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Wulaningsih W, Villamaria C, Akram A, Benemile J, Croce F, Watkins J. Deep Learning Models for Predicting Malignancy Risk in CT-Detected Pulmonary Nodules: A Systematic Review and Meta-analysis. Lung 2024:10.1007/s00408-024-00706-1. [PMID: 38782779 DOI: 10.1007/s00408-024-00706-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 05/12/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND There has been growing interest in using artificial intelligence/deep learning (DL) to help diagnose prevalent diseases earlier. In this study we sought to survey the landscape of externally validated DL-based computer-aided diagnostic (CADx) models, and assess their diagnostic performance for predicting the risk of malignancy in computed tomography (CT)-detected pulmonary nodules. METHODS An electronic search was performed in four databases (from inception to 10 August 2023). Studies were eligible if they were peer-reviewed experimental or observational articles comparing the diagnostic performance of externally validated DL-based CADx models with models widely used in clinical practice to predict the risk of malignancy. A bivariate random-effect approach for the meta-analysis on the included studies was used. RESULTS Seventeen studies were included, comprising 8553 participants and 9884 nodules. Pooled analyses showed DL-based CADx models were 11.6% more sensitive than physician judgement alone, and 14.5% more than clinical risk models alone. They had a similar pooled specificity to physician judgement alone [0.77 (95% CI 0.68-0.84) v 0.81 (95% CI 0.71-0.88)], and were 7.4% more specific than clinical risk models alone. They had superior pooled areas under the receiver operating curve (AUC), with relative pooled AUCs of 1.03 (95% CI 1.00-1.07) and 1.10 (95% CI 1.07-1.13) versus physician judgement and clinical risk models alone, respectively. CONCLUSION DL-based models are already used in clinical practice in certain settings for nodule management. Our results show their diagnostic performance potentially justifies wider, more routine deployment alongside experienced physician readers to help inform multidisciplinary team decision-making.
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Affiliation(s)
- Wahyu Wulaningsih
- The Royal Marsden, London, UK.
- Faculty of Life Sciences & Medicine, King's College London, London, UK.
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Kiraly AP, Cunningham CA, Najafi R, Nabulsi Z, Yang J, Lau C, Ledsam JR, Ye W, Ardila D, McKinney SM, Pilgrim R, Liu Y, Saito H, Shimamura Y, Etemadi M, Melnick D, Jansen S, Corrado GS, Peng L, Tse D, Shetty S, Prabhakara S, Nadich DP, Beladia N, Eswaran K. Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan. Radiol Artif Intell 2024; 6:e230079. [PMID: 38477661 PMCID: PMC11140517 DOI: 10.1148/ryai.230079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 01/07/2024] [Accepted: 03/01/2024] [Indexed: 03/14/2024]
Abstract
Purpose To evaluate the impact of an artificial intelligence (AI) assistant for lung cancer screening on multinational clinical workflows. Materials and Methods An AI assistant for lung cancer screening was evaluated on two retrospective randomized multireader multicase studies where 627 (141 cancer-positive cases) low-dose chest CT cases were each read twice (with and without AI assistance) by experienced thoracic radiologists (six U.S.-based or six Japan-based radiologists), resulting in a total of 7524 interpretations. Positive cases were defined as those within 2 years before a pathology-confirmed lung cancer diagnosis. Negative cases were defined as those without any subsequent cancer diagnosis for at least 2 years and were enriched for a spectrum of diverse nodules. The studies measured the readers' level of suspicion (on a 0-100 scale), country-specific screening system scoring categories, and management recommendations. Evaluation metrics included the area under the receiver operating characteristic curve (AUC) for level of suspicion and sensitivity and specificity of recall recommendations. Results With AI assistance, the radiologists' AUC increased by 0.023 (0.70 to 0.72; P = .02) for the U.S. study and by 0.023 (0.93 to 0.96; P = .18) for the Japan study. Scoring system specificity for actionable findings increased 5.5% (57% to 63%; P < .001) for the U.S. study and 6.7% (23% to 30%; P < .001) for the Japan study. There was no evidence of a difference in corresponding sensitivity between unassisted and AI-assisted reads for the U.S. (67.3% to 67.5%; P = .88) and Japan (98% to 100%; P > .99) studies. Corresponding stand-alone AI AUC system performance was 0.75 (95% CI: 0.70, 0.81) and 0.88 (95% CI: 0.78, 0.97) for the U.S.- and Japan-based datasets, respectively. Conclusion The concurrent AI interface improved lung cancer screening specificity in both U.S.- and Japan-based reader studies, meriting further study in additional international screening environments. Keywords: Assistive Artificial Intelligence, Lung Cancer Screening, CT Supplemental material is available for this article. Published under a CC BY 4.0 license.
