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Fransen SJ, Kwee TC, Rouw D, Roest C, van Lohuizen QY, Simonis FFJ, van Leeuwen PJ, Heijmink S, Ongena YP, Haan M, Yakar D. Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI. Eur Radiol 2024:10.1007/s00330-024-11012-y. [PMID: 39143247 DOI: 10.1007/s00330-024-11012-y] [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: 03/21/2024] [Revised: 07/17/2024] [Accepted: 07/23/2024] [Indexed: 08/16/2024]
Abstract
OBJECTIVES This study investigated patients' acceptance of artificial intelligence (AI) for diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI diagnoses. MATERIALS AND METHODS A prospective, multicenter study was conducted between January and November 2023. Patients undergoing prostate MRI were surveyed about their opinions on hypothetical AI assessment of their MRI scans. The questionnaire included nine items: four on hypothetical scenarios of combinations between AI and the radiologist, two on trust in the diagnosis, and three on accountability for misdiagnosis. Relationships between the items and independent variables were assessed using multivariate analysis. RESULTS A total of 212 PCa suspicious patients undergoing prostate MRI were included. The majority preferred AI involvement in their PCa diagnosis alongside a radiologist, with 91% agreeing with AI as the primary reader and 79% as the secondary reader. If AI has a high certainty diagnosis, 15% of the respondents would accept it as the sole decision-maker. Autonomous AI outperforming radiologists would be accepted by 52%. Higher educated persons tended to accept AI when it would outperform radiologists (p < 0.05). The respondents indicated that the hospital (76%), radiologist (70%), and program developer (55%) should be held accountable for misdiagnosis. CONCLUSIONS Patients favor AI involvement alongside radiologists in PCa diagnosis. Trust in AI diagnosis depends on the patient's education level and the AI performance, with autonomous AI acceptance by a small majority on the condition that AI outperforms a radiologist. Respondents held the hospital, radiologist, and program developers accountable for misdiagnosis in descending order of accountability. CLINICAL RELEVANCE STATEMENT Patients show a high level of acceptance for AI-assisted prostate cancer diagnosis on MRI, either alongside radiologists or fully autonomous, particularly if it demonstrates superior performance to radiologists alone. KEY POINTS Prostate cancer suspicious patients may accept autonomous AI based on performance. Patients prefer AI involvement alongside a radiologist in diagnosing prostate cancer. Patients indicate accountability for AI should be shared among multiple stakeholders.
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Affiliation(s)
| | - T C Kwee
- University Medical Center Groningen, Groningen, Netherlands
| | - D Rouw
- Martini Hospital, Groningen, Netherlands
| | - C Roest
- University Medical Center Groningen, Groningen, Netherlands
| | | | | | | | - S Heijmink
- Dutch Cancer Institute, Amsterdam, Netherlands
| | - Y P Ongena
- University of Groningen, Groningen, Netherlands
| | - M Haan
- University of Groningen, Groningen, Netherlands
| | - D Yakar
- University Medical Center Groningen, Groningen, Netherlands
- Dutch Cancer Institute, Amsterdam, Netherlands
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Tsuboyama T, Yanagawa M, Fujioka T, Fujita S, Ueda D, Ito R, Yamada A, Fushimi Y, Tatsugami F, Nakaura T, Nozaki T, Kamagata K, Matsui Y, Hirata K, Fujima N, Kawamura M, Naganawa S. Recent trends in AI applications for pelvic MRI: a comprehensive review. LA RADIOLOGIA MEDICA 2024:10.1007/s11547-024-01861-4. [PMID: 39096356 DOI: 10.1007/s11547-024-01861-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 07/25/2024] [Indexed: 08/05/2024]
Abstract
Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI.
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Affiliation(s)
- Takahiro Tsuboyama
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe-City, Hyogo, 650-0017, Japan.
