction and eventual failure. Tissue fibrosis has also been found to be central in both the process of carcinogenesis and cancer progression. Thus, its prompt diagnosis and regular monitoring is necessary for implementing effective disease-modifying interventions aiming to reduce mortality and improve overall quality of life. While significant research has been conducted on these subjects, a comprehensive understanding of how their relationship manifests through modern imaging techniques remains to be established. This work intends to provide a comprehensive overview of imaging technologies relevant to the detection of fibrosis affecting thoracic organs as well as to explore potential future advancements in this field., (© 2024. The Author(s).)

Fibrosis is the aberrant process of connective tissue deposition from abnormal tissue repair in response to sustained tissue injury caused by hypoxia, infection, or physical damage. It can affect almost all organs in the body causing dysfunction and ultimate organ failure. Tissue fibrosis also plays a vital role in carcinogenesis and cancer progression. The early and accurate diagnosis of organ fibrosis along with adequate surveillance are helpful to implement early disease-modifying interventions, important to reduce mortality and improve quality of life. While extensive research has already been carried out on the topic, a thorough understanding of how this relationship reveals itself using modern imaging techniques has yet to be established. This work outlines the ways in which fibrosis shows up in abdominal organs and has listed the most relevant imaging technologies employed for its detection. New imaging technologies and developments are discussed along with their promising applications in the early detection of organ fibrosis., (© 2024. The Author(s).)

Purpose: To assess the role of pretreatment multiparametric (mp)MRI-based radiomic features in predicting pathologic complete response (pCR) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation therapy (nCRT)., Methods: This was a retrospective dual-center study including 98 patients (M/F 77/21, mean age 60 years) with LARC who underwent pretreatment mpMRI followed by nCRT and total mesorectal excision or watch and wait. Fifty-eight patients from institution 1 constituted the training set and 40 from institution 2 the validation set. Manual segmentation using volumes of interest was performed on T1WI pre-/post-contrast, T2WI and diffusion-weighted imaging (DWI) sequences. Demographic information and serum carcinoembryonic antigen (CEA) levels were collected. Shape, 1st and 2nd order radiomic features were extracted and entered in models based on principal component analysis used to predict pCR. The best model was obtained using a k-fold cross-validation method on the training set, and AUC, sensitivity and specificity for prediction of pCR were calculated on the validation set., Results: Stage distribution was T3 (n = 79) or T4 (n = 19). Overall, 16 (16.3%) patients achieved pCR. Demographics, MRI TNM stage, and CEA were not predictive of pCR (p range 0.59-0.96), while several radiomic models achieved high diagnostic performance for prediction of pCR (in the validation set), with AUCs ranging from 0.7 to 0.9, with the best model based on high b-value DWI demonstrating AUC of 0.9 [95% confidence intervals: 0.67, 1], sensitivity of 100% [100%, 100%], and specificity of 81% [66%, 96%]., Conclusion: Radiomic models obtained from pre-treatment MRI show good to excellent performance for the prediction of pCR in patients with LARC, superior to clinical parameters and CEA. A larger study is needed for confirmation of these results., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Radiologic imaging, especially MRI, has long been the mainstay for rectal cancer staging and patient selection for neoadjuvant therapy prior to surgical resection. In contrast, colonoscopy and CT have been the standard for colon cancer diagnosis and metastasis staging with T and N staging often performed at the time of surgical resection. With recent clinical trials exploring the expansion of the use of neoadjuvant therapy beyond the anorectum to the remainder of the colon, the current and future state of colon cancer treatment is evolving with a renewed interest in evaluating the role radiology may play in the primary T staging of colon cancer. The performance of CT, CT colonography, MRI, and FDG PET-CT for colon cancer staging will be reviewed. N staging will also be briefly discussed. It is expected that accurate radiologic T staging will significantly impact future clinical decisions regarding the neoadjuvant versus surgical management of colon cancer., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Importance: Intravenous (IV) contrast medium is sometimes withheld due to risk of complication or lack of availability in patients undergoing computed tomography (CT) for abdominal pain. The risk from withholding contrast medium is understudied., Objective: To determine the diagnostic accuracy of unenhanced abdominopelvic CT using contemporaneous contrast-enhanced CT as the reference standard in emergency department (ED) patients with acute abdominal pain., Design, Setting, and Participants: This was an institutional review board-approved, multicenter retrospective diagnostic accuracy study of 201 consecutive adult ED patients who underwent dual-energy contrast-enhanced CT for the evaluation of acute abdominal pain from April 1, 2017, through April 22, 2017. Three blinded radiologists interpreted these scans to establish the reference standard by majority rule. IV and oral contrast media were then digitally subtracted using dual-energy techniques. Six different blinded radiologists from 3 institutions (3 specialist faculty and 3 residents) interpreted the resulting unenhanced CT examinations. Participants included a consecutive sample of ED patients with abdominal pain who underwent dual-energy CT., Exposure: Contrast-enhanced and virtual unenhanced CT derived from dual-energy CT., Main Outcome: Diagnostic accuracy of unenhanced CT for primary (ie, principal cause[s] of pain) and actionable secondary (ie, incidental findings requiring management) diagnoses. The Gwet interrater agreement coefficient was calculated., Results: There were 201 included patients (female, 108; male, 93) with a mean age of 50.1 (SD, 20.9) years and mean BMI of 25.5 (SD, 5.4). Overall accuracy of unenhanced CT was 70% (faculty, 68% to 74%; residents, 69% to 70%). Faculty had higher accuracy than residents for primary diagnoses (82% vs 76%; adjusted odds ratio [OR], 1.83; 95% CI, 1.26-2.67; P = .002) but lower accuracy for actionable secondary diagnoses (87% vs 90%; OR, 0.57; 95% CI, 0.35-0.93; P < .001). This was because faculty made fewer false-negative primary diagnoses (38% vs 62%; OR, 0.23; 95% CI, 0.13-0.41; P < .001) but more false-positive actionable secondary diagnoses (63% vs 37%; OR, 2.11, 95% CI, 1.26-3.54; P = .01). False-negative (19%) and false-positive (14%) results were common. Interrater agreement for overall accuracy was moderate (Gwet agreement coefficient, 0.58)., Conclusion: Unenhanced CT was approximately 30% less accurate than contrast-enhanced CT for evaluating abdominal pain in the ED. This should be balanced with the risk of administering contrast material to patients with risk factors for kidney injury or hypersensitivity reaction.