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Affiliation(s)
- Atilla P. Kiraly
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Corbin A. Cunningham
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Ryan Najafi
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Zaid Nabulsi
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Jie Yang
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Charles Lau
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Joseph R. Ledsam
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Wenxing Ye
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Diego Ardila
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Scott M. McKinney
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Rory Pilgrim
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Yun Liu
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Hiroaki Saito
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Yasuteru Shimamura
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Mozziyar Etemadi
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - David Melnick
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Sunny Jansen
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Greg S. Corrado
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Lily Peng
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Daniel Tse
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Shravya Shetty
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Shruthi Prabhakara
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - David P. Nadich
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Neeral Beladia
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
| | - Krish Eswaran
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View,
CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L.,
S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y.,
N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.);
Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai
Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.);
Department of Telemedicine, Northwestern University Feinberg School of Medicine,
Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York
University–Langone Medical Center, New York, NY (D.P.N.)
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Naser JA, Lee E, Pislaru SV, Tsaban G, Malins JG, Jackson JI, Anisuzzaman DM, Rostami B, Lopez-Jimenez F, Friedman PA, Kane GC, Pellikka PA, Attia ZI. Artificial intelligence-based classification of echocardiographic views. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:260-269. [PMID: 38774376 PMCID: PMC11104471 DOI: 10.1093/ehjdh/ztae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 05/24/2024]
Abstract
Aims Augmenting echocardiography with artificial intelligence would allow for automated assessment of routine parameters and identification of disease patterns not easily recognized otherwise. View classification is an essential first step before deep learning can be applied to the echocardiogram. Methods and results We trained two- and three-dimensional convolutional neural networks (CNNs) using transthoracic echocardiographic (TTE) studies obtained from 909 patients to classify nine view categories (10 269 videos). Transthoracic echocardiographic studies from 229 patients were used in internal validation (2582 videos). Convolutional neural networks were tested on 100 patients with comprehensive TTE studies (where the two examples chosen by CNNs as most likely to represent a view were evaluated) and 408 patients with five view categories obtained via point-of-care ultrasound (POCUS). The overall accuracy of the two-dimensional CNN was 96.8%, and the averaged area under the curve (AUC) was 0.997 on the comprehensive TTE testing set; these numbers were 98.4% and 0.998, respectively, on the POCUS set. For the three-dimensional CNN, the accuracy and AUC were 96.3% and 0.998 for full TTE studies and 95.0% and 0.996 on POCUS videos, respectively. The positive predictive value, which defined correctly identified predicted views, was higher with two-dimensional rather than three-dimensional networks, exceeding 93% in apical, short-axis aortic valve, and parasternal long-axis left ventricle views. Conclusion An automated view classifier utilizing CNNs was able to classify cardiac views obtained using TTE and POCUS with high accuracy. The view classifier will facilitate the application of deep learning to echocardiography.
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Affiliation(s)
- Jwan A Naser
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Eunjung Lee
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Sorin V Pislaru
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Gal Tsaban
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Jeffrey G Malins
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - John I Jackson
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - D M Anisuzzaman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Behrouz Rostami
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Francisco Lopez-Jimenez
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Garvan C Kane
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Patricia A Pellikka
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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4
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Jacobs C. Decoding pulmonary nodules: can machine learning enhance malignancy risk stratification? Thorax 2024; 79:293-294. [PMID: 38286616 DOI: 10.1136/thorax-2023-221300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/18/2024] [Indexed: 01/31/2024]
Affiliation(s)
- Colin Jacobs
- Medical Imaging, Radboudumc, Nijmegen, The Netherlands
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5
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Huang J, Xie S, Huang J, Zheng Z, Lin Z, Lin J, Tang K, Meng M, Zhao Y, Liao W, Liu C, Gu Y, Li S, Chen H, Chen R. Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images. J Thorac Dis 2024; 16:935-947. [PMID: 38505025 PMCID: PMC10944745 DOI: 10.21037/jtd-23-455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 09/08/2023] [Indexed: 03/21/2024]
Abstract
Background Pulmonary epithelioid hemangioendothelioma (PEH) is a rare vascular tumour, and its early diagnosis remains challenging. This study aims to comprehensively analyse the imaging features of PEH and develop a model for predicting PEH. Methods Retrospective and pooled analyses of imaging findings were performed in PEH patients at our center (n=25) and in published cases (n=71), respectively. Relevant computed tomography (CT) images were extracted and used to build a deep learning model for PEH identification and differentiation from other diseases. Results In this study, bilateral multiple nodules/masses (n=19) appeared to be more common with most nodules less than 2 cm. In addition to the common types and features, the pattern of mixed type (n=4) and isolated nodules (n=4), punctate calcifications (5/25) and lymph node enlargement were also observed (10/25). The presence of pleural effusion is associated with a poor prognosis in PEH. The deep learning model, with an area under the receiver operating characteristic curve (AUC) of 0.71 [95% confidence interval (CI): 0.69-0.72], has a differentiation accuracy of 100% and 74% for the training and test sets respectively. Conclusions This study confirmed the heterogeneity of the imaging findings in PEH and showed several previously undescribed types and features. The current deep learning model based on CT has potential for clinical application and needs to be further explored in the future.