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, 565-0871, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan
| | - Shohei Fujita
- Department of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Daiju Ueda
- Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Akira Yamada
- Medical Data Science Course, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-0016, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N15, W5, Kita-ku, Sapporo, 060-8638, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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Cai JC, Nakai H, Kuanar S, Froemming AT, Bolan CW, Kawashima A, Takahashi H, Mynderse LA, Dora CD, Humphreys MR, Korfiatis P, Rouzrokh P, Bratt AK, Conte GM, Erickson BJ, Takahashi N. Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI. Radiology 2024; 312:e232635. [PMID: 39105640 DOI: 10.1148/radiol.232635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Abstract
Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience and interobserver variability. In contrast, deep learning (DL) produces deterministic outputs. Purpose To develop a DL model to predict the presence of csPCa by using patient-level labels without information about tumor location and to compare its performance with that of radiologists. Materials and Methods Data from patients without known csPCa who underwent MRI from January 2017 to December 2019 at one of multiple sites of a single academic institution were retrospectively reviewed. A convolutional neural network was trained to predict csPCa from T2-weighted images, diffusion-weighted images, apparent diffusion coefficient maps, and T1-weighted contrast-enhanced images. The reference standard was pathologic diagnosis. Radiologist performance was evaluated as follows: Radiology reports were used for the internal test set, and four radiologists' PI-RADS ratings were used for the external (ProstateX) test set. The performance was compared using areas under the receiver operating characteristic curves (AUCs) and the DeLong test. Gradient-weighted class activation maps (Grad-CAMs) were used to show tumor localization. Results Among 5735 examinations in 5215 patients (mean age, 66 years ± 8 [SD]; all male), 1514 examinations (1454 patients) showed csPCa. In the internal test set (400 examinations), the AUC was 0.89 and 0.89 for the DL classifier and radiologists, respectively (P = .88). In the external test set (204 examinations), the AUC was 0.86 and 0.84 for the DL classifier and radiologists, respectively (P = .68). DL classifier plus radiologists had an AUC of 0.89 (P < .001). Grad-CAMs demonstrated activation over the csPCa lesion in 35 of 38 and 56 of 58 true-positive examinations in internal and external test sets, respectively. Conclusion The performance of a DL model was not different from that of radiologists in the detection of csPCa at MRI, and Grad-CAMs localized the tumor. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Johnson and Chandarana in this issue.
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Affiliation(s)
- Jason C Cai
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Hirotsugu Nakai
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Shiba Kuanar
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Adam T Froemming
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Candice W Bolan
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Akira Kawashima
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Hiroaki Takahashi
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Lance A Mynderse
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Chandler D Dora
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Mitchell R Humphreys
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Panagiotis Korfiatis
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Pouria Rouzrokh
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Alexander K Bratt
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Gian Marco Conte
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Bradley J Erickson
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
| | - Naoki Takahashi
- From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz
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Roest C, Yakar D, Rener Sitar DI, Bosma JS, Rouw DB, Fransen SJ, Huisman H, Kwee TC. Multimodal AI Combining Clinical and Imaging Inputs Improves Prostate Cancer Detection. Invest Radiol 2024:00004424-990000000-00236. [PMID: 39074400 DOI: 10.1097/rli.0000000000001102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
OBJECTIVES Deep learning (DL) studies for the detection of clinically significant prostate cancer (csPCa) on magnetic resonance imaging (MRI) often overlook potentially relevant clinical parameters such as prostate-specific antigen, prostate volume, and age. This study explored the integration of clinical parameters and MRI-based DL to enhance diagnostic accuracy for csPCa on MRI. MATERIALS AND METHODS We retrospectively analyzed 932 biparametric prostate MRI examinations performed for suspected csPCa (ISUP ≥2) at 2 institutions. Each MRI scan was automatically analyzed by a previously developed DL model to detect and segment csPCa lesions. Three sets of features were extracted: DL lesion suspicion levels, clinical parameters (prostate-specific antigen, prostate volume, age), and MRI-based lesion volumes for all DL-detected lesions. Six multimodal artificial intelligence (AI) classifiers were trained for each combination of feature sets, employing both early (feature-level) and late (decision-level) information fusion methods. The diagnostic performance of each model was tested internally on 20% of center 1 data and externally on center 2 data (n = 529). Receiver operating characteristic comparisons determined the optimal feature combination and information fusion method and assessed the benefit of multimodal versus unimodal analysis. The optimal model performance was compared with a radiologist using PI-RADS. RESULTS Internally, the multimodal AI integrating DL suspicion levels with clinical features via early fusion achieved the highest performance. Externally, it surpassed baselines using clinical parameters (0.77 vs 0.67 area under the curve [AUC], P < 0.001) and DL suspicion levels alone (AUC: 0.77 vs 0.70, P = 0.006). Early fusion outperformed late fusion in external data (0.77 vs 0.73 AUC, P = 0.005). No significant performance gaps were observed between multimodal AI and radiologist assessments (internal: 0.87 vs 0.88 AUC; external: 0.77 vs 0.75 AUC, both P > 0.05). CONCLUSIONS Multimodal AI (combining DL suspicion levels and clinical parameters) outperforms clinical and MRI-only AI for csPCa detection. Early information fusion enhanced AI robustness in our multicenter setting. Incorporating lesion volumes did not enhance diagnostic efficacy.
