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Maki JH, Patel NU, Ulrich EJ, Dhaouadi J, Jones RW. Part I: prostate cancer detection, artificial intelligence for prostate cancer and how we measure diagnostic performance: a comprehensive review. Curr Probl Diagn Radiol 2024; 53:606-613. [PMID: 38658286 DOI: 10.1067/j.cpradiol.2024.04.002] [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: 01/12/2024] [Revised: 03/14/2024] [Accepted: 04/18/2024] [Indexed: 04/26/2024]
Abstract
MRI has firmly established itself as a mainstay for the detection, staging and surveillance of prostate cancer. Despite its success, prostate MRI continues to suffer from poor inter-reader variability and a low positive predictive value. The recent emergence of Artificial Intelligence (AI) to potentially improve diagnostic performance shows great potential. Understanding and interpreting the AI landscape as well as ever-increasing research literature, however, is difficult. This is in part due to widely varying study design and reporting techniques. This paper aims to address this need by first outlining the different types of AI used for the detection and diagnosis of prostate cancer, next deciphering how data collection methods, statistical analysis metrics (such as ROC and FROC analysis) and end points/outcomes (lesion detection vs. case diagnosis) affect the performance and limit the ability to compare between studies. Finally, this work explores the need for appropriately enriched investigational datasets and proper ground truth, and provides guidance on how to best conduct AI prostate MRI studies. Published in parallel, a clinical study applying this suggested study design was applied to review and report a multiple-reader multiple-case clinical study of 150 bi-parametric prostate MRI studies across nine readers, measuring physician performance both with and without the use of a recently FDA cleared Artificial Intelligence software.1.
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Affiliation(s)
- Jeffrey H Maki
- University of Colorado Anschutz Medical Center, Department of Radiology, 12401 E 17th Ave (MS L954), Aurora, Colorado, USA.
| | - Nayana U Patel
- University of New Mexico Department of Radiology, Albuquerque, NM, USA
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2
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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, Wolfe S. Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI. Radiology 2024; 312:e232635. [PMID: 39105640 PMCID: PMC11366675 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] [Received: 10/04/2023] [Revised: 04/10/2024] [Accepted: 04/25/2024] [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
| | - Shannyn Wolfe
- 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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Santucci D, Ragone R, Vergantino E, Vaccarino F, Esperto F, Prata F, Scarpa RM, Papalia R, Beomonte Zobel B, Grasso FR, Faiella E. Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features. Cancers (Basel) 2024; 16:2731. [PMID: 39123458 PMCID: PMC11311324 DOI: 10.3390/cancers16152731] [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: 06/27/2024] [Revised: 07/24/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024] Open
Abstract
PURPOSE We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients. MATERIALS AND METHODS The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared. RESULTS This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences. CONCLUSIONS Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction.
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Affiliation(s)
- Domiziana Santucci
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
| | - Raffaele Ragone
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
| | - Elva Vergantino
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
| | - Federica Vaccarino
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
| | - Francesco Esperto
- Department of Urology, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (F.E.); (F.P.); (R.M.S.); (R.P.)
| | - Francesco Prata
- Department of Urology, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (F.E.); (F.P.); (R.M.S.); (R.P.)
| | - Roberto Mario Scarpa
- Department of Urology, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (F.E.); (F.P.); (R.M.S.); (R.P.)
| | - Rocco Papalia
- Department of Urology, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (F.E.); (F.P.); (R.M.S.); (R.P.)
| | - Bruno Beomonte Zobel
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
| | - Francesco Rosario Grasso
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
| | - Eliodoro Faiella
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
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Patel K, Huang S, Rashid A, Varghese B, Gholamrezanezhad A. A Narrative Review of the Use of Artificial Intelligence in Breast, Lung, and Prostate Cancer. Life (Basel) 2023; 13:2011. [PMID: 37895393 PMCID: PMC10608739 DOI: 10.3390/life13102011] [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: 08/27/2023] [Revised: 09/30/2023] [Accepted: 09/30/2023] [Indexed: 10/29/2023] Open
Abstract
Artificial intelligence (AI) has been an important topic within radiology. Currently, AI is used clinically to assist with the detection of lesions through detection systems. However, a number of recent studies have demonstrated the increased value of neural networks in radiology. With an increasing number of screening requirements for cancers, this review aims to study the accuracy of the numerous AI models used in the detection and diagnosis of breast, lung, and prostate cancers. This study summarizes pertinent findings from reviewed articles and provides analysis on the relevancy to clinical radiology. This study found that whereas AI is showing continual improvement in radiology, AI alone does not surpass the effectiveness of a radiologist. Additionally, it was found that there are multiple variations on how AI should be integrated with a radiologist's workflow.
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Affiliation(s)
- Kishan Patel
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA (A.G.)
| | - Sherry Huang
- Department of Urology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Arnav Rashid
- Department of Biological Sciences, Dana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Bino Varghese
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA (A.G.)
| | - Ali Gholamrezanezhad
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA (A.G.)
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Priester A, Fan RE, Shubert J, Rusu M, Vesal S, Shao W, Khandwala YS, Marks LS, Natarajan S, Sonn GA. Prediction and Mapping of Intraprostatic Tumor Extent with Artificial Intelligence. EUR UROL SUPPL 2023; 54:20-27. [PMID: 37545845 PMCID: PMC10403686 DOI: 10.1016/j.euros.2023.05.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2023] [Indexed: 08/08/2023] Open
Abstract
Background Magnetic resonance imaging (MRI) underestimation of prostate cancer extent complicates the definition of focal treatment margins. Objective To validate focal treatment margins produced by an artificial intelligence (AI) model. Design setting and participants Testing was conducted retrospectively in an independent dataset of 50 consecutive patients who had radical prostatectomy for intermediate-risk cancer. An AI deep learning model incorporated multimodal imaging and biopsy data to produce three-dimensional cancer estimation maps and margins. AI margins were compared with conventional MRI regions of interest (ROIs), 10-mm margins around ROIs, and hemigland margins. The AI model also furnished predictions of negative surgical margin probability, which were assessed for accuracy. Outcome measurements and statistical analysis Comparing AI with conventional margins, sensitivity was evaluated using Wilcoxon signed-rank tests and negative margin rates using chi-square tests. Predicted versus observed negative margin probability was assessed using linear regression. Clinically significant prostate cancer (International Society of Urological Pathology grade ≥2) delineated on whole-mount histopathology served as ground truth. Results and limitations The mean sensitivity for cancer-bearing voxels was higher for AI margins (97%) than for conventional ROIs (37%, p < 0.001), 10-mm ROI margins (93%, p = 0.24), and hemigland margins (94%, p < 0.001). For index lesions, AI margins were more often negative (90%) than conventional ROIs (0%, p < 0.001), 10-mm ROI margins (82%, p = 0.24), and hemigland margins (66%, p = 0.004). Predicted and observed negative margin probabilities were strongly correlated (R2 = 0.98, median error = 4%). Limitations include a validation dataset derived from a single institution's prostatectomy population. Conclusions The AI model was accurate and effective in an independent test set. This approach could improve and standardize treatment margin definition, potentially reducing cancer recurrence rates. Furthermore, an accurate assessment of negative margin probability could facilitate informed decision-making for patients and physicians. Patient summary Artificial intelligence was used to predict the extent of tumors in surgically removed prostate specimens. It predicted tumor margins more accurately than conventional methods.