Acute abdominal pain is a common cause of ED visits and often requires imaging to identify a specific diagnosis. Prompt and appropriate imaging plays a crucial role in patient management and leads to improved patient outcomes, decreased hospital stay, and improved ED workflow. There are many cases of abdominal pain in the ED with delayed diagnosis and management secondary to a combination of institutional policies and knowledge deficits in current imaging guidelines. Inappropriate use of abdominal radiographs, use of oral contrast for CT abdomen and pelvis, and concern for iodinated contrast-induced acute kidney injury are three of the more commonly encountered roadblocks to prompt imaging diagnosis of abdominal pain. The purpose of this review is to discuss why these potential causes of delayed diagnosis occur and how radiologists can help improve both imaging and ED workflow by utilizing the most up-to-date imaging guidelines such the American College of Radiology (ACR) Appropriateness Criteria and ACR Manual on Contrast Media to assist clinicians working in the emergency setting., Competing Interests: Declaration of competing interest The authors do not have any competing or conflicting interests to declare in the writing and submission of this review manuscript., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objective: To review existing structured MRI reports for primary staging of rectal cancer and create a new, freely available structured report based on multidisciplinary expert opinion and literature review., Methods: Twenty abdominal imaging experts from the Society of Abdominal Radiology (SAR)'s Disease Focused Panel (DFP) on Rectal and Anal Cancer completed a questionnaire and participated in a subsequent consensus meeting based on the RAND-UCLA Appropriateness Method. Twenty-two items were classified via a group survey as "appropriate" or "inappropriate" (defined by ≥ 70% consensus), or "needs group discussion" (defined by < 70% consensus). Certain items were also discussed with multidisciplinary team members from colorectal surgery, oncology and pathology., Results: After completion of the questionnaire, 16 (72%) items required further discussion (< 70% consensus). Following group discussion, consensus was achieved for 21 (95%) of the items. Based on the consensus meeting, a revised structured report was developed. The most significant modifications included (1) Exclusion of the T2/early T3 category; (2) Replacement of the term "circumferential resection margin (CRM)" with "mesorectal fascia (MRF)"; (3) A revised definition of "mucinous content"; (4) Creation of two distinct categories for suspicious lymph nodes (LNs) and tumor deposits; and (5) Classification of suspicious extra-mesorectal LNs by anatomic location., Conclusion: The SAR DFP on Rectal and Anal Cancer recommends using this newly updated reporting template for primary MRI staging of rectal cancer., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: To investigate specific imaging and patient-related factors associated with a false negative (FN) MRI-targeted prostate fusion biopsies (TBx) of suspicious MRI lesions., Methods: Retrospective study of men with PI-RADS 4 or 5 lesions November, 2015-December 2020 with TBx and systematic biopsy (SBx) performed. Only FN and true positive (TP) targeted lesions were included. FN biopsy was defined as a negative TBx with a positive systematic core in the ROI or perilesional sextant. Logistic regression was used to determine the association of patient and imaging-specific factors with the probability of a FN TBx., Results: 361 PI-RADS 4 or 5 lesions in 304 patients, including 67 FN (19%) and 294 TP (81%) were included. There was a significant inverse association between lesion size (OR: 0.94, P-value: .02), presence of a suspicious DRE (OR: 0.36, P-value: .02) and PSA density (OR: 0.01, P-value: .004) on the probability of obtaining a FN TBx. There was no association between age, biopsy indication, use of an enema before MRI, prostate size, or discrepant US and MRI segmentation volumes on the probability of a FN TBx., Conclusion: In this cohort, SBx detected 19% of csPCa missed on TBx. Smaller PI-RADS 4/5 lesions, lower PSAD values, and a normal DRE were all associated with an increased probability of a FN TBx., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: In the absence of overt infection signs, clinical criteria for early intervention in patients with ureteral stones are poorly defined. We aimed to develop a model that can identify patients who are at risk for developing sepsis if discharged home from the emergency department (ED). Materials and Methods: We retrospectively reviewed patients between January 2010 and December 2019 who were discharged from the ED after diagnosis of ureteral stones. The primary outcome was sepsis requiring urgent surgical decompression. We used multivariable logistic regression to identify predictors of sepsis. We refined the model using backward stepwise regression with a threshold p -value of 0.05. Results: We identified 1331 patients who were discharged from the ED with ureteral stones. Of these patients, 22 (2%) subsequently developed sepsis requiring urgent decompression. In the initial multivariable model, female gender (odds ratio [OR]: 2.82, p  = 0.039) and urine white blood cells (WBCs) (OR: 1.02 per cell count, p  < 0.001) were predictive of sepsis. After performing backward stepwise regression, female gender, urine WBCs, and leukocytosis (WBCs >15,000/mm 3 ) met criteria for inclusion in the model. A logistic model including these variables predicted sepsis with an internally cross-validated area under the curve (AUC) of 0.79. Among patients with urine cultures completed in the ED, rates of sepsis were 9% in patients with positive cultures and 1% in patients with negative cultures ( p  < 0.001). Antibiotic usage was not protective against developing sepsis. Conclusions: Sepsis is a rare complication among patients with ureteral stones selected for conservative management. The presence of elevated urine WBCs and female gender can help identify patients who are at risk of developing sepsis. Patients with risk factors should be managed with an increased index of suspicion for infection and may benefit from early intervention to reduce the risk of sepsis. Sepsis is more common in patients with positive urine cultures.