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Affiliation(s)
- Christian Roest
- From the Department of Radiology, Medical Imaging Center, University Medical Center Groningen, Groningen, the Netherlands (C.R., D.Y., D.I.R.S., S.J.F., T.C.K.); Department of Radiology, Netherlands Cancer Center Antoni van Leeuwenhoek, Amsterdam, the Netherlands (D.Y.); Department of Radiology, Radboud University Medical Center, Nijmegen, the Netherlands (J.S.B., H.H.); and Department of Radiology, Martini Ziekenhuis Groningen, Groningen, the Netherlands (D.B.R.)
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Schmidt B, Soerensen SJC, Bhambhvani HP, Fan RE, Bhattacharya I, Choi MH, Kunder CA, Kao CS, Higgins J, Rusu M, Sonn GA. External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens. BJU Int 2024. [PMID: 38989669 DOI: 10.1111/bju.16464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
OBJECTIVES To externally validate the performance of the DeepDx Prostate artificial intelligence (AI) algorithm (Deep Bio Inc., Seoul, South Korea) for Gleason grading on whole-mount prostate histopathology, considering potential variations observed when applying AI models trained on biopsy samples to radical prostatectomy (RP) specimens due to inherent differences in tissue representation and sample size. MATERIALS AND METHODS The commercially available DeepDx Prostate AI algorithm is an automated Gleason grading system that was previously trained using 1133 prostate core biopsy images and validated on 700 biopsy images from two institutions. We assessed the AI algorithm's performance, which outputs Gleason patterns (3, 4, or 5), on 500 1-mm2 tiles created from 150 whole-mount RP specimens from a third institution. These patterns were then grouped into grade groups (GGs) for comparison with expert pathologist assessments. The reference standard was the International Society of Urological Pathology GG as established by two experienced uropathologists with a third expert to adjudicate discordant cases. We defined the main metric as the agreement with the reference standard, using Cohen's kappa. RESULTS The agreement between the two experienced pathologists in determining GGs at the tile level had a quadratically weighted Cohen's kappa of 0.94. The agreement between the AI algorithm and the reference standard in differentiating cancerous vs non-cancerous tissue had an unweighted Cohen's kappa of 0.91. Additionally, the AI algorithm's agreement with the reference standard in classifying tiles into GGs had a quadratically weighted Cohen's kappa of 0.89. In distinguishing cancerous vs non-cancerous tissue, the AI algorithm achieved a sensitivity of 0.997 and specificity of 0.88; in classifying GG ≥2 vs GG 1 and non-cancerous tissue, it demonstrated a sensitivity of 0.98 and specificity of 0.85. CONCLUSION The DeepDx Prostate AI algorithm had excellent agreement with expert uropathologists and performance in cancer identification and grading on RP specimens, despite being trained on biopsy specimens from an entirely different patient population.