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Affiliation(s)
- Alan Priester
- Department of Urology, David Geffen School of Medicine, Los Angeles, CA, USA
- Avenda Health, Inc., Culver City, CA, USA
| | - Richard E. Fan
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Mirabela Rusu
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Sulaiman Vesal
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Wei Shao
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Yash Samir Khandwala
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Leonard S. Marks
- Department of Urology, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Shyam Natarajan
- Department of Urology, David Geffen School of Medicine, Los Angeles, CA, USA
- Avenda Health, Inc., Culver City, 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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Canellas R, Kohli MD, Westphalen AC. The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging. Curr Oncol Rep 2023; 25:243-250. [PMID: 36749494 DOI: 10.1007/s11912-023-01371-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] [Accepted: 11/14/2022] [Indexed: 02/08/2023]
Abstract
PURPOSE OF REVIEW The purpose of this review is to summarize the current status of artificial intelligence applied to prostate cancer MR imaging. RECENT FINDINGS Artificial intelligence has been applied to prostate cancer MR imaging to improve its diagnostic accuracy and reproducibility of interpretation. Multiple models have been tested for gland segmentation and volume calculation, automated lesion detection, localization, and characterization, as well as prediction of tumor aggressiveness and tumor recurrence. Studies show, for example, that very robust automated gland segmentation and volume calculations can be achieved and that lesions can be detected and accurately characterized. Although results are promising, we should view these with caution. Most studies included a small sample of patients from a single institution and most models did not undergo proper external validation. More research is needed with larger and well-design studies for the development of reliable artificial intelligence tools.
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Affiliation(s)
- Rodrigo Canellas
- Department of Radiology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA
| | - Marc D Kohli
- Clinical Informatics, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA.,Imaging Informatics, UCSF Health, 500 Parnassus Ave, 3rd Floor, San Francisco, CA, 94143, USA
| | - Antonio C Westphalen
- Department of Radiology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA. .,Department of Urology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA. .,Department Radiation Oncology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA.
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Using machine learning and an electronic tongue for discriminating saliva samples from oral cavity cancer patients and healthy individuals. Talanta 2022; 243:123327. [DOI: 10.1016/j.talanta.2022.123327] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/14/2022] [Accepted: 02/16/2022] [Indexed: 11/20/2022]
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8
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The current role of MRI for guiding active surveillance in prostate cancer. Nat Rev Urol 2022; 19:357-365. [PMID: 35393568 DOI: 10.1038/s41585-022-00587-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2022] [Indexed: 01/13/2023]
Abstract
Active surveillance (AS) is the recommended treatment option for low-risk and favourable intermediate-risk prostate cancer management, preserving oncological and functional outcomes. However, active monitoring using relevant parameters in addition to the usual clinical, biological and pathological considerations is necessary to compensate for initial undergrading of the tumour or to detect early progression without missing the opportunity to provide curative therapy. Indeed, several studies have raised concerns about inadequate biopsy sampling at diagnosis. However, the implementation of baseline MRI and targeted biopsy have led to improved initial stratification of low-risk disease; baseline MRI correlates well with disease characteristics and AS outcomes. The use of follow-up MRI during the surveillance phase also raises the question of the requirement for serial biopsies in the absence of radiological progression and the possibility of using completely MRI-based surveillance, with triggers for biopsies based solely on MRI findings. This concept of a tailored-risk, imaging-based monitoring strategy is aimed at reducing invasive procedures. However, the abandonment of serial biopsies in the absence of MRI progression can probably not yet be recommended in routine practice, as the data from real-life cohorts are heterogeneous and inconclusive. Thus, the evolution towards a routine, fully MRI-guided AS pathway has to be preceded by ensuring quality programme assessment for MRI reading and by demonstrating its safety in prospective trials.
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Ayyad SM, Badawy MA, Shehata M, Alksas A, Mahmoud A, Abou El-Ghar M, Ghazal M, El-Melegy M, Abdel-Hamid NB, Labib LM, Ali HA, El-Baz A. A New Framework for Precise Identification of Prostatic Adenocarcinoma. SENSORS 2022; 22:s22051848. [PMID: 35270995 PMCID: PMC8915102 DOI: 10.3390/s22051848] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/21/2022] [Accepted: 02/24/2022] [Indexed: 02/01/2023]
Abstract
Prostate cancer, which is also known as prostatic adenocarcinoma, is an unconstrained growth of epithelial cells in the prostate and has become one of the leading causes of cancer-related death worldwide. The survival of patients with prostate cancer relies on detection at an early, treatable stage. In this paper, we introduce a new comprehensive framework to precisely differentiate between malignant and benign prostate cancer. This framework proposes a noninvasive computer-aided diagnosis system that integrates two imaging modalities of MR (diffusion-weighted (DW) and T2-weighted (T2W)). For the first time, it utilizes the combination of functional features represented by apparent diffusion coefficient (ADC) maps estimated from DW-MRI for the whole prostate in combination with texture features with its first- and second-order representations, extracted from T2W-MRIs of the whole prostate, and shape features represented by spherical harmonics constructed for the lesion inside the prostate and integrated with PSA screening results. The dataset presented in the paper includes 80 biopsy confirmed patients, with a mean age of 65.7 years (43 benign prostatic hyperplasia, 37 prostatic carcinomas). Experiments were conducted using different well-known machine learning approaches including support vector machines (SVM), random forests (RF), decision trees (DT), and linear discriminant analysis (LDA) classification models to study the impact of different feature sets that lead to better identification of prostatic adenocarcinoma. Using a leave-one-out cross-validation approach, the diagnostic results obtained using the SVM classification model along with the combined feature set after applying feature selection (88.75% accuracy, 81.08% sensitivity, 95.35% specificity, and 0.8821 AUC) indicated that the system’s performance, after integrating and reducing different types of feature sets, obtained an enhanced diagnostic performance compared with each individual feature set and other machine learning classifiers. In addition, the developed diagnostic system provided consistent diagnostic performance using 10-fold and 5-fold cross-validation approaches, which confirms the reliability, generalization ability, and robustness of the developed system.