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Affiliation(s)
- Junfeng Huang
- Department of Allergy and Clinical Immunology, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Shuojia Xie
- Department of Allergy and Clinical Immunology, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Nanshan School of Medicine, Guangzhou Medical University, Guangzhou, China
| | - Junjie Huang
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Department of Medical Imaging, Foshan Hospital of Traditional Chinese Medicine, Foshan, China
| | - Ziwen Zheng
- Department of Allergy and Clinical Immunology, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zikai Lin
- Department of Allergy and Clinical Immunology, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Nanshan School of Medicine, Guangzhou Medical University, Guangzhou, China
| | - Jinsheng Lin
- Department of Allergy and Clinical Immunology, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Kailun Tang
- Department of Allergy and Clinical Immunology, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Clinical Medical College of Henan University, Kaifeng, China
| | - Mingqiang Meng
- The School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, China
| | - Yulin Zhao
- Nanshan School of Medicine, Guangzhou Medical University, Guangzhou, China
| | - Wanzhe Liao
- Nanshan School of Medicine, Guangzhou Medical University, Guangzhou, China
| | - Chunping Liu
- Department of Allergy and Clinical Immunology, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yingying Gu
- Department of Allergy and Clinical Immunology, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Shiyue Li
- Department of Allergy and Clinical Immunology, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Huai Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Ruchong Chen
- Department of Allergy and Clinical Immunology, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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Grenier PA, Brun AL, Mellot F. [The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]. Rev Mal Respir 2024; 41:110-126. [PMID: 38129269 DOI: 10.1016/j.rmr.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.
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Affiliation(s)
- P A Grenier
- Délégation à la recherche clinique et l'innovation, hôpital Foch, Suresnes, France.
| | - A L Brun
- Service de radiologie, hôpital Foch, Suresnes, France
| | - F Mellot
- Service de radiologie, hôpital Foch, Suresnes, France
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7
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Cobanaj M, Corti C, Dee EC, McCullum L, Boldrini L, Schlam I, Tolaney SM, Celi LA, Curigliano G, Criscitiello C. Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow. Eur J Cancer 2024; 198:113504. [PMID: 38141549 PMCID: PMC11362966 DOI: 10.1016/j.ejca.2023.113504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 12/25/2023]
Abstract
Patient care workflows are highly multimodal and intertwined: the intersection of data outputs provided from different disciplines and in different formats remains one of the main challenges of modern oncology. Artificial Intelligence (AI) has the potential to revolutionize the current clinical practice of oncology owing to advancements in digitalization, database expansion, computational technologies, and algorithmic innovations that facilitate discernment of complex relationships in multimodal data. Within oncology, radiation therapy (RT) represents an increasingly complex working procedure, involving many labor-intensive and operator-dependent tasks. In this context, AI has gained momentum as a powerful tool to standardize treatment performance and reduce inter-observer variability in a time-efficient manner. This review explores the hurdles associated with the development, implementation, and maintenance of AI platforms and highlights current measures in place to address them. In examining AI's role in oncology workflows, we underscore that a thorough and critical consideration of these challenges is the only way to ensure equitable and unbiased care delivery, ultimately serving patients' survival and quality of life.
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Affiliation(s)
- Marisa Cobanaj
- National Center for Radiation Research in Oncology, OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Chiara Corti
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy.
| | - Edward C Dee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lucas McCullum
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Laura Boldrini
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Ilana Schlam
- Department of Hematology and Oncology, Tufts Medical Center, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sara M Tolaney
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Leo A Celi
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Carmen Criscitiello
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
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8
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Hendrix W, Hendrix N, Scholten ET, Mourits M, Trap-de Jong J, Schalekamp S, Korst M, van Leuken M, van Ginneken B, Prokop M, Rutten M, Jacobs C. Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans. COMMUNICATIONS MEDICINE 2023; 3:156. [PMID: 37891360 PMCID: PMC10611755 DOI: 10.1038/s43856-023-00388-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 10/12/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Outside a screening program, early-stage lung cancer is generally diagnosed after the detection of incidental nodules in clinically ordered chest CT scans. Despite the advances in artificial intelligence (AI) systems for lung cancer detection, clinical validation of these systems is lacking in a non-screening setting. METHOD We developed a deep learning-based AI system and assessed its performance for the detection of actionable benign nodules (requiring follow-up), small lung cancers, and pulmonary metastases in CT scans acquired in two Dutch hospitals (internal and external validation). A panel of five thoracic radiologists labeled all nodules, and two additional radiologists verified the nodule malignancy status and searched for any missed cancers using data from the national Netherlands Cancer Registry. The detection performance was evaluated by measuring the sensitivity at predefined false positive rates on a free receiver operating characteristic curve and was compared with the panel of radiologists. RESULTS On the external test set (100 scans from 100 patients), the sensitivity of the AI system for detecting benign nodules, primary lung cancers, and metastases is respectively 94.3% (82/87, 95% CI: 88.1-98.8%), 96.9% (31/32, 95% CI: 91.7-100%), and 92.0% (104/113, 95% CI: 88.5-95.5%) at a clinically acceptable operating point of 1 false positive per scan (FP/s). These sensitivities are comparable to or higher than the radiologists, albeit with a slightly higher FP/s (average difference of 0.6). CONCLUSIONS The AI system reliably detects benign and malignant pulmonary nodules in clinically indicated CT scans and can potentially assist radiologists in this setting.