Purpose: To investigate practice patterns related to MR technique and structured reporting for MRI of rectal cancer at academic centers and private practice groups in the United States., Methods and Materials: A survey developed by active members of the Society of Abdominal Radiology Rectal and Anal Cancer Disease Focus Panel was sent to 100 private practice and 189 academic radiology groups. The survey asked targeted questions about practice demographics and utilization, technical MR parameters and reporting practices related to MRI of rectal cancer. The results were analyzed using software in an online survey program., Results: The survey received 47 unique responses from academic (37/47, 78.7%) and private practice (10/47, 21.3%) groups. Most practices report using rectal MR for staging newly diagnosed rectal cancer always (29/47, 61.7%), and less than half always do so for re-staging after neoadjuvant chemoradiation (20/47, 42.6%). Most groups perform between 1 to 5 rectal MR studies for baseline staging per week (32/47, 68.1%) and most groups perform 0 to 2 MR studies for re-staging per week (27/47, 57.4%). Regarding patient preparation and MR technical parameters, there is variability, but a few key points include most practices (27/47, 57.4%) acquire axial T2-weighted images at a slice thickness of 3 mm or less, axial T2-weighted images with 2D acquisition (30/47, 63.8%), a T2-weighted axial oblique sequence through the tumor (43/47, 91.5%), and T2-weighted images without fat suppression (37/47, 78.7%). Equal numbers of groups report using a maximal b-value less than 1000 s/mm 2 (19/43, 44.2%) and 1000 s/mm 2 or greater (19/43, 44.2%); the rest were unsure. A substantial portion of respondents do not use intravenous contrast (13/47, 27.7%). Most believe that structured report templates contribute to uniformity of reporting practices (39/47, 83.0%), though there is considerable heterogeneity in usage and included elements., Conclusion: There is considerable technical heterogeneity among respondents' answers and reporting practices in MR for rectal cancer, and most of the groups report reading only a modest number of studies per week. Our findings suggest there may be room for improvement in terms of radiologist education for performance and standardization of clinical practice for MR imaging of rectal cancer., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

We developed a deep learning-based super-resolution model for prostate MRI. 2D T2-weighted turbo spin echo (T2w-TSE) images are the core anatomical sequences in a multiparametric MRI (mpMRI) protocol. These images have coarse through-plane resolution, are non-isotropic, and have long acquisition times (approximately 10-15 min). The model we developed aims to preserve high-frequency details that are normally lost after 3D reconstruction. We propose a novel framework for generating isotropic volumes using generative adversarial networks (GAN) from anisotropic 2D T2w-TSE and single-shot fast spin echo (ssFSE) images. The CycleGAN model used in this study allows the unpaired dataset mapping to reconstruct super-resolution (SR) volumes. Fivefold cross-validation was performed. The improvements from patch-to-volume reconstruction (PVR) to SR are 80.17%, 63.77%, and 186% for perceptual index (PI), RMSE, and SSIM, respectively; the improvements from slice-to-volume reconstruction (SVR) to SR are 72.41%, 17.44%, and 7.5% for PI, RMSE, and SSIM, respectively. Five ssFSE cases were used to test for generalizability; the perceptual quality of SR images surpasses the in-plane ssFSE images by 37.5%, with 3.26% improvement in SSIM and a higher RMSE by 7.92%. SR images were quantitatively assessed with radiologist Likert scores. Our isotropic SR volumes are able to reproduce high-frequency detail, maintaining comparable image quality to in-plane TSE images in all planes without sacrificing perceptual accuracy. The SR reconstruction networks were also successfully applied to the ssFSE images, demonstrating that high-quality isotropic volume achieved from ultra-fast acquisition is feasible., (© 2021. Society for Imaging Informatics in Medicine.)

Purpose: The goal of the current study was to evaluate the effect of a standardized prostate mpMRI reporting template on urologists' understanding and confidence in counselling a patient on the results of the MRI. To do this we performed a survey study to assess the understanding and confidence of urologists reviewing reports prior to (pre) and after (post) adoption of a standardized mpMRI template., Methods: Six urologists reviewed ten pre- and post- mpMRI templated reports and completed a survey to assess the clarity of key elements and the confidence in counseling the patient. The urologists were blinded to the study objective. Nonparametric constrained permutation test for significance was performed to compare the results prior to and after implementation of the template., Results: 29 pre- and 30 post-template mpMRI reports were reviewed. The average score for the post-template reports was significantly higher (10.7 ± 0.6 vs 7.5 ± 2.7 [ p< 0.001]) regardless of the reviewer. Urologists were also overall more confident in counselling patients when the standardized mpMRI reporting template had been used., Conclusion: Implementation of a standardized template for reporting of prostate mpMRI findings resulted in improved clarity and confidence in counselling patients. Radiologists should consider implementing a standardized reporting template to improve clinicians' understanding and confidence of the report., (© 2020. Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: To study the association between gallbladder dimensions and acute cholecystitis and to define a sensitive cutoff for excluding the disease., Materials and Methods: 456 consecutive patients with an abdominal ultrasound performed for right upper quadrant pain, from 1/2019 to 4/2019, were retrospectively collected. Length and width of the gallbladder were measured by a blinded radiology fellow. Patient charts were examined for prospective sonographic findings, clinical data, and pathology from subsequent cholecystectomy or at least 1 month of follow-up with symptom resolution. Univariable and multivariable logistic regression analysis were conducted to define the association of gallbladder dimensions and other sonographic and clinical variables with acute cholecystitis. Optimal and sensitive cutoffs of gallbladder widths were defined. The determined sensitive cutoff was validated with a separate cohort of 501 consecutive patients., Results: 319 patients (median age 48 ± 19 years) including 11%, 19%, and 70% with acute, chronic, and no cholecystitis were included in the experimental cohort, respectively, and 298 patients (median age 50 ± 19 years) including 10%, 12%, and 79% with acute, chronic, and no cholecystitis were included in the validation cohort, respectively. Of all sonographic findings and clinical data, gallbladder width produced the best univariate ROC curve with an AUC of 0.91 (Odds ratio 5.1, 95% CI 3.1-8.5, p < 0.001). 2.2 cm was the gallbladder width cutoff below which there were no cases of acute cholecystitis in the experimental cohort. Multivariable logistic regression analysis using sonographic findings only produced an ROC curve with an AUC of 0.94. Applying the 2.2 cm cutoff in the validation cohort resulted in 100% sensitivity., Conclusion: Lack of gallbladder distention, defined as a width less than 2.2 cm, has potential to serve as a highly sensitive sign for exclusion of acute cholecystitis, regardless of additional sonographic findings and clinical data.