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Affiliation(s)
- Sarah M. Ayyad
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - Mohamed A. Badawy
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt; (M.A.B.); (M.A.E.-G.)
| | - Mohamed Shehata
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.A.); (A.M.)
| | - Ahmed Alksas
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.A.); (A.M.)
| | - Ali Mahmoud
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.A.); (A.M.)
| | - Mohamed Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt; (M.A.B.); (M.A.E.-G.)
| | - Mohammed Ghazal
- Department of Electrical and Computer Engineering, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Moumen El-Melegy
- Department of Electrical Engineering, Assiut University, Assiut 71511, Egypt;
| | - Nahla B. Abdel-Hamid
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - Labib M. Labib
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - H. Arafat Ali
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
- Faulty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35516, Egypt
| | - Ayman El-Baz
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.A.); (A.M.)
- Correspondence:
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Ferro M, de Cobelli O, Musi G, del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022; 14:17562872221109020. [PMID: 35814914 PMCID: PMC9260602 DOI: 10.1177/17562872221109020] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy, via Ripamonti 435 Milano, Italy
| | - Ottavio de Cobelli
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco del Giudice
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Carrieri
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | | | - Alessandro Sciarra
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Martina Maggi
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Biagio Barone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Vincenzo Francesco Caputo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, Chieti, Italy; Urology Unit, ‘SS. Annunziata’ Hospital, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti, Italy
| | - Giuseppe Lucarelli
- Department of Emergency and Organ Transplantation, Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, Italy
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Stefano Luzzago
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Luigi Cormio
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
- Urology Unit, Bonomo Teaching Hospital, Foggia, Italy
| | | | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral Studies, I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
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Peng T, Xiao J, Li L, Pu B, Niu X, Zeng X, Wang Z, Gao C, Li C, Chen L, Yang J. Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis? Int J Comput Assist Radiol Surg 2021; 16:2235-2249. [PMID: 34677748 PMCID: PMC8616865 DOI: 10.1007/s11548-021-02507-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 09/22/2021] [Indexed: 12/24/2022]
Abstract
Purpose To establish machine learning(ML) models for the diagnosis of clinically significant prostate cancer (csPC) using multiparameter magnetic resonance imaging (mpMRI), texture analysis (TA), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative analysis and clinical parameters and to evaluate the stability of these models in internal and temporal validation. Methods The dataset of 194 men was split into training (n = 135) and internal validation (n = 59) cohorts, and a temporal dataset (n = 58) was used for evaluation. The lesions with Gleason score ≥ 7 were defined as csPC. Logistic regression (LR), stepwise regression (SR), classical decision tree (cDT), conditional inference tree (CIT), random forest (RF) and support vector machine (SVM) models were established by combining mpMRI-TA, DCE-MRI and clinical parameters and validated by internal and temporal validation using the receiver operating characteristic (ROC) curve and Delong’s method. Results Eight variables were determined as important predictors for csPC, with the first three related to texture features derived from the apparent diffusion coefficient (ADC) mapping. RF, LR and SR models yielded larger and more stable area under the ROC curve values (AUCs) than other models. In the temporal validation, the sensitivity was lower than that of the internal validation (p < 0.05). There were no significant differences in specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and AUC (p > 0.05). Conclusions Each machine learning model in this study has good classification ability for csPC. Compared with internal validation, the sensitivity of each machine learning model in temporal validation was reduced, but the specificity, accuracy, PPV, NPV and AUCs remained stable at a good level. The RF, LR and SR models have better classification performance in the imaging-based diagnosis of csPC, and ADC texture-related parameters are of the highest importance. Supplementary Information The online version contains supplementary material available at 10.1007/s11548-021-02507-w.
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Affiliation(s)
- Tao Peng
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - JianMing Xiao
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - Lin Li
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - BingJie Pu
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - XiangKe Niu
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China.
| | - XiaoHui Zeng
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - ZongYong Wang
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - ChaoBang Gao
- College of Information Science and Technology, Chengdu University, 1 Shiling shang Street, Chengdu, 610106, Sichuan Province, China
| | - Ci Li
- Department of Pathology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - Lin Chen
- Department of Urology Surgery, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - Jin Yang
- Department of Urology Surgery, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
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Spohn SK, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021; 11:8027-8042. [PMID: 34335978 PMCID: PMC8315055 DOI: 10.7150/thno.61207] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/17/2021] [Indexed: 12/14/2022] Open
Abstract
Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics - a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and improve PCa detection, tissue characterization and clinical outcome prediction. This article gives an overview on the current aspects of methodology and systematically reviews available literature on radiomics in PCa patients, showing its potential for personalized therapy approaches. The qualitative synthesis includes all imaging modalities and focuses on validated studies, putting forward future directions.
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Affiliation(s)
- Simon K.B. Spohn
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
| | - Alisa S. Bettermann
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Matthias Benndorf
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Michael Mix
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Nils H. Nicolay
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Tobias Fechter
- Department of Radiation Oncology - Division of Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Tobias Hölscher
- Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Radu Grosu
- Institute of Computer Engineering, Vienne University of Technology, Vienna, Austria
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele - Milan, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano - Milan, Italy
| | - Anca L. Grosu
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
- German Oncology Center, European University of Cyprus, Limassol, Cyprus
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Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Eid M, Iannicelli E, Laghi A. Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications. Cancers (Basel) 2021; 13:cancers13112681. [PMID: 34072366 PMCID: PMC8197789 DOI: 10.3390/cancers13112681] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 01/08/2023] Open
Abstract
Simple Summary This Part II is an overview of the main applications of Radiomics in oncologic imaging with a focus on diagnosis, prognosis prediction and assessment of response to therapy in thoracic, genito-urinary, breast, neurologic, hematologic and musculoskeletal oncology. In this part II we describe the radiomic applications, limitations and future perspectives for each pre-eminent tumor. In the future, Radiomics could have a pivotal role in management of cancer patients as an imaging tool to support clinicians in decision making process. However, further investigations need to obtain some stable results and to standardize radiomic analysis (i.e., image acquisitions, segmentation and model building) in clinical routine. Abstract Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing evidence-based medicine, uniquely tailored to each patient and tumor. Mineable data, extracted from medical images could be combined with clinical and survival parameters to develop models useful for the clinicians in cancer patients’ assessment. As such, adding Radiomics to traditional subjective imaging may provide a quantitative and extensive cancer evaluation reflecting histologic architecture. In this Part II, we present an overview of radiomic applications in thoracic, genito-urinary, breast, neurological, hematologic and musculoskeletal oncologic applications.