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Affiliation(s)
- Ward Hendrix
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
- Radiology Department, Jeroen Bosch Hospital, Henri Dunantstraat 1, 5223 GZ, 's-Hertogenbosch, The Netherlands
| | - Nils Hendrix
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
- Radiology Department, Jeroen Bosch Hospital, Henri Dunantstraat 1, 5223 GZ, 's-Hertogenbosch, The Netherlands
- Jheronimus Academy of Data Science, Sint Janssingel 92, 5211 DA, 's-Hertogenbosch, The Netherlands
| | - Ernst T Scholten
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Mariëlle Mourits
- Radiology Department, Canisius Wilhelmina Hospital, Weg door Jonkerbos 100, 6532 SZ, Nijmegen, The Netherlands
| | - Joline Trap-de Jong
- Radiology Department, St. Antonius Hospital, Koekoekslaan 1, 3435 CM, Nieuwegein, The Netherlands
| | - Steven Schalekamp
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Mike Korst
- Radiology Department, Jeroen Bosch Hospital, Henri Dunantstraat 1, 5223 GZ, 's-Hertogenbosch, The Netherlands
| | - Maarten van Leuken
- Radiology Department, Canisius Wilhelmina Hospital, Weg door Jonkerbos 100, 6532 SZ, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Mathias Prokop
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
- Radiology Department, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Matthieu Rutten
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
- Radiology Department, Jeroen Bosch Hospital, Henri Dunantstraat 1, 5223 GZ, 's-Hertogenbosch, The Netherlands
| | - Colin Jacobs
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands.
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9
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Prosper AE, Kammer MN, Maldonado F, Aberle DR, Hsu W. Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization. Radiology 2023; 309:e222904. [PMID: 37815447 PMCID: PMC10623199 DOI: 10.1148/radiol.222904] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/23/2023] [Accepted: 03/30/2023] [Indexed: 10/11/2023]
Abstract
The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.
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Affiliation(s)
- Ashley Elizabeth Prosper
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Michael N. Kammer
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Fabien Maldonado
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Denise R. Aberle
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - William Hsu
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
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10
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Rösler W, Altenbuchinger M, Baeßler B, Beissbarth T, Beutel G, Bock R, von Bubnoff N, Eckardt JN, Foersch S, Loeffler CML, Middeke JM, Mueller ML, Oellerich T, Risse B, Scherag A, Schliemann C, Scholz M, Spang R, Thielscher C, Tsoukakis I, Kather JN. An overview and a roadmap for artificial intelligence in hematology and oncology. J Cancer Res Clin Oncol 2023; 149:7997-8006. [PMID: 36920563 PMCID: PMC10374829 DOI: 10.1007/s00432-023-04667-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 02/23/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals. METHODS In this article, we provide an expert-based consensus statement by the joint Working Group on "Artificial Intelligence in Hematology and Oncology" by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology. RESULTS First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology. CONCLUSION Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future.