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Affiliation(s)
- Bogdana Schmidt
- Division of Urology, Department of Surgery, Huntsman Cancer Hospital, University of Utah, Salt Lake City, UT, USA
| | - Simon John Christoph Soerensen
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Hriday P Bhambhvani
- Department of Urology, Weill Cornell Medical College, New York-Presbyterian Hospital, New York, NY, USA
| | - Richard E Fan
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Indrani Bhattacharya
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Moon Hyung Choi
- Department of Radiology, College of Medicine, Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, Korea
| | - Christian A Kunder
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Chia-Sui Kao
- Department of Pathology and Laboratory Medicine, Cleveland Clinic, Cleveland, OH, USA
| | - John Higgins
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Mirabela Rusu
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Geoffrey A Sonn
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
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6
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Fransen SJ, Roest C, Van Lohuizen QY, Bosma JS, Simonis FFJ, Kwee TC, Yakar D, Huisman H. Using deep learning to optimize the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences. Eur J Radiol 2024; 175:111470. [PMID: 38640822 DOI: 10.1016/j.ejrad.2024.111470] [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: 01/05/2024] [Revised: 03/29/2024] [Accepted: 04/14/2024] [Indexed: 04/21/2024]
Abstract
PURPOSE To explore diagnostic deep learning for optimizing the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences. METHOD This retrospective study included 840 patients with a biparametric prostate MRI scan. The MRI protocol included a T2-weighted image, three DWI sequences (b50, b400, and b800 s/mm2), a calculated ADC map, and a calculated b1400 sequence. Two accelerated MRI protocols were simulated, using only two acquired b-values to calculate the ADC and b1400. Deep learning models were trained to detect prostate cancer lesions on accelerated and full protocols. The diagnostic performances of the protocols were compared on the patient-level with the area under the receiver operating characteristic (AUROC), using DeLong's test, and on the lesion-level with the partial area under the free response operating characteristic (pAUFROC), using a permutation test. Validation of the results was performed among expert radiologists. RESULTS No significant differences in diagnostic performance were found between the accelerated protocols and the full bpMRI baseline. Omitting b800 reduced 53% DWI scan time, with a performance difference of + 0.01 AUROC (p = 0.20) and -0.03 pAUFROC (p = 0.45). Omitting b400 reduced 32% DWI scan time, with a performance difference of -0.01 AUROC (p = 0.65) and + 0.01 pAUFROC (p = 0.73). Multiple expert radiologists underlined the findings. CONCLUSIONS This study shows that deep learning can assess the diagnostic efficacy of MRI sequences by comparing prostate MRI protocols on diagnostic accuracy. Omitting either the b400 or the b800 DWI sequence can optimize the prostate MRI protocol by reducing scan time without compromising diagnostic quality.
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Affiliation(s)
- Stefan J Fransen
- University Medical Centre Groningen, Department of Radiology, Hanzeplein 1, 9713 GZ, Groningen, the Netherlands.
| | - Christian Roest
- University Medical Centre Groningen, Department of Radiology, Hanzeplein 1, 9713 GZ, Groningen, the Netherlands
| | - Quintin Y Van Lohuizen
- University Medical Centre Groningen, Department of Radiology, Hanzeplein 1, 9713 GZ, Groningen, the Netherlands
| | - Joeran S Bosma
- University Medical Centre Nijmegen, DIAG, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands
| | - Frank F J Simonis
- Technical University Twente, TechMed Centre, Hallenweg 5, 7522 NH, Enschede, the Netherlands
| | - Thomas C Kwee
- University Medical Centre Groningen, Department of Radiology, Hanzeplein 1, 9713 GZ, Groningen, the Netherlands
| | - Derya Yakar
- University Medical Centre Groningen, Department of Radiology, Hanzeplein 1, 9713 GZ, Groningen, the Netherlands
| | - Henkjan Huisman
- University Medical Centre Nijmegen, DIAG, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands
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7
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Rodrigues NM, Almeida JGD, Verde ASC, Gaivão AM, Bilreiro C, Santiago I, Ip J, Belião S, Moreno R, Matos C, Vanneschi L, Tsiknakis M, Marias K, Regge D, Silva S, Papanikolaou N. Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data. Comput Biol Med 2024; 171:108216. [PMID: 38442555 DOI: 10.1016/j.compbiomed.2024.108216] [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: 12/12/2023] [Revised: 02/09/2024] [Accepted: 02/25/2024] [Indexed: 03/07/2024]
Abstract
Despite being one of the most prevalent forms of cancer, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Computational methods can help make this detection process considerably faster and more robust. However, some modern machine-learning approaches require accurate segmentation of the prostate gland and the index lesion. Since performing manual segmentations is a very time-consuming task, and highly prone to inter-observer variability, there is a need to develop robust semi-automatic segmentation models. In this work, we leverage the large and highly diverse ProstateNet dataset, which includes 638 whole gland and 461 lesion segmentation masks, from 3 different scanner manufacturers provided by 14 institutions, in addition to other 3 independent public datasets, to train accurate and robust segmentation models for the whole prostate gland, zones and lesions. We show that models trained on large amounts of diverse data are better at generalizing to data from other institutions and obtained with other manufacturers, outperforming models trained on single-institution single-manufacturer datasets in all segmentation tasks. Furthermore, we show that lesion segmentation models trained on ProstateNet can be reliably used as lesion detection models.