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Affiliation(s)
- Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Francesco Pucciarelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Gisella Guido
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Tiziano Polidori
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Federica Landolfi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Matteo Nicolai
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elena Lucertini
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, 00161 Rome, Italy;
| | - Benedetta Bracci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Ilaria Nacci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Carlotta Rucci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marwen Eid
- Internal Medicine, Northwell Health Staten Island University Hospital, Staten Island, New York, NY 10305, USA;
| | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
- Correspondence: ; Tel.: +39-0633775285
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Twilt JJ, van Leeuwen KG, Huisman HJ, Fütterer JJ, de Rooij M. Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11060959. [PMID: 34073627 PMCID: PMC8229869 DOI: 10.3390/diagnostics11060959] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 12/14/2022] Open
Abstract
Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.
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15
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Castaldo R, Cavaliere C, Soricelli A, Salvatore M, Pecchia L, Franzese M. Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review. J Med Internet Res 2021; 23:e22394. [PMID: 33792552 PMCID: PMC8050752 DOI: 10.2196/22394] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 11/26/2020] [Accepted: 01/17/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Machine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. OBJECTIVE This study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. METHODS Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies-version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice. RESULTS In the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. CONCLUSIONS The performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers.
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Park J, Rho MJ, Moon HW, Kim J, Lee C, Kim D, Kim CS, Jeon SS, Kang M, Lee JY. Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy. Technol Cancer Res Treat 2021; 20:15330338211024660. [PMID: 34180308 PMCID: PMC8243093 DOI: 10.1177/15330338211024660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 03/08/2021] [Accepted: 04/19/2021] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVES To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. PATIENTS AND METHODS This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. RESULTS We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. CONCLUSION We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system.
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Affiliation(s)
- Jihwan Park
- School of Software Convergence, College of Software Convergence,
Dankook University, Yongin, Republic of Korea
| | - Mi Jung Rho
- Catholic Cancer Research Institute, College of Medicine, The
Catholic University of Korea, Seoul, Republic of Korea
| | - Hyong Woo Moon
- Department of Urology, Seoul St. Mary’s Hospital, College of
Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | | | | | | | - Choung-Soo Kim
- Department of Urology, Asan Medical Center, University of Ulsan
College of Medicine, Seoul, Republic of Korea
| | - Seong Soo Jeon
- Department of Urology, Samsung Medical Center, Sungkyunkwan
University School of Medicine, Seoul, Republic of Korea
| | - Minyong Kang
- Department of Urology, Samsung Medical Center, Sungkyunkwan
University School of Medicine, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan
University, Seoul, Republic of Korea
| | - Ji Youl Lee
- Department of Urology, Seoul St. Mary’s Hospital, College of
Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Mehralivand S, Harmon SA, Shih JH, Smith CP, Lay N, Argun B, Bednarova S, Baroni RH, Canda AE, Ercan K, Girometti R, Karaarslan E, Kural AR, Purysko AS, Rais-Bahrami S, Tonso VM, Magi-Galluzzi C, Gordetsky JB, Macarenco RSES, Merino MJ, Gumuskaya B, Saglican Y, Sioletic S, Warren AY, Barrett T, Bittencourt L, Coskun M, Knauss C, Law YM, Malayeri AA, Margolis DJ, Marko J, Yakar D, Wood BJ, Pinto PA, Choyke PL, Summers RM, Turkbey B. Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI. AJR Am J Roentgenol 2020; 215:903-912. [PMID: 32755355 PMCID: PMC8974983 DOI: 10.2214/ajr.19.22573] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
OBJECTIVE. The purpose of this study was to evaluate in a multicenter dataset the performance of an artificial intelligence (AI) detection system with attention mapping compared with multiparametric MRI (mpMRI) interpretation in the detection of prostate cancer. MATERIALS AND METHODS. MRI examinations from five institutions were included in this study and were evaluated by nine readers. In the first round, readers evaluated mpMRI studies using the Prostate Imaging Reporting and Data System version 2. After 4 weeks, images were again presented to readers along with the AI-based detection system output. Readers accepted or rejected lesions within four AI-generated attention map boxes. Additional lesions outside of boxes were excluded from detection and categorization. The performances of readers using the mpMRI-only and AI-assisted approaches were compared. RESULTS. The study population included 152 case patients and 84 control patients with 274 pathologically proven cancer lesions. The lesion-based AUC was 74.9% for MRI and 77.5% for AI with no significant difference (p = 0.095). The sensitivity for overall detection of cancer lesions was higher for AI than for mpMRI but did not reach statistical significance (57.4% vs 53.6%, p = 0.073). However, for transition zone lesions, sensitivity was higher for AI than for MRI (61.8% vs 50.8%, p = 0.001). Reading time was longer for AI than for MRI (4.66 vs 4.03 minutes, p < 0.001). There was moderate interreader agreement for AI and MRI with no significant difference (58.7% vs 58.5%, p = 0.966). CONCLUSION. Overall sensitivity was only minimally improved by use of the AI system. Significant improvement was achieved, however, in the detection of transition zone lesions with use of the AI system at the cost of a mean of 40 seconds of additional reading time.