Objective: We aim to test the hypothesis that neurovascular bundle (NVB) displacement by rectal hydrogel spacer combined with NVB delineation as an organ at risk (OAR) is a feasible method for NVB-sparing stereotactic body radiotherapy., Methods: Thirty-five men with low- and intermediate-risk prostate cancer who underwent rectal hydrogel spacer placement and pre-, post-spacer prostate MRI studies were treated with prostate SBRT (36.25 Gy in five fractions). A prostate radiologist contoured the NVB on both the pre- and post-spacer T2W MRI sequences that were then registered to the CT simulation scan for NVB-sparing radiation treatment planning. Three SBRT treatment plans were developed for each patient: (1) no NVB sparing, (2) NVB-sparing using pre-spacer MRI, and (3) NVB-sparing using post-spacer MRI. NVB dose constraints include maximum dose 36.25 Gy (100%), V34.4 Gy (95% of dose) <60%, V32Gy <70%, V28Gy <90%., Results: Rectal hydrogel spacer placement shifted NVB contours an average of 3.1 ± 3.4 mm away from the prostate, resulting in a 10% decrease in NVB V34.4 Gy in non-NVB-sparing plans ( p < 0.01). NVB-sparing treatment planning reduced the NVB V34.4 by 16% without the spacer ( p < 0.01) and 25% with spacer ( p < 0.001). NVB-sparing did not compromise PTV coverage and OAR endpoints., Conclusions: NVB-sparing SBRT with rectal hydrogel spacer significantly reduces the volume of NVB treated with high-dose radiation. Rectal spacer contributes to this effect through a dosimetrically meaningful displacement of the NVB that may significantly reduce RiED. These results suggest that NVB-sparing SBRT warrants further clinical evaluation., Advances in Knowledge: This is a feasibility study showing that the periprostatic NVBs can be spared high doses of radiation during prostate SBRT using a hydrogel spacer and nerve-sparing treatment planning.

Background: Prior research has shown that retrospectively measured apparent diffusion coefficient (ADC) of prostate magnetic resonance imaging (MRI) lesions is associated with clinically significant prostate cancer (csPCa) on targeted biopsy suggesting that ADC should be measured and reported prospectively., Purpose: To assess the impact of mandatory prospective measurement of ADC on the rates of positivity across PI-RADS scores for csPCa., Material and Methods: Consecutive patients who underwent ultrasound (US)-MRI fusion prostate biopsy from August 2018 to July 2019 and who had prospectively reported ADC were compared to control patients who did not. Rates of positivity by PI-RADS category were computed and compared using Chi-square. Multivariable regression was performed., Results: In total, 126 patients (median age 65 years) with 165 prostate lesions (19, 51, 70, and 25 PI-RADS 2, 3, 4, and 5, respectively) and prospectively reported ADC values were compared to 113 control patients (median age 66 years) with 157 prostate lesions (17, 42, 64, and 34 PI-RADS 2, 3, 4, and 5, respectively). Rates of positivity across PI-RADS scores were similar between the two cohorts; 11%, 25%, 55%, and 76% and 0%, 21%, 56%, and 62% for PI-RADS 2, 3, 4, and 5 in the test and control cohorts, respectively (Chi-square P  = 0.78). Multivariate logistic regression showed no significant association between the presence of prospectively measured ADC and csPCa (odds ratio 1.1, 95% confidence interval 0.7-1.7, P  = 0.82)., Conclusion: Prospective ADC measurement may not impact PI-RADS category assignments or positivity rates for csPCa under current guidelines. Future versions of PI-RADS may need to incorporate ADC into scoring rules to realize their potential.