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Affiliation(s)
- Wiebke Rösler
- Department for Medical Oncology and Hematology, University Hospital Zurich, Zurich, Switzerland
| | - Michael Altenbuchinger
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Tim Beissbarth
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Gernot Beutel
- Department for Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany
| | - Robert Bock
- IMMS Institute for Microelectronics and Mechatronics Systems GmbH (NPO), Ilmenau, Germany
| | - Nikolas von Bubnoff
- Department of Hematology and Oncology, Medical Center, University of Schleswig Holstein, Campus Lübeck, Lübeck, Germany
| | - Jan-Niklas Eckardt
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Chiara M L Loeffler
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Jan Moritz Middeke
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | | | - Thomas Oellerich
- Medizinische Klinik 2-Haematology/Oncology, University Hospital, Frankfurt am Main, Germany
| | - Benjamin Risse
- Computer Vision and Machine Learning Systems Group, Institute for Geoinformatics, University of Münster, Münster, Germany
| | - André Scherag
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital - Friedrich Schiller University, Jena, Germany
| | | | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Rainer Spang
- Department of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany
| | | | - Ioannis Tsoukakis
- Department of Hematology and Oncology, Sana Klinikum Offenbach, Offenbach, Germany
| | - Jakob Nikolas Kather
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
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Catarata MJ, Van Geffen WH, Banka R, Ferraz B, Sidhu C, Carew A, Viola L, Gijtenbeek R, Hardavella G. ERS International Congress 2022: highlights from the Thoracic Oncology Assembly. ERJ Open Res 2023; 9:00579-2022. [PMID: 37583965 PMCID: PMC10423989 DOI: 10.1183/23120541.00579-2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 05/31/2023] [Indexed: 08/17/2023] Open
Abstract
Thoracic malignancies are associated with a substantial public health burden. Lung cancer is the leading cause of cancer-related mortality worldwide, with significant impact on patients' quality of life. Following 2 years of virtual European Respiratory Society (ERS) Congresses due to the COVID-19 pandemic, the 2022 hybrid ERS Congress in Barcelona, Spain allowed peers from all over the world to meet again and present their work. Thoracic oncology experts presented best practices and latest developments in lung cancer screening, lung cancer diagnosis and management. Early lung cancer diagnosis, subsequent pros and cons of aggressive management, identification and management of systemic treatments' side-effects, and the application of artificial intelligence and biomarkers across all aspects of the thoracic oncology pathway were among the areas that triggered specific interest and will be summarised here.
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Affiliation(s)
- Maria Joana Catarata
- Pulmonology Department, Hospital de Braga, Braga, Portugal
- Tumour & Microenvironment Interactions Group, I3S-Institute for Health Research & Innovation, University of Porto, Porto, Portugal
| | - Wouter H. Van Geffen
- Department of Respiratory Medicine, Medical Center Leeuwarden, Leeuwarden, The Netherlands
| | - Radhika Banka
- P.D. Hinduja National Hospital and Medical Research Centre, Mumbai, India
| | - Beatriz Ferraz
- Pulmonology Department, Centro Hospitalar e Universitário do Porto, Porto, Portugal
- ICBAS School of Medicine and Biomedical Sciences, University of Porto, Porto, Portugal
| | | | - Alan Carew
- Queensland Lung Transplant Service, Department of Thoracic Medicine, Prince Charles Hospital, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Lucia Viola
- Thoracic Oncology Service, Fundación Neumológica Colombiana, Bogotá, Colombia
- Thoracic Clinic, Luis Carlos Sarmiento Angulo Cancer Treatment and Research Center (Fundación CTIC), Bogotá, Colombia
| | - Rolof Gijtenbeek
- Department of Respiratory Medicine, Medical Center Leeuwarden, Leeuwarden, The Netherlands
| | - Georgia Hardavella
- 9th Department of Respiratory Medicine, “Sotiria” Athens Chest Diseases Hospital, Athens, Greece
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12
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Madani MH, Riess JW, Brown LM, Cooke DT, Guo HH. Imaging of lung cancer. Curr Probl Cancer 2023:100966. [PMID: 37316337 DOI: 10.1016/j.currproblcancer.2023.100966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/29/2023] [Accepted: 05/23/2023] [Indexed: 06/16/2023]
Abstract
Lung cancer is the leading cause of cancer-related mortality globally. Imaging is essential in the screening, diagnosis, staging, response assessment, and surveillance of patients with lung cancer. Subtypes of lung cancer can have distinguishing imaging appearances. The most frequently used imaging modalities include chest radiography, computed tomography, magnetic resonance imaging, and positron emission tomography. Artificial intelligence algorithms and radiomics are emerging technologies with potential applications in lung cancer imaging.
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Affiliation(s)
- Mohammad H Madani
- Department of Radiology, University of California, Davis, Sacramento, CA.