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Affiliation(s)
- Sherif Mehralivand
- Department of Urology and Pediatric Urology, University Medical Center, Mainz, Germany
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892-1088
| | - Stephanie A Harmon
- Clinical Research Directorate, Leidos Biomedical Research, Inc., Frederick, MD
| | - Joanna H Shih
- Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Rockville, MD
| | - Clayton P Smith
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892-1088
| | - Nathan Lay
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892-1088
| | - Burak Argun
- Department of Urology, Acibadem University, Istanbul, Turkey
| | | | | | | | - Karabekir Ercan
- Department of Radiology, Ankara City Hospital, Ankara, Turkey
| | | | | | - Ali Riza Kural
- Department of Urology, Acibadem University, Istanbul, Turkey
| | | | - Soroush Rais-Bahrami
- Department of Urology, University of Alabama at Birmingham, Birmingham, AL
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
- O'Neal Comprehensive Cancer Center at UAB, University of Alabama at Birmingham, Birmingham, AL
| | | | | | - Jennifer B Gordetsky
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL
- Present address: Department of Pathology, Vanderbilt University, Nashville, TN
| | | | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Berrak Gumuskaya
- Department of Pathology, Ankara Yildirim Beyazit University, School of Medicine, Ankara, Turkey
| | - Yesim Saglican
- Department of Pathology, Acibadem University, Istanbul, Turkey
| | | | - Anne Y Warren
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Tristan Barrett
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Leonardo Bittencourt
- Department of Radiology, Federal Fluminense University, Rio de Janeiro, Brazil
- DASA Company, Rio de Janeiro, Brazil
| | - Mehmet Coskun
- Department of Radiology, University of Health Sciences Dr. Behçet Uz Child Disease and Pediatric Surgery Training and Research Hospital, İzmir, Turkey
| | - Chris Knauss
- Department of Radiology, Walter Reed Medical Center, Bethesda, MD
| | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore
| | - Ashkan A Malayeri
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD
| | | | - Jamie Marko
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Derya Yakar
- Department of Radiology, Medical Imaging Centre, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute and Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892-1088
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Bethesda, MD
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892-1088
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Using decision curve analysis to benchmark performance of a magnetic resonance imaging-based deep learning model for prostate cancer risk assessment. Eur Radiol 2020; 30:6867-6876. [PMID: 32591889 DOI: 10.1007/s00330-020-07030-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 06/10/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVES To benchmark the performance of a calibrated 3D convolutional neural network (CNN) applied to multiparametric MRI (mpMRI) for risk assessment of clinically significant prostate cancer (csPCa) using decision curve analysis (DCA). METHODS We retrospectively analyzed 499 patients who had positive mpMRI (PI-RADSv2 ≥ 3) and MRI-targeted biopsy. The training cohort comprised 449 men, including a calibration set of 50 men. Biopsy decision strategies included using risk estimates from the CNN (original and calibrated), to perform biopsy in men with PI-RADSv2 ≥ 4 only, or additionally in men with PI-RADSv2 3 and PSA density (PSAd) ≥ 0.15 ng/ml/ml. Discrimination, calibration and clinical usefulness in the unseen test cohort (n = 50) were assessed using C-statistic, calibration plots and DCA, respectively. RESULTS The calibrated CNN achieved moderate calibration (Hosmer-Lemeshow calibration test, p = 0.41) and good discrimination (C = 0.85). DCA revealed consistently higher net benefit and net reduction in biopsies for the calibrated CNN compared with the original CNN, PI-RADSv2 ≥ 4 and the combined strategy of PI-RADSv2 and PSAd. Original CNN predictions were severely miscalibrated (p < 0.0001) resulting in net harm compared with a 'biopsy all' patients strategy. At-risk thresholds ≥ 10% using the calibrated CNN and the combined strategy reduced the number of biopsies by an estimated 201 and 55 men, respectively, per 1000 men at risk, without missing csPCa, while original CNN and PI-RADSv2 ≥ 4 could not achieve a net reduction in biopsies. CONCLUSIONS DCA revealed that our calibrated 3D-CNN resulted in fewer unnecessary biopsies compared with using PI-RADSv2 alone or in combination with PSAd. CNN calibration is important in achieving clinical utility. KEY POINTS • A 3D deep learning model applied to multiparametric MRI may help to prevent unnecessary prostate biopsies in patients eligible for MRI-targeted biopsy. • Owing to miscalibration, original risk estimates by the deep learning model require prior calibration to enable clinical utility. • Decision curve analysis confirmed a net benefit of using our calibrated deep learning model for biopsy decisions compared with alternative strategies, including PI-RADSv2 alone and in combination with prostate-specific antigen density.
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19
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Stanzione A, Gambardella M, Cuocolo R, Ponsiglione A, Romeo V, Imbriaco M. Prostate MRI radiomics: A systematic review and radiomic quality score assessment. Eur J Radiol 2020; 129:109095. [PMID: 32531722 DOI: 10.1016/j.ejrad.2020.109095] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/20/2020] [Accepted: 05/25/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND Radiomics have the potential to further increase the value of MRI in prostate cancer management. However, implementation in clinical practice is still far and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the literature to assess the quality of prostate MRI radiomic studies using the radiomics quality score (RQS). METHODS Multiple medical literature archives (PubMed, Web of Science and EMBASE) were searched to retrieve original investigations focused on prostate MRI radiomic approaches up to the end of June 2019. Three researchers independently assessed each paper using the RQS. Data from the most experienced researcher were used for descriptive analysis. Inter-rater reproducibility was assessed using the intraclass correlation coefficient (ICC) on the total RQS score. RESULTS 73 studies were included in the analysis. Overall, the average RQS total score was 7.93 ± 5.13 on a maximum of 36 points, with a final average percentage of 23 ± 13%. Among the most critical items, the lack of feature robustness testing strategies and external validation datasets. The ICC resulted poor to moderate, with an average value of 0.57 and 95% Confidence Intervals between 0.44 and 0.69. CONCLUSIONS Current studies on prostate MRI radiomics still lack the quality required to allow their introduction in clinical practice.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Michele Gambardella
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy.
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
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20
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Bardis MD, Houshyar R, Chang PD, Ushinsky A, Glavis-Bloom J, Chahine C, Bui TL, Rupasinghe M, Filippi CG, Chow DS. Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends. Cancers (Basel) 2020; 12:E1204. [PMID: 32403240 PMCID: PMC7281682 DOI: 10.3390/cancers12051204] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 05/02/2020] [Accepted: 05/08/2020] [Indexed: 01/13/2023] Open
Abstract
Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists' accuracy and speed.
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Affiliation(s)
- Michelle D. Bardis
- Department of Radiology, University of California, Irvine, Orange, CA 92868-3201, USA; (R.H.); (P.D.C.); (J.G.-B.); (C.C.); (T.-L.B.); (M.R.); (D.S.C.)
| | - Roozbeh Houshyar
- Department of Radiology, University of California, Irvine, Orange, CA 92868-3201, USA; (R.H.); (P.D.C.); (J.G.-B.); (C.C.); (T.-L.B.); (M.R.); (D.S.C.)
| | - Peter D. Chang
- Department of Radiology, University of California, Irvine, Orange, CA 92868-3201, USA; (R.H.); (P.D.C.); (J.G.-B.); (C.C.); (T.-L.B.); (M.R.); (D.S.C.)
| | - Alexander Ushinsky
- Mallinckrodt Institute of Radiology, Washington University Saint Louis, St. Louis, MO 63110, USA;
| | - Justin Glavis-Bloom
- Department of Radiology, University of California, Irvine, Orange, CA 92868-3201, USA; (R.H.); (P.D.C.); (J.G.-B.); (C.C.); (T.-L.B.); (M.R.); (D.S.C.)
| | - Chantal Chahine
- Department of Radiology, University of California, Irvine, Orange, CA 92868-3201, USA; (R.H.); (P.D.C.); (J.G.-B.); (C.C.); (T.-L.B.); (M.R.); (D.S.C.)
| | - Thanh-Lan Bui
- Department of Radiology, University of California, Irvine, Orange, CA 92868-3201, USA; (R.H.); (P.D.C.); (J.G.-B.); (C.C.); (T.-L.B.); (M.R.); (D.S.C.)
| | - Mark Rupasinghe
- Department of Radiology, University of California, Irvine, Orange, CA 92868-3201, USA; (R.H.); (P.D.C.); (J.G.-B.); (C.C.); (T.-L.B.); (M.R.); (D.S.C.)
| | | | - Daniel S. Chow
- Department of Radiology, University of California, Irvine, Orange, CA 92868-3201, USA; (R.H.); (P.D.C.); (J.G.-B.); (C.C.); (T.-L.B.); (M.R.); (D.S.C.)