Rationale: The computed tomography (CT) pattern of definite or probable usual interstitial pneumonia (UIP) can be diagnostic of idiopathic pulmonary fibrosis and may obviate the need for invasive surgical biopsy. Few machine-learning studies have investigated the classification of interstitial lung disease (ILD) on CT imaging, but none have used histopathology as a reference standard. Objectives: To predict histopathologic UIP using deep learning of high-resolution computed tomography (HRCT). Methods: Institutional databases were retrospectively searched for consecutive patients with ILD, HRCT, and diagnostic histopathology from 2011 to 2014 (training cohort) and from 2016 to 2017 (testing cohort). A blinded expert radiologist and pulmonologist reviewed all training HRCT scans in consensus and classified HRCT scans based on the 2018 American Thoracic Society/European Respriatory Society/Japanese Respiratory Society/Latin American Thoracic Association diagnostic criteria for idiopathic pulmonary fibrosis. A convolutional neural network (CNN) was built accepting 4 × 4 × 2 cm virtual wedges of peripheral lung on HRCT as input and outputting the UIP histopathologic pattern. The CNN was trained and evaluated on the training cohort using fivefold cross validation and was then tested on the hold-out testing cohort. CNN and human performance were compared in the training cohort. Logistic regression and survival analyses were performed. Results: The CNN was trained on 221 patients (median age 60 yr; interquartile range [IQR], 53-66), including 71 patients (32%) with UIP or probable UIP histopathologic patterns. The CNN was tested on a separate hold-out cohort of 80 patients (median age 66 yr; IQR, 58-69), including 22 patients (27%) with UIP or probable UIP histopathologic patterns. An average of 516 wedges were generated per patient. The percentage of wedges with CNN-predicted UIP yielded a cross validation area under the curve of 74% for histopathological UIP pattern per patient. The optimal cutoff point for classifying patients on the training cohort was 16.5% of virtual lung wedges with CNN-predicted UIP and resulted in sensitivity and specificity of 74% and 58%, respectively, in the testing cohort. CNN-predicted UIP was associated with an increased risk of death or lung transplantation during cross validation (hazard ratio, 1.5; 95% confidence interval, 1.1-2.2; P  = 0.03). Conclusions: Virtual lung wedge resection in patients with ILD can be used as an input to a CNN for predicting the histopathologic UIP pattern and transplant-free survival.

Over the past decade, the Prostate Imaging-Reporting and Data System (PI-RADS) has revolutionized the manner in which prostate cancer is screened for, detected, biopsied, and managed. The single greatest contribution of PI-RADS has been the standardization of interpretation and reporting of findings on MRI of the prostate. This standardization has led to the wide acceptance of the PI-RADS lexicon at a time when structured reporting templates are becoming more widespread in radiology and other medical fields. The author reviews the benefits of structured reporting templates with a focus on prostate MRI, prior studies on this topic, and details of a suggested template.

Objective: To investigate whether pretreatment MRI-based radiomics of locally advanced rectal cancer (LARC) and/or the surrounding mesorectal compartment (MC) can predict pathologic complete response (pCR), neoadjuvant rectal (NAR) score, and tumor regression grade (TRG)., Methods: One hundred thirty-two consecutive patients with LARC who underwent neoadjuvant chemoradiation and total mesorectal excision (TME) were retrospectively collected from 2 centers in the USA and Italy. The primary tumor and surrounding MC were segmented on the best available T2-weighted sequence (axial, coronal, or sagittal). Three thousand one hundred ninety radiomic features were extracted using a python package. The most salient radiomic features as well as MRI parameter and clinical-based features were selected using recursive feature elimination. A logistic regression classifier was built to distinguish between any 2 binned categories in the considered endpoints: pCR, NAR, and TRG. Repeated k-fold validation was performed and AUCs calculated., Results: There were 24, 87, and 21 T4, T3, and T2 LARCs, respectively (median age 63 years, 32 to 86). For NAR and TRG, the best classification performance was obtained using both the tumor and MC segmentations. The AUCs for classifying NAR 0 versus 2, pCR, and TRG 0/1 versus 2/3 were 0.66 (95% CI, 0.60-0.71), 0.80 (95% CI, 0.74-0.85), and 0.80 (95% CI, 0.77-0.82), respectively., Conclusion: Radiomics of pretreatment MRIs can predict pCR, TRG, and NAR score in patients with LARC undergoing neoadjuvant treatment and TME with moderate accuracy despite extremely heterogenous image data. Both the tumor and MC contain important prognostic information., Key Points: • Machine learning of rectal cancer on images from the pretreatment MRI can predict important patient outcomes with moderate accuracy. • The tumor and the tissue around it both contain important prognostic information.

Bladder cancer (BC) is the 10th most common cancer worldwide. Approximately one quarter of patients with BC have muscle-invasive disease (MIBC). Muscle-invasive disease carries a poor prognosis and choosing the optimal treatment option is critical to improve patients' outcomes. Ongoing research supports the role of 2-deoxy-2-(18F)fluoro-D-glucose positron emission tomography (18F-FDG PET) in guiding patient-specific management decisions throughout the course of MIBC. As an imaging modality, 18F-FDG PET is acquired simultaneously with either computed tomography (CT) or MRI to offer a hybrid approach combining anatomical and metabolic information that complement each other. At initial staging, 18F-FDG PET/CT enhances the detection of extravesical disease, particularly in patients classified as oligometastatic by conventional imaging. 18F-FDG PET/CT has value in monitoring response to neoadjuvant and systemic chemotherapy, as well as in localizing relapse after treatment. In the new era of immunotherapy, 18F-FDG PET/CT may also be useful to monitor treatment efficacy as well as to detect immune-related adverse events. With the advent of artificial intelligence techniques such as radiomics and deep learning, these hybrid medical images can be mined for quantitative data, providing incremental value over current standard-of-care clinical and biological data. This approach has the potential to produce a major paradigm shift toward data-driven precision medicine with the ultimate goal of personalized medicine. In this review, we highlight current literature reporting the role of 18F-FDG PET in supporting personalized management decisions for patients with MIBC. Specific topics reviewed include the incremental value of 18F-FDG PET in prognostication, pre-operative planning, response assessment, prediction of recurrence, and diagnosing drug toxicity., (Copyright © 2020 Girard, Vila Reyes, Shaish, Grellier, Dercle, Salaün, Delcroix and Rouanne.)