| | - Jonathan W Riess
- Division of Hematology/Oncology, Department of Internal Medicine, UC Davis Medical Center, UC Davis Comprehensive Cancer Center, Sacramento, CA
| | - Lisa M Brown
- Division of General Thoracic Surgery, Department of Surgery, UC Davis Health, Sacramento, CA
| | - David T Cooke
- Division of General Thoracic Surgery, Department of Surgery, UC Davis Health, Sacramento, CA
| | - H Henry Guo
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
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13
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Mankidy BJ, Mohammad G, Trinh K, Ayyappan AP, Huang Q, Bujarski S, Jafferji MS, Ghanta R, Hanania AN, Lazarus DR. High risk lung nodule: A multidisciplinary approach to diagnosis and management. Respir Med 2023; 214:107277. [PMID: 37187432 DOI: 10.1016/j.rmed.2023.107277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 04/28/2023] [Accepted: 05/04/2023] [Indexed: 05/17/2023]
Abstract
Pulmonary nodules are often discovered incidentally during CT scans performed for other reasons. While the vast majority of nodules are benign, a small percentage may represent early-stage lung cancer with the potential for curative treatments. With the growing use of CT for both clinical purposes and lung cancer screening, the number of pulmonary nodules detected is expected to increase substantially. Despite well-established guidelines, many nodules do not receive proper evaluation due to a variety of factors, including inadequate coordination of care and financial and social barriers. To address this quality gap, novel approaches such as multidisciplinary nodule clinics and multidisciplinary boards may be necessary. As pulmonary nodules may indicate early-stage lung cancer, it is crucial to adopt a risk-stratified approach to identify potential lung cancers at an early stage, while minimizing the risk of harm and expense associated with over investigation of low-risk nodules. This article, authored by multiple specialists involved in nodule management, delves into the diagnostic approach to lung nodules. It covers the process of determining whether a patient requires tissue sampling or continued surveillance. Additionally, the article provides an in-depth examination of the various biopsy and therapeutic options available for malignant lung nodules. The article also emphasizes the significance of early detection in reducing lung cancer mortality, especially among high-risk populations. Furthermore, it addresses the creation of a comprehensive lung nodule program, which involves smoking cessation, lung cancer screening, and systematic evaluation and follow-up of both incidental and screen-detected nodules.
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Affiliation(s)
- Babith J Mankidy
- Division of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, 1Baylor Plaza, Houston, TX, 77030, USA.
| | - GhasemiRad Mohammad
- Department of Radiology, Division of Vascular and Interventional Radiology, Baylor College of Medicine, USA.
| | - Kelly Trinh
- Texas Tech University Health Sciences Center, School of Medicine, USA.
| | - Anoop P Ayyappan
- Department of Radiology, Division of Thoracic Radiology, Baylor College of Medicine, USA.
| | - Quillan Huang
- Department of Oncology, Baylor College of Medicine, USA.
| | - Steven Bujarski
- Division of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, 1Baylor Plaza, Houston, TX, 77030, USA.
| | | | - Ravi Ghanta
- Department of Cardiothoracic Surgery, Baylor College of Medicine, USA.
| | | | - Donald R Lazarus
- Division of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, 1Baylor Plaza, Houston, TX, 77030, USA.
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14
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Paudyal R, Shah AD, Akin O, Do RKG, Konar AS, Hatzoglou V, Mahmood U, Lee N, Wong RJ, Banerjee S, Shin J, Veeraraghavan H, Shukla-Dave A. Artificial Intelligence in CT and MR Imaging for Oncological Applications. Cancers (Basel) 2023; 15:cancers15092573. [PMID: 37174039 PMCID: PMC10177423 DOI: 10.3390/cancers15092573] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Akash D Shah
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Amaresha Shridhar Konar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Usman Mahmood
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Nancy Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Richard J Wong
- Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | | | | | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
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15
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Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions. Diagnostics (Basel) 2023; 13:diagnostics13050968. [PMID: 36900112 PMCID: PMC10000509 DOI: 10.3390/diagnostics13050968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/11/2023] [Accepted: 02/18/2023] [Indexed: 03/08/2023] Open
Abstract
CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.
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16
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de Margerie-Mellon C, Chassagnon G. Artificial intelligence: A critical review of applications for lung nodule and lung cancer. Diagn Interv Imaging 2023; 104:11-17. [PMID: 36513593 DOI: 10.1016/j.diii.2022.11.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent years, the growth of deep learning, a successful technique for computer vision tasks that does not require explicit programming, coupled with the availability of large imaging databases fostered the development of multiple applications in the medical imaging field, especially for lung nodules and lung cancer, mostly through convolutional neural networks (CNN). Some of the first applications of AI is this field were dedicated to automated detection of lung nodules on X-ray and computed tomography (CT) examinations, with performances now reaching or exceeding those of radiologists. For lung nodule segmentation, CNN-based algorithms applied to CT images show excellent spatial overlap index with manual segmentation, even for irregular and ground glass nodules. A third application of AI is the classification of lung nodules between malignant and benign, which could limit the number of follow-up CT examinations for less suspicious lesions. Several algorithms have demonstrated excellent capabilities for the prediction of the malignancy risk when a nodule is discovered. These different applications of AI for lung nodules are particularly appealing in the context of lung cancer screening. In the field of lung cancer, AI tools applied to lung imaging have been investigated for distinct aims. First, they could play a role for the non-invasive characterization of tumors, especially for histological subtype and somatic mutation predictions, with a potential therapeutic impact. Additionally, they could help predict the patient prognosis, in combination to clinical data. Despite these encouraging perspectives, clinical implementation of AI tools is only beginning because of the lack of generalizability of published studies, of an inner obscure working and because of limited data about the impact of such tools on the radiologists' decision and on the patient outcome. Radiologists must be active participants in the process of evaluating AI tools, as such tools could support their daily work and offer them more time for high added value tasks.