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Affiliation(s)
- Nuno Miguel Rodrigues
- Computational Clinical Imaging Group, Champalimaud Foundation, Portugal; LASIGE, Faculty of Sciences, University of Lisbon, Portugal.
| | | | | | - Ana Mascarenhas Gaivão
- Radiology Department, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal
| | - Carlos Bilreiro
- Radiology Department, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal
| | - Inês Santiago
- Radiology Department, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal
| | - Joana Ip
- Radiology Department, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal
| | - Sara Belião
- Radiology Department, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal
| | - Raquel Moreno
- Computational Clinical Imaging Group, Champalimaud Foundation, Portugal
| | - Celso Matos
- Computational Clinical Imaging Group, Champalimaud Foundation, Portugal
| | - Leonardo Vanneschi
- NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
| | - Manolis Tsiknakis
- Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), GR 700 13, Heraklion, Greece; Department of Electrical and Computer Engineering, Hellenic Mediterranean University, GR 710 04, Heraklion, Greece
| | - Kostas Marias
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, GR 710 04, Heraklion, Greece; Computational BioMedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece
| | - Daniele Regge
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 Km 3.95, Candiolo, Turin 10060, Italy; Department of Surgical Sciences, University of Turin, Turin 10124, Italy
| | - Sara Silva
- LASIGE, Faculty of Sciences, University of Lisbon, Portugal
| | - Nickolas Papanikolaou
- Computational Clinical Imaging Group, Champalimaud Foundation, Portugal; Department of Radiology, Royal Marsden Hospital, Sutton, UK
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8
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Yang E, Shankar K, Kumar S, Seo C, Moon I. Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images. Biomedicines 2023; 11:3200. [PMID: 38137421 PMCID: PMC10740673 DOI: 10.3390/biomedicines11123200] [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: 10/23/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023] Open
Abstract
The enlargement of the prostate gland in the reproductive system of males is considered a form of prostate cancer (PrC). The survival rate is considerably improved with earlier diagnosis of cancer; thus, timely intervention should be administered. In this study, a new automatic approach combining several deep learning (DL) techniques was introduced to detect PrC from MRI and ultrasound (US) images. Furthermore, the presented method describes why a certain decision was made given the input MRI or US images. Many pretrained custom-developed layers were added to the pretrained model and employed in the dataset. The study presents an Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) technique on MRIs. The main goal of the EOADL-PCDC method lies in the detection and classification of PrC. To achieve this, the EOADL-PCDC technique applies image preprocessing to improve the image quality. In addition, the EOADL-PCDC technique follows the CapsNet (capsule network) model for the feature extraction model. The EOA is based on hyperparameter tuning used to increase the efficiency of CapsNet. The EOADL-PCDC algorithm makes use of the stacked bidirectional long short-term memory (SBiLSTM) model for prostate cancer classification. A comprehensive set of simulations of the EOADL-PCDC algorithm was tested on the benchmark MRI dataset. The experimental outcome revealed the superior performance of the EOADL-PCDC approach over existing methods in terms of different metrics.