OBJECTIVE. The purpose of this study was to calculate the negative predictive value of a prostate MRI study with a Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) score of 1 (hereafter referred to as a PI-RADS 1 MRI study) and to explore the patient characteristics and MRI-based factors associated with an MRI study with false-negative results. MATERIALS AND METHODS. A total of 542 consecutive patients with a PI-RADS 1 MRI study obtained between January 2016 and July 2019 were retrospectively identified. Patient charts were examined to identify those patients who subsequently underwent systematic prostate biopsy within 1 year of undergoing MRI or at any later date if the biopsy was negative. Patient characteristics and MRI-specific factors were recorded. Two blinded radiologists evaluated the quality of the axial T2-weighted, DWI, and apparent diffusion coefficient sequences; measured the volume of the bladder, the prostate gland, and rectal gas; and determined whether the peripheral zone was avidly enhancing and whether low signal intensity was seen in 50% or more of the peripheral zone on T2-weighted images. Interobserver agreement was tested. Univariable and multivariable logistic regression models were built. RESULTS. A total of 150 patients (median age, 63 years; interquartile range, 56-70 years) were included. Of these patients, 19 (13%) had prostate cancer with a Gleason score of 3 + 4 or greater, yielding a negative predictive value of 87%. Both low T2 signal intensity in the peripheral zone and the prostate-specific antigen level were associated with a false-negative PI-RADS 1 assessment (odds ratio, 4.9 [95% CI, 1.6-14.9; p = 0.006] and 1.1 [95% CI, 1.0-1.2; p = 0.03], respectively). A cutoff prostate-specific antigen level of 3.97 ng/mL resulted in sensitivity and specificity of 89% and 21%, respectively. There was moderate interobserver agreement for low T2 signal intensity in the peripheral zone (κ coefficient = 0.75). CONCLUSION. Even among select patients who undergo subsequent biopsy because of a high clinical suspicion of prostate cancer, a PI-RADS 1 prostate MRI study has a high negative predictive value. A T2-hypointense peripheral zone and an elevated prostate-specific antigen level are significantly associated with a false-negative MRI study.

Purpose: Evaluate the fluid percentage (FP) and enhancing solid volume (SV) of small (< 4 cm) Bosniak 2F, 3 and 4 renal lesions and the association with Bosniak category at baseline and follow-up., Methods: Hospital database was searched from 1/1/2010 to 8/3/2018 for small (< 4 cm) Bosniak 2F, 3 and 4 lesions studied with initial and follow-up C+CT/MRI. Two radiologists blindly assigned Bosniak categories to first and last available studies. One radiologist performed volumetric analysis of each lesion, calculating the FP and SV. Association with Bosniak category was explored., Results: 121 patients (84:37 M:F) were identified with 136 renal lesions (84, 37 and 15 Bosniak category 2F, 3 and 4) and followed for 1-12.3 years (mean 3.5 years). 87% (73/84) of 2F lesions were downgraded or remained stable. 48% (25/52) of 3/4 lesions were downgraded. Higher FP was associated with a lower Bosniak category (p value = 0.0042). Increase in FP was associated with the probability of being downgraded on follow-up (OR 1.03, p value = 0.0036), while increase in fluid volume of lesion was directly associated with change in overall lesion size among lesions that were downgraded (b-estimate = 0.03, p value = 0.0003). All Bosniak 3/4 lesions with initial SV less than 0.05 cc were downgraded., Conclusion: FP and SV are useful quantitative surrogates for Bosniak category and future behavior, respectively. Growth of small renal lesions by an increase in fluid volume and FP leads to a downgrade in Bosniak category. Initial SV less than 0.05 cc in Bosniak 3/4 lesions suggests possible future downgrade.

Objectives: This study aimed to quantify nonenhancing tumor (NT) component in clear cell renal cell carcinoma (ccRCC) and assess its association with histologically defined tumor necrosis, stage, and survival outcomes., Methods: Among 183 patients with ccRCC, multi-institutional changes in computed tomography attenuation of tumor voxels were used to quantify percent of NT. Associations of NT with histologic tumor necrosis and tumor stage/grade were tested using Wilcoxon signed rank test and with survival outcomes using Kaplan-Meier curves/Cox regression analysis., Results: Nonenhancing tumor was higher in ccRCC with tumor necrosis (11% vs 7%; P = 0.040) and higher pathological stage (P = 0.042 and P < 0.001, respectively). Patients with greater NT had higher incidence of cancer recurrence after resection (P < 0.001) and cancer-specific mortality (P < 0.001)., Conclusion: Nonenhancing tumor on preoperative computed tomographic scans in patients with ccRCC correlates with tumor necrosis and stage and may serve as an independent imaging prognostic biomarker for cancer recurrence and cancer-specific survival.

Objective: Evaluate the accuracy of an obscured surgical capsule (OSC) for detecting diffuse prostate cancer (PCa) in the setting of diffusely T2 hypointense peripheral zones (PZs) on prostate magnetic resonance imaging., Methods: Axial T2-weighted sequences of prostate magnetic resonance imaging performed on July 1, 2015, to August 1, 2017, were blindly assessed for diffusely T2 hypointense PZs, OSC, and focal diffusion abnormalities by 2 radiologists. Diffuse PCa was defined by the presence of bilateral PCa on at least 4 contiguous biopsy cores involving 2/3 levels. Interobserver agreement and multivariate analysis were performed., Results: Seven (10%) of 70 patients had diffuse PCa. There was weak interreader agreement for OSC (κ coefficient = 0.58). Obscured surgical capsule was 57% sensitive and 89% specific for the presence of diffuse PCa and was associated with it (odds ratio, 17.6; P = 0.0008). Multivariate logistic regression demonstrated that OSC was the only significant predictor for the presence of diffuse PCa., Conclusion: Obscured surgical capsule is a strong independent predictor for diffuse PCa in T2 hypointense PZs.