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Affiliation(s)
- Constance de Margerie-Mellon
- Université Paris Cité, Laboratory of Imaging Biomarkers, Center for Research on Inflammation, UMR 1149, INSERM, 75018 Paris, France; Department of Radiology, Hôpital Saint-Louis APHP, 75010 Paris, France
| | - Guillaume Chassagnon
- Université Paris Cité, Faculté de Médecine, 75006 Paris, France; Department of Radiology, Hôpital Cochin APHP, 75014 Paris, France
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17
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Vliegenthart R, Fouras A, Jacobs C, Papanikolaou N. Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry. Respirology 2022; 27:818-833. [PMID: 35965430 PMCID: PMC9546393 DOI: 10.1111/resp.14344] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 07/08/2022] [Indexed: 12/11/2022]
Abstract
In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of ‘non visual’ markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID‐19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x‐ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra‐low‐dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon‐counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X‐ray velocimetry integrates x‐ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation. See relatedEditorial
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Affiliation(s)
- Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.,Data Science in Health (DASH), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Colin Jacobs
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nickolas Papanikolaou
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.,AI Hub, The Royal Marsden NHS Foundation Trust, London, UK.,The Institute of Cancer Research, London, UK
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18
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Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis. Cancers (Basel) 2022; 14:cancers14163856. [PMID: 36010850 PMCID: PMC9405626 DOI: 10.3390/cancers14163856] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 07/30/2022] [Accepted: 08/04/2022] [Indexed: 12/19/2022] Open
Abstract
We conducted a systematic review and meta-analysis of the diagnostic performance of current deep learning algorithms for the diagnosis of lung cancer. We searched major databases up to June 2022 to include studies that used artificial intelligence to diagnose lung cancer, using the histopathological analysis of true positive cases as a reference. The quality of the included studies was assessed independently by two authors based on the revised Quality Assessment of Diagnostic Accuracy Studies. Six studies were included in the analysis. The pooled sensitivity and specificity were 0.93 (95% CI 0.85−0.98) and 0.68 (95% CI 0.49−0.84), respectively. Despite the significantly high heterogeneity for sensitivity (I2 = 94%, p < 0.01) and specificity (I2 = 99%, p < 0.01), most of it was attributed to the threshold effect. The pooled SROC curve with a bivariate approach yielded an area under the curve (AUC) of 0.90 (95% CI 0.86 to 0.92). The DOR for the studies was 26.7 (95% CI 19.7−36.2) and heterogeneity was 3% (p = 0.40). In this systematic review and meta-analysis, we found that when using the summary point from the SROC, the pooled sensitivity and specificity of DL algorithms for the diagnosis of lung cancer were 93% and 68%, respectively.
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19
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Nadkarni P, Merchant SA. Enhancing medical-imaging artificial intelligence through holistic use of time-tested key imaging and clinical parameters: Future insights. Artif Intell Med Imaging 2022; 3:55-69. [DOI: 10.35711/aimi.v3.i3.55] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/12/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023] Open
Abstract
Much of the published literature in Radiology-related Artificial Intelligence (AI) focuses on single tasks, such as identifying the presence or absence or severity of specific lesions. Progress comparable to that achieved for general-purpose computer vision has been hampered by the unavailability of large and diverse radiology datasets containing different types of lesions with possibly multiple kinds of abnormalities in the same image. Also, since a diagnosis is rarely achieved through an image alone, radiology AI must be able to employ diverse strategies that consider all available evidence, not just imaging information. Using key imaging and clinical signs will help improve their accuracy and utility tremendously. Employing strategies that consider all available evidence will be a formidable task; we believe that the combination of human and computer intelligence will be superior to either one alone. Further, unless an AI application is explainable, radiologists will not trust it to be either reliable or bias-free; we discuss some approaches aimed at providing better explanations, as well as regulatory concerns regarding explainability (“transparency”). Finally, we look at federated learning, which allows pooling data from multiple locales while maintaining data privacy to create more generalizable and reliable models, and quantum computing, still prototypical but potentially revolutionary in its computing impact.