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Affiliation(s)
- Eunmok Yang
- Department of Financial Information Security, Kookmin University, Seoul 02707, Republic of Korea;
| | - K. Shankar
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India;
- Big Data and Machine Learning Lab, South Ural State University, Chelyabinsk 454080, Russia
| | - Sachin Kumar
- College of IBS, National University of Science and Technology, MISiS, Moscow 119049, Russia;
| | - Changho Seo
- Department of Convergence Science, Kongju National University, Gongju-si 32588, Republic of Korea
| | - Inkyu Moon
- Department of Robotics & Mechatronics Engineering, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu 42988, Republic of Korea
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9
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Alves N, Bosma JS, Venkadesh KV, Jacobs C, Saghir Z, de Rooij M, Hermans J, Huisman H. Prediction Variability to Identify Reduced AI Performance in Cancer Diagnosis at MRI and CT. Radiology 2023; 308:e230275. [PMID: 37724961 DOI: 10.1148/radiol.230275] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
Background A priori identification of patients at risk of artificial intelligence (AI) failure in diagnosing cancer would contribute to the safer clinical integration of diagnostic algorithms. Purpose To evaluate AI prediction variability as an uncertainty quantification (UQ) metric for identifying cases at risk of AI failure in diagnosing cancer at MRI and CT across different cancer types, data sets, and algorithms. Materials and Methods Multicenter data sets and publicly available AI algorithms from three previous studies that evaluated detection of pancreatic cancer on contrast-enhanced CT images, detection of prostate cancer on MRI scans, and prediction of pulmonary nodule malignancy on low-dose CT images were analyzed retrospectively. Each task's algorithm was extended to generate an uncertainty score based on ensemble prediction variability. AI accuracy percentage and partial area under the receiver operating characteristic curve (pAUC) were compared between certain and uncertain patient groups in a range of percentile thresholds (10%-90%) for the uncertainty score using permutation tests for statistical significance. The pulmonary nodule malignancy prediction algorithm was compared with 11 clinical readers for the certain group (CG) and uncertain group (UG). Results In total, 18 022 images were used for training and 838 images were used for testing. AI diagnostic accuracy was higher for the cases in the CG across all tasks (P < .001). At an 80% threshold of certain predictions, accuracy in the CG was 21%-29% higher than in the UG and 4%-6% higher than in the overall test data sets. The lesion-level pAUC in the CG was 0.25-0.39 higher than in the UG and 0.05-0.08 higher than in the overall test data sets (P < .001). For pulmonary nodule malignancy prediction, accuracy of AI was on par with clinicians for cases in the CG (AI results vs clinician results, 80% [95% CI: 76, 85] vs 78% [95% CI: 70, 87]; P = .07) but worse for cases in the UG (AI results vs clinician results, 50% [95% CI: 37, 64] vs 68% [95% CI: 60, 76]; P < .001). Conclusion An AI-prediction UQ metric consistently identified reduced performance of AI in cancer diagnosis. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Babyn in this issue.
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Affiliation(s)
- Natália Alves
- From the Department of Medical Imaging, Radboudumc, Route 767, Room 2.30, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (N.A., J.S.B., K.V.V., C.J., M.d.R., J.H., H.H.); Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Herlev, Denmark (Z.S.); and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark (Z.S.)
| | - Joeran S Bosma
- From the Department of Medical Imaging, Radboudumc, Route 767, Room 2.30, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (N.A., J.S.B., K.V.V., C.J., M.d.R., J.H., H.H.); Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Herlev, Denmark (Z.S.); and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark (Z.S.)
| | - Kiran V Venkadesh
- From the Department of Medical Imaging, Radboudumc, Route 767, Room 2.30, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (N.A., J.S.B., K.V.V., C.J., M.d.R., J.H., H.H.); Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Herlev, Denmark (Z.S.); and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark (Z.S.)
| | - Colin Jacobs
- From the Department of Medical Imaging, Radboudumc, Route 767, Room 2.30, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (N.A., J.S.B., K.V.V., C.J., M.d.R., J.H., H.H.); Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Herlev, Denmark (Z.S.); and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark (Z.S.)
| | - Zaigham Saghir
- From the Department of Medical Imaging, Radboudumc, Route 767, Room 2.30, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (N.A., J.S.B., K.V.V., C.J., M.d.R., J.H., H.H.); Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Herlev, Denmark (Z.S.); and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark (Z.S.)
| | - Maarten de Rooij
- From the Department of Medical Imaging, Radboudumc, Route 767, Room 2.30, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (N.A., J.S.B., K.V.V., C.J., M.d.R., J.H., H.H.); Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Herlev, Denmark (Z.S.); and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark (Z.S.)
| | - John Hermans
- From the Department of Medical Imaging, Radboudumc, Route 767, Room 2.30, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (N.A., J.S.B., K.V.V., C.J., M.d.R., J.H., H.H.); Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Herlev, Denmark (Z.S.); and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark (Z.S.)
| | - Henkjan Huisman
- From the Department of Medical Imaging, Radboudumc, Route 767, Room 2.30, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (N.A., J.S.B., K.V.V., C.J., M.d.R., J.H., H.H.); Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Herlev, Denmark (Z.S.); and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark (Z.S.)
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