OBJECTIVE. The purpose of this study was to determine the percentage of small (< 4 cm) Bosniak category 2F, 3, and 4 lesions that regress during active surveillance. MATERIALS AND METHODS. In this retrospective study, a hospital database was searched from January 1, 2005, through September 9, 2017, for small (< 4 cm) Bosniak category 2F, 3, and 4 lesions studied with initial and follow-up unenhanced and contrast-enhanced CT or MRI. Prospective Bosniak categories were recorded. Two blinded radiologists retrospectively reassigned Bosniak categories to the initial and last follow-up studies. Interreader variability was analyzed. Rates of stability, regression, and progression were calculated and stratified by size. Logistic regression was used to assess the effects of lesion size, lesion growth, and duration of follow-up on the change in Bosniak categories. RESULTS. The search identified 123 patients (85 men, 38 women) with 138 renal lesions (according to the blinded readings, 83 Bosniak category 2F, 37 category 3, and 18 category 4) and followed for 1-12.3 years (median, 2.7 years). Fifty-one percent (70/138) of the lesions were smaller than 2 cm. Eighty-eight percent (73/83) of category 2F lesions were downgraded or remained stable. Forty-five percent (25/55) of category 3 or 4 lesions were downgraded to 2F or lower. Kappa values were 0.94 between the two readers and 0.72-0.76 between the readers and the prospective Bosniak categories. There was no association between initial size, change in size, or duration of follow-up and change in Bosniak category. CONCLUSION. Approximately one-half of small (< 4 cm) Bosniak category 3 and 4 cystic renal lesions were downgraded, and the majority (88%) of small Bosniak category 2F lesions regressed or remained stable during active surveillance. Therefore, small size should be a consideration for conservative management.

Objective: The aim of this study was to determine which apparent diffusion coefficient-derived texture features are associated with malignancy in Bosniak IIF and III renal cystic lesions., Methods: Twenty benign and 7 malignant Bosniak IIF (22) or III (5) renal cysts, as evaluated with magnetic resonance imaging, were assessed for progression to pathology-confirmed malignancy. Whole-cyst volumes of interest were manually segmented from apparent diffusion coefficient maps. Texture features were extracted from each volume of interest, including first-order histogram-based features and higher-order features, and data were analyzed with the Mann-Whitney U test to predict malignant progression., Results: Eleven of 17 first-order features were significantly greater in benign compared with malignant cysts. Eight higher-order gray-level co-occurrence matrix (GLCM) texture features were significantly different between groups, 5 of which were greater in the benign population., Conclusions: Apparent diffusion coefficient-derived texture measures may help differentiate between benign and malignant Bosniak IIF and III cysts.

Objective: The purpose of this study is to determine whether a convolutional neural network (CNN) can predict the maximum standardized uptake value (SUV max ) of lymph nodes in patients with cancer using the unenhanced CT images from a PET/CT examination, thus providing a proof of concept for potentially using deep learning to diagnose nodal involvement., Materials and Methods: Consecutive initial staging PET/CT scans obtained in 2017 for patients with pathologically proven malignancy were collected. Two blinded radiologists selected one to 10 lymph nodes from the unenhanced CT portion of each PET/CT examination. The SUV max of the lymph nodes was recorded. Lymph nodes were cropped and used with the primary tumor histology type as input for a novel 3D CNN with predicted SUV max as the output. The CNN was trained using one cohort and tested using a separate cohort. An SUV max of 2.5 or greater was defined as FDG avid. Two blinded radiologists separately classified lymph nodes as FDG avid or not FDG avid on the basis of unenhanced CT images and separately using a short-axis measurement cutoff of 1 cm. Logistic regression analysis was performed., Results: A total of 400 lymph nodes (median SUV max , 6.8 [interquartile range {IQR}, 2.7-11.6]; median short-axis, 1.1 cm [IQR, 0.9-1.6 cm]) in 136 patients were used for training. A total of 164 lymph nodes (median SUV max , 3.5 [IQR, 1.9-8.6]; median short-axis, 1.0 cm [IQR, 0.7-1.4 cm]) in 49 patients were used for testing. The predicted SUV max was associated with the real SUV max (β estimate = 0.83, p < 0.0001). The predicted SUV max was associated with FDG avidity (p < 0.0001), with an ROC AUC value of 0.85, and it improved when combined with radiologist qualitative assessment and short-axis criteria., Conclusion: A CNN is able to predict with moderate accuracy the SUV max of lymph nodes, as determined from the unenhanced CT images and tumor histology subtype for patients with cancer.