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O'Connor L, Wang A, Walker SM, Yerram N, Pinto PA, Turkbey B. Use of multiparametric magnetic resonance imaging (mpMRI) in localized prostate cancer. Expert Rev Med Devices 2020; 17:435-442. [PMID: 32275845 DOI: 10.1080/17434440.2020.1755257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Introduction: Prostate magnetic resonance imaging (MRI) is commonly used for localized disease mainly to detect intraprostatic lesions and to guide biopsies. Despite its documented success in clinical practice, limitations still exist for prostate MRI. In this review, we discuss common clinical uses of prostate MRI, its limitations, and potential solutions for those limitations.Areas covered: Current uses of prostate MRI and challenges discussed. Literature search in PubMed was completed using the keywords "prostate MRI, prostate cancer."Expert opinion: Prostate MRI is a useful method for localization, biopsy, and treatment guidance of prostate cancer. Certain limitations of prostate MRI such as false negatives due to spatial resolution and relatively low repeatability between different radiologists can potentially be solved by investing more on education training and artificial intelligence technology.
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Affiliation(s)
- Luke O'Connor
- Urologic Oncology Branch, NCI, NIH, Bethesda, MD, USA
| | - Alex Wang
- Urologic Oncology Branch, NCI, NIH, Bethesda, MD, USA
| | | | - Nitin Yerram
- Urologic Oncology Branch, NCI, NIH, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, NCI, NIH, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Program, NCI, NIH, Bethesda, MD, USA
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Summers RM. Are we at a crossroads or a plateau? Radiomics and machine learning in abdominal oncology imaging. Abdom Radiol (NY) 2019; 44:1985-1989. [PMID: 29730736 DOI: 10.1007/s00261-018-1613-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Advances in radiomics and machine learning have driven a technology boom in the automated analysis of radiology images. For the past several years, expectations have been nearly boundless for these new technologies to revolutionize radiology image analysis and interpretation. In this editorial, I compare the expectations with the realities with particular attention to applications in abdominal oncology imaging. I explore whether these technologies will leave us at a crossroads to an exciting future or to a sustained plateau and disillusionment.
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Affiliation(s)
- Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences Department, National Institutes of Health Clinical Center, Bldg. 10 Room 1C224D, MSC 1182, Bethesda, MD, 20892-1182, USA.
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Abstract
Radiomics and radiogenomics are attractive research topics in prostate cancer. Radiomics mainly focuses on extraction of quantitative information from medical imaging, whereas radiogenomics aims to correlate these imaging features to genomic data. The purpose of this review is to provide a brief overview summarizing recent progress in the application of radiomics-based approaches in prostate cancer and to discuss the potential role of radiogenomics in prostate cancer.
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Harmon SA, Tuncer S, Sanford T, Choyke PL, Türkbey B. Artificial intelligence at the intersection of pathology and radiology in prostate cancer. Diagn Interv Radiol 2019; 25:183-188. [PMID: 31063138 PMCID: PMC6521904 DOI: 10.5152/dir.2019.19125] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 03/08/2019] [Accepted: 03/23/2019] [Indexed: 01/30/2023]
Abstract
Pathologic grading plays a key role in prostate cancer risk stratification and treatment selection, traditionally assessed from systemic core needle biopsies sampled throughout the prostate gland. Multiparametric magnetic resonance imaging (mpMRI) has become a well-established clinical tool for detecting and localizing prostate cancer. However, both pathologic and radiologic assessment suffer from poor reproducibility among readers. Artificial intelligence (AI) methods show promise in aiding the detection and assessment of imaging-based tasks, dependent on the curation of high-quality training sets. This review provides an overview of recent advances in AI applied to mpMRI and digital pathology in prostate cancer which enable advanced characterization of disease through combined radiology-pathology assessment.
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Affiliation(s)
- Stephanie A. Harmon
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Sena Tuncer
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Thomas Sanford
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Peter L. Choyke
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Barış Türkbey
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
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Sun Y, Reynolds HM, Parameswaran B, Wraith D, Finnegan ME, Williams S, Haworth A. Multiparametric MRI and radiomics in prostate cancer: a review. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:3-25. [PMID: 30762223 DOI: 10.1007/s13246-019-00730-z] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 01/22/2019] [Indexed: 12/30/2022]
Abstract
Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.
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Affiliation(s)
- Yu Sun
- University of Sydney, Sydney, Australia. .,Peter MacCallum Cancer Centre, Melbourne, Australia.
| | | | | | - Darren Wraith
- Queensland University of Technology, Brisbane, Australia
| | - Mary E Finnegan
- Imperial College Healthcare NHS Trust, London, UK.,Imperial College London, London, UK
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26
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Sumathipala Y, Lay N, Turkbey B, Smith C, Choyke PL, Summers RM. Prostate cancer detection from multi-institution multiparametric MRIs using deep convolutional neural networks. J Med Imaging (Bellingham) 2018; 5:044507. [PMID: 30840728 DOI: 10.1117/1.jmi.5.4.044507] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 11/05/2018] [Indexed: 01/21/2023] Open
Abstract
Multiparametric magnetic resonance imaging (mpMRI) of the prostate aids in early diagnosis of prostate cancer, but is difficult to interpret and subject to interreader variability. Our objective is to generate probability maps, overlaid on original mpMRI images to help radiologists identify where a cancer is suspected as a computer-aided diagnostic (CAD). We optimized the holistically nested edge detection (HED) deep convolutional neural network. Our dataset contains T2, apparent diffusion coefficient, and high b -value images from 186 patients across six institutions worldwide: 92 with an endorectal coil (ERC) and 94 without. Ground-truth was based on tumor segmentations manually drawn by expert radiologists based on histologic evidence of cancer. The training set consisted of 120 patients and the validation set and test set included 19 and 47, respectively. Slice-level probability maps are evaluated at the lesion level of analysis. The best model: HED using 5 × 5 convolutional kernels, batch normalization, and optimized using Adam. This CAD performed significantly better ( p < 0.001 ) in the peripheral zone ( AUC = 0.94 ± 0.01 ) than the transition zone. It outperforms a previous CAD from our group in a head-to-head comparison on the same ERC-only test cases ( AUC = 0.97 ± 0.01 ; p < 0.001 ). Our CAD establishes a state-of-the-art performance for predicting prostate cancer lesions on mpMRIs.