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Affiliation(s)
- Prakash Nadkarni
- College of Nursing, University of Iowa, Iowa City, IA 52242, United States
| | - Suleman Adam Merchant
- Department of Radiology, LTM Medical College & LTM General Hospital, Mumbai 400022, Maharashtra, India
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20
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Diao K, Chen Y, Liu Y, Chen BJ, Li WJ, Zhang L, Qu YL, Zhang T, Zhang Y, Wu M, Li K, Song B. Diagnostic study on clinical feasibility of an AI-based diagnostic system as a second reader on mobile CT images: a preliminary result. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:668. [PMID: 35845492 PMCID: PMC9279799 DOI: 10.21037/atm-22-2157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/06/2022] [Indexed: 02/05/2023]
Abstract
Background Artificial intelligence (AI) has breathed new life into the lung nodules detection and diagnosis. However, whether the output information from AI will translate into benefits for clinical workflow or patient outcomes in a real-world setting remains unknown. This study was to demonstrate the feasibility of an AI-based diagnostic system deployed as a second reader in imaging interpretation for patients screened for pulmonary abnormalities in a clinical setting. Methods The study included patients from a lung cancer screening program conducted in Sichuan Province, China using a mobile computed tomography (CT) scanner which traveled to medium-size cities between July 10th, 2020 and September 10th, 2020. Cases that were suspected to have malignant nodules by junior radiologists, senior radiologists or AI were labeled a high risk (HR) tag as HR-junior, HR-senior and HR-AI, respectively, and included into final analysis. The diagnosis efficacy of the AI was evaluated by calculating negative predictive value and positive predictive value when referring to the senior readers’ final results as the gold standard. Besides, characteristics of the lesions were compared among cases with different HR labels. Results In total, 251/3,872 patients (6.48%, male/female: 91/160, median age, 66 years) with HR lung nodules were included. The AI algorithm achieved a negative predictive value of 88.2% [95% confidence interval (CI): 62.2–98.0%] and a positive predictive value of 55.6% (95% CI: 49.0–62.0%). The diagnostic duration was significantly reduced when AI was used as a second reader (223±145.6 vs. 270±143.17 s, P<0.001). The information yielded by AI affected the radiologist’s decision-making in 35/145 cases. Lesions of HR cases had a higher volume [309.9 (214.9–732.5) vs. 141.3 (79.3–380.8) mm3, P<0.001], lower average CT number [−511.0 (−576.5 to −100.5) vs. −191.5 (−487.3 to 22.5), P=0.010], and pure ground glass opacity rather than solid. Conclusions The AI algorithm had high negative predictive value but low positive predictive value in diagnosing HR lung lesions in a clinical setting. Deploying AI as a second reader could help avoid missed diagnoses, reduce diagnostic duration, and strengthen diagnostic confidence for radiologists.
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Affiliation(s)
- Kaiyue Diao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ying Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bo-Jiang Chen
- Department of Respiratory Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Wan-Jiang Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Lin Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ya-Li Qu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yun Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Min Wu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Huaxi MR Research Center, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, Sanya People's Hospital (West China Sanya Hospital of Sichuan University), Chengdu, China
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21
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Ni S, Li X, Yi X. Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7069348. [PMID: 35388316 PMCID: PMC8979681 DOI: 10.1155/2022/7069348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 02/23/2022] [Accepted: 02/28/2022] [Indexed: 11/17/2022]
Abstract
Adolescents and children worldwide are threatened by osteosarcoma, a tumor that predominantly affects the long bone epiphysis. Osteosarcoma is the most common and highly malignant bone tumor in youngsters. Early tumor detection is the key to effective treatment of this disease. The discovery of biomarkers and the growing understanding of molecules and their complex interactions have improved the outcome of clinical trials in osteosarcoma. This article describes biomarkers of osteosarcoma with the aim of positively influencing the progress of clinical treatment of osteosarcoma. Femoral bone tumor is a typical condition of osteosarcoma. Due to the wide range of femoral stem types, complexities in the distal femur, and tumors in the rotor part of femur, physicians following the traditional clinical approach face difficulties in removing the lesion and fixing the femur with resection of the tumor segment. In this paper, the effect of small doses of different concentrations of lidocaine anesthesia in patients undergoing lumpectomy for osteosarcoma femoral tumor segments is investigated. A computer-based artificial intelligence method for automated determination of different concentration levels of lidocaine anesthesia and amputation of osteosarcoma femoral tumor segment is proposed. Statistical analysis is carried on the empirical data including intraoperative bleeding, intraoperative and postoperative pain scores, surgical operation time, postoperative complications, patient satisfaction, and local anesthetic dose. The results showed that the patients in the study group had low intraoperative bleeding, short operation time, low postoperative hematoma formation rate, high patient satisfaction, higher dosage of anesthetic solution, and low dosage of lidocaine. Results revealed that mean arterial pressure and heart rate in extubating and intubating were significantly lower in the observation group than in the control group, and a significant difference (P < 0.05) was observed between the two groups. This proves that the proposed algorithm can adequately reduce bleeding, alleviate postoperative pain, shorten operation time, reduce complications, accelerate recovery, and ensure better treatment results.
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Affiliation(s)
- Shuqin Ni
- Department of Anesthesiology, Yantaishan Hospital, Yantai 264003, Shandong, China
| | - Xin Li
- Department of Surgery, Jinyintan Hospital, Wuhan, Hubei 430022, China
| | - Xiuna Yi
- Department of Anesthesiology, Yantaishan Hospital, Yantai 264003, Shandong, China
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