Purpose: The aim of this study was to assess the impact of a structured reporting template on adherence to the Prostate Imaging Reporting and Data System (PI-RADS) version 2 lexicon and on the diagnostic performance of prostate MRI to detect clinically significant prostate cancer (CS-PCa)., Methods: An imaging database was searched for consecutive patients who underwent prostate MRI followed by MRI-ultrasound fusion biopsy from October 2015 through October 2017. The initial MRI reporting template used included only subheadings. In July 2016, the template was changed to a standardized PI-RADS-compliant structured template incorporating dropdown menus. Lesion, patient characteristics, pathology, and adherence to the PI-RADS lexicon were extracted from MRI reports and patient charts. Diagnostic performance of prostate MRI to detect CS-PCa using combined ultrasound-MRI fusion and systematic biopsy as a reference standard was assessed., Results: Three hundred twenty-four lesions in 202 patients (average age, 67 years; average prostate-specific antigen level, 5.9 ng/mL) were analyzed, including 217 MRI peripheral zone (PZ) lesions, 84 MRI non-PZ lesions, and 23 additional PZ lesions found on systematic biopsy but missed on MRI. Thirty-three percent (106 of 324) were CS-PCa. Adherence to the PI-RADS lexicon improved from 32.9% (50 of 152) to 88.4% (152 of 172) (P < .0001) after introduction of the structured template. The sensitivity of prostate MRI for CS-PCa in the PZ increased from 53% to 70% (P = .011). There was no significant change in specificity (60% versus 55%, P = .458)., Conclusions: A structured template with dropdown menus incorporating the PI-RADS lexicon and classification rules improves adherence to PI-RADS and may increase the diagnostic performance of prostate MRI for CS-PCa., (Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Purpose: To determine the accuracy of split-bolus CT urogram (SB-CTU) without special maneuvers for ureteral distention in diagnosing upper urinary tract urothelial carcinoma (UCA)., Materials and Methods: A hospital database was searched from 1/1/10, to 9/1/15, for SB-CTU exams without special maneuvers for ureteral distention. Accuracy of SB-CTU for detecting upper and lower urinary tract UCA was computed by comparing the prospective radiology report with cystoscopy, ureteroscopy, and/or urologic clinical follow-up. Patients with less than 12 months of clinical follow-up were excluded., Results: 339 studies were identified in 334 patients (60% male 40% female, avg. age 64). 119 studies were performed for microhematuria, 150 for gross hematuria, 13 for hematuria not otherwise specified, 57 for history of UCA, and one for a collecting system mass on a prior CT. There were five upper tract and 33 bladder tumors with overall prevalence of 1.5% and 9.7%, respectively. The prevalence varied significantly with patient age and clinical indication for SB-CTU. There were one false negative and four false positives for upper urinary tract UCA. Sensitivity, specificity, positive predictive value, and negative predictive value for detecting upper tract and bladder tumors were 80%, 99%, 44%, and 100%, respectively, and 55%, 98%, 78%, and 95%, respectively., Conclusion: Based on this study, SB-CTU without special maneuvers for ureteral distention is highly sensitive for detecting upper tract UCA, although with a low positive predictive value, false positives do occur. The clinical utility of increasing accuracy in diagnosing this low-prevalence disease through other more complex CT urogram protocols should be weighed against the added cost and radiation dose associated with these protocols.

Improvements in prostate MR imaging techniques and the introduction of MR imaging-targeted biopsies have had central roles in prostate cancer (PCa) management. The role of MR imaging has progressed from largely staging patients with biopsy-proven PCa to detecting, characterizing, and guiding the biopsy of suspected PCa. These diagnostic advances, combined with improved therapeutic interventions, have led to a more sophisticated and individually tailored approach to patients' unique PCa profile. This review discusses the MR imaging, a standardized reporting scheme, and the role of fusion-targeted prostate biopsy., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: The purpose of the study is to perform a meta-analysis of studies investigating the diagnostic performance of apparent diffusion coefficient (ADC) values in separating high-risk from low-risk prostate cancer (PCa)., Methods: MEDLINE and EMBASE databases were searched in December 2015 for studies reporting diagnostic performance of ADC values for discriminating high-risk from low-risk PCa and providing sufficient data to construct 2 × 2 contingency tables. Diagnostic performance was quantitatively pooled using a bivariate random-effects model including subgroup analysis and assessment of study heterogeneity and methodological quality., Results: 13 studies were included, providing 1107 tumor foci in 705 patients. Heterogeneity among studies was moderate (τ 2  = 0.222). Overall sensitivity was 76.9% (95% CI 68.6-83.6%); overall specificity was 77.0% (95% CI 69.9-82.8%); and summary AUC was 0.67. Inverse correlation between sensitivity and specificity (ρ = -0.58) indicated interstudy heterogeneity was partly due to variation in threshold for test positivity. Primary biases were readers' knowledge of Gleason score during ADC measurement, lack of prespecified ADC thresholds, and lack of prostatectomy as reference in some studies. Higher sensitivity was seen in studies published within the past 2 years and studies not using b value of at least 2000; higher specificity was associated with involvement of one, rather than two, readers measuring ADC. Field strength, coil selection, and advanced diffusion metrics did not significantly impact diagnostic performance., Conclusion: ADC values show moderate accuracy in separating high-risk from low-risk PCa, although important biases may overestimate performance and unexplained sources of heterogeneity likely exist. Further studies using a standardized methodology and addressing identified weaknesses may help guide the use of ADC values for clinical decision-making.

In evaluating the gastrointestinal tract, whether in the emergency room setting, the inpatient setting or the outpatient setting, the radiologist may encounter a myriad of intraluminal radio-opaque, non-anatomic entities. It is the radiologist's role to distinguish between true foreign bodies and medical paraphernalia. Further, the later must be evaluated for proper positioning vs. improper, potentially detrimental positioning. While many foreign bodies from the community may be distinctly familiar to the radiologist, the large variety of medical tools in existence may not be. Furthermore, many medical devices are designed to transiently traverse, or interact with the gastrointestinal tract, requiring the radiologist to become familiar with their natural history. We explore a select group of common and uncommon intraluminal foreign bodies and will divide them into medical paraphernalia that are properly positioned; medical paraphernalia that are in abnormal locations and miscellaneous foreign bodies from the community. For each medical tool, we will discuss its development and medical utility, natural history as it relates to the gastrointestinal tract, optimal positioning as assessed radiologically, malpositioning, and subsequent complications. A small selection of unusual foreign bodies from the community will be presented. Finally, a selection of medical conditions which produce symptoms due to acquired intraluminal objects will be reviewed.