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Affiliation(s)
- Yohan Sumathipala
- National Institutes of Health Clinical Center, Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Bethesda, Maryland, United States
| | - Nathan Lay
- National Institutes of Health Clinical Center, Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Bethesda, Maryland, United States
| | - Baris Turkbey
- National Institutes of Health, National Cancer Institute, Molecular Imaging Program, Bethesda, Maryland, United States
| | - Clayton Smith
- National Institutes of Health, National Cancer Institute, Molecular Imaging Program, Bethesda, Maryland, United States
| | - Peter L Choyke
- National Institutes of Health, National Cancer Institute, Molecular Imaging Program, Bethesda, Maryland, United States
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Bethesda, Maryland, United States
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27
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Armato SG, Huisman H, Drukker K, Hadjiiski L, Kirby JS, Petrick N, Redmond G, Giger ML, Cha K, Mamonov A, Kalpathy-Cramer J, Farahani K. PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J Med Imaging (Bellingham) 2018; 5:044501. [PMID: 30840739 DOI: 10.1117/1.jmi.5.4.044501] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 10/10/2018] [Indexed: 12/18/2022] Open
Abstract
Grand challenges stimulate advances within the medical imaging research community; within a competitive yet friendly environment, they allow for a direct comparison of algorithms through a well-defined, centralized infrastructure. The tasks of the two-part PROSTATEx Challenges (the PROSTATEx Challenge and the PROSTATEx-2 Challenge) are (1) the computerized classification of clinically significant prostate lesions and (2) the computerized determination of Gleason Grade Group in prostate cancer, both based on multiparametric magnetic resonance images. The challenges incorporate well-vetted cases for training and testing, a centralized performance assessment process to evaluate results, and an established infrastructure for case dissemination, communication, and result submission. In the PROSTATEx Challenge, 32 groups apply their computerized methods (71 methods total) to 208 prostate lesions in the test set. The area under the receiver operating characteristic curve for these methods in the task of differentiating between lesions that are and are not clinically significant ranged from 0.45 to 0.87; statistically significant differences in performance among the top-performing methods, however, are not observed. In the PROSTATEx-2 Challenge, 21 groups apply their computerized methods (43 methods total) to 70 prostate lesions in the test set. When compared with the reference standard, the quadratic-weighted kappa values for these methods in the task of assigning a five-point Gleason Grade Group to each lesion range from - 0.24 to 0.27; superiority to random guessing can be established for only two methods. When approached with a sense of commitment and scientific rigor, challenges foster interest in the designated task and encourage innovation in the field.
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Affiliation(s)
- Samuel G Armato
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Henkjan Huisman
- Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen, The Netherlands
| | - Karen Drukker
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Lubomir Hadjiiski
- University of Michigan, Department of Radiology, Ann Arbor, Michigan, United States
| | - Justin S Kirby
- Frederick National Laboratory for Cancer Research, Cancer Imaging Program, Frederick, Maryland, United States
| | - Nicholas Petrick
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, Maryland, United States
| | - George Redmond
- National Cancer Institute, Cancer Imaging Program, Division of Cancer Treatment and Diagnosis, Bethesda, Maryland, United States
| | - Maryellen L Giger
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Kenny Cha
- University of Michigan, Department of Radiology, Ann Arbor, Michigan, United States.,U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, Maryland, United States
| | - Artem Mamonov
- MGH/Harvard Medical School, Boston, Massachusetts, United States
| | | | - Keyvan Farahani
- National Cancer Institute, Cancer Imaging Program, Division of Cancer Treatment and Diagnosis, Bethesda, Maryland, United States
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28
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Sumathipala Y, Shafiq M, Bongen E, Brinton C, Paik D. Machine learning to predict lung nodule biopsy method using CT image features: A pilot study. Comput Med Imaging Graph 2018; 71:1-8. [PMID: 30448741 DOI: 10.1016/j.compmedimag.2018.10.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2018] [Revised: 09/21/2018] [Accepted: 10/26/2018] [Indexed: 12/18/2022]
Abstract
Computed tomography (CT)-based screening on lung cancer mortality is poised to make lung nodule management a growing public health problem. Biopsy and pathologic analysis of suspicious nodules is necessary to ensure accurate diagnosis and appropriate intervention. Biopsy techniques vary as do the specialists that perform them and the ways lung nodule patients are referred and triaged. The largest dichotomy is between minimally invasive biopsy (MIB) and surgical biopsy (SB). Cases of unsuccessful MIB preceding a SB can result in considerable delay in definitive care with potentially an adverse impact on prognosis besides potentially avoidable healthcare expenditures. An automated method that predicts the optimal biopsy method for a given lung nodule could save time and healthcare costs by facilitating referral and triage patterns. To our knowledge, no such method has been published. Here, we used CT image features and radiologist-annotated semantic features to predict successful MIB in a way that has not been described before. Using data from the Lung Image Database Consortium image collection (LIDC-IDRI), we trained a logistic regression model to determine whether a MIB or SB procedure was used to diagnose lung cancer in a patient presenting with lung nodules. We found that in successful MIB cases, the nodules were significantly larger and more spiculated. Our model illustrates that using robust machine learning tools on easily accessible semantic and image data can predict whether a patient's nodule is best biopsied by MIB or SB. Pending further validation and optimization, clinicians could use our publicly accessible model to aid clinical decision-making.
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Affiliation(s)
- Yohan Sumathipala
- Biomedical Informatics Program, Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, United States.
| | - Majid Shafiq
- Division of Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA, United States.
| | - Erika Bongen
- Program in Immunology, Stanford University School of Medicine, Stanford, CA, United States.
| | - Connor Brinton
- Department of Computer Science, Stanford University School of Engineering, Stanford, CA, United States.
| | - David Paik
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States.
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29
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Greer MD, Lay N, Shih JH, Barrett T, Bittencourt LK, Borofsky S, Kabakus I, Law YM, Marko J, Shebel H, Mertan FV, Merino MJ, Wood BJ, Pinto PA, Summers RM, Choyke PL, Turkbey B. Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study. Eur Radiol 2018; 28:4407-4417. [PMID: 29651763 PMCID: PMC8023433 DOI: 10.1007/s00330-018-5374-6] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 01/12/2018] [Accepted: 02/06/2018] [Indexed: 12/19/2022]
Abstract
OBJECTIVES To evaluate if computer-aided diagnosis (CAD) prior to prostate multi-parametric MRI (mpMRI) can improve sensitivity and agreement between radiologists. METHODS Nine radiologists (three each high, intermediate, low experience) from eight institutions participated. A total of 163 patients with 3-T mpMRI from 4/2012 to 6/2015 were included: 110 cancer patients with prostatectomy after mpMRI, 53 patients with no lesions on mpMRI and negative TRUS-guided biopsy. Readers were blinded to all outcomes and detected lesions per PI-RADSv2 on mpMRI. After 5 weeks, readers re-evaluated patients using CAD to detect lesions. Prostatectomy specimens registered to MRI were ground truth with index lesions defined on pathology. Sensitivity, specificity and agreement were calculated per patient, lesion level and zone-peripheral (PZ) and transition (TZ). RESULTS Index lesion sensitivity was 78.2% for mpMRI alone and 86.3% for CAD-assisted mpMRI (p = 0.013). Sensitivity was comparable for TZ lesions (78.7% vs 78.1%; p = 0.929); CAD improved PZ lesion sensitivity (84% vs 94%; p = 0.003). Improved sensitivity came from lesions scored PI-RADS < 3 as index lesion sensitivity was comparable at PI-RADS ≥ 3 (77.6% vs 78.1%; p = 0.859). Per patient specificity was 57.1% for CAD and 70.4% for mpMRI (p = 0.003). CAD improved agreement between all readers (56.9% vs 71.8%; p < 0.001). CONCLUSIONS CAD-assisted mpMRI improved sensitivity and agreement, but decreased specificity, between radiologists of varying experience. KEY POINTS • Computer-aided diagnosis (CAD) assists clinicians in detecting prostate cancer on MRI. • CAD assistance improves agreement between radiologists in detecting prostate cancer lesions. • However, this CAD system induces more false positives, particularly for less-experienced clinicians and in the transition zone. • CAD assists radiologists in detecting cancer missed on MRI, suggesting a path for improved diagnostic confidence.
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Affiliation(s)
- Matthew D Greer
- Molecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA
| | - Nathan Lay
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Joanna H Shih
- Biometric Research Program, NCI, NIH, Bethesda, MD, USA
| | - Tristan Barrett
- Department of Radiology, University of Cambridge School of Medicine, Cambridge, UK
| | | | | | | | - Yan Mee Law
- Singapore General Hospital, Singapore, Singapore
| | - Jamie Marko
- Radiology and Imaging Sciences Department, Clinical Center, NIH, Bethesda, MD, USA
| | - Haytham Shebel
- Department of Radiology, Nephrology Center, Mansoura University, Mansoura, 35516, Egypt
| | - Francesca V Mertan
- Molecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA
| | | | - Bradford J Wood
- Center for Interventional Oncology, NCI and Radiology Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, NCI, NIH, Bethesda, MD, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA
| | - Baris Turkbey
- Molecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA.
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30
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Gaur S, Lay N, Harmon SA, Doddakashi S, Mehralivand S, Argun B, Barrett T, Bednarova S, Girometti R, Karaarslan E, Kural AR, Oto A, Purysko AS, Antic T, Magi-Galluzzi C, Saglican Y, Sioletic S, Warren AY, Bittencourt L, Fütterer JJ, Gupta RT, Kabakus I, Law YM, Margolis DJ, Shebel H, Westphalen AC, Wood BJ, Pinto PA, Shih JH, Choyke PL, Summers RM, Turkbey B. Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation. Oncotarget 2018; 9:33804-33817. [PMID: 30333911 PMCID: PMC6173466 DOI: 10.18632/oncotarget.26100] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 08/23/2018] [Indexed: 12/31/2022] Open
Abstract
For prostate cancer detection on prostate multiparametric MRI (mpMRI), the Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) and computer-aided diagnosis (CAD) systems aim to widely improve standardization across radiologists and centers. Our goal was to evaluate CAD assistance in prostate cancer detection compared with conventional mpMRI interpretation in a diverse dataset acquired from five institutions tested by nine readers of varying experience levels, in total representing 14 globally spread institutions. Index lesion sensitivities of mpMRI-alone were 79% (whole prostate (WP)), 84% (peripheral zone (PZ)), 71% (transition zone (TZ)), similar to CAD at 76% (WP, p=0.39), 77% (PZ, p=0.07), 79% (TZ, p=0.15). Greatest CAD benefit was in TZ for moderately-experienced readers at PI-RADSv2 <3 (84% vs mpMRI-alone 67%, p=0.055). Detection agreement was unchanged but CAD-assisted read times improved (4.6 vs 3.4 minutes, p<0.001). At PI-RADSv2 ≥ 3, CAD improved patient-level specificity (72%) compared to mpMRI-alone (45%, p<0.001). PI-RADSv2 and CAD-assisted mpMRI interpretations have similar sensitivities across multiple sites and readers while CAD has potential to improve specificity and moderately-experienced radiologists' detection of more difficult tumors in the center of the gland. The multi-institutional evidence provided is essential to future prostate MRI and CAD development.
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Affiliation(s)
- Sonia Gaur
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nathan Lay
- Imaging Biomarkers and Computer-aided Diagnosis Lab, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie A. Harmon
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Clinical Research Directorate/ Clinical Monitoring Research Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Sreya Doddakashi
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sherif Mehralivand
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Urology and Pediatric Urology, University Medical Center Mainz, Mainz, Germany
| | - Burak Argun
- Department of Urology, Acibadem University, Istanbul, Turkey
| | - Tristan Barrett
- Department of Radiology, University of Cambridge, Cambridge, UK
| | | | | | | | - Ali Riza Kural
- Department of Urology, Acibadem University, Istanbul, Turkey
| | - Aytekin Oto
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | | | - Tatjana Antic
- Department of Pathology, University of Chicago, Chicago, IL, USA
| | | | - Yesim Saglican
- Department of Pathology, Acibadem University, Istanbul, Turkey
| | | | - Anne Y. Warren
- Department of Pathology, University of Cambridge, Cambridge, UK
| | | | | | - Rajan T. Gupta
- Department of Radiology, Duke University, Durham, NC, USA
| | - Ismail Kabakus
- Department of Radiology, Hacettepe University, Ankara, Turkey
| | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore
| | | | - Haytham Shebel
- Department of Radiology, Mansoura University, Mansoura, Egypt
| | - Antonio C. Westphalen
- UCSF Department of Radiology, University of California-San Francisco, San Francisco, CA, USA
| | - Bradford J. Wood
- Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Peter A. Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joanna H. Shih
- Biometric Research Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter L. Choyke
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ronald M. Summers
- Imaging Biomarkers and Computer-aided Diagnosis Lab, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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31
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Abstract
MR imaging is an important part of prostate cancer diagnosis. Variations in quality and skill in general practice mean results are not as impressive as they were in academic centers. This observation provides an impetus to improve the method. Improved quality assurance will likely result in better outcomes. Improved characterization of clinically significant prostate cancer may assist in making MR imaging more useful. Improved methods of registering MR imaging with transrectal ultrasound imaging and robotic arms controlling the biopsy can reduce the impact of inexperienced operators and make the entire system of MR imaging-guided biopsies more robust.
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Affiliation(s)
- Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Drive, Room B3B69, Bethesda, MD 20892, USA
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Drive, Room B3B69, Bethesda, MD 20892, USA.
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