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Mazzetti S, Defeudis A, Nicoletti G, Chiorino G, De Luca S, Faletti R, Gatti M, Gontero P, Manfredi M, Mello-Grand M, Peraldo-Neia C, Zitella A, Porpiglia F, Regge D, Giannini V. Development and validation of a clinical decision support system based on PSA, microRNAs, and MRI for the detection of prostate cancer. Eur Radiol 2024; 34:5108-5117. [PMID: 38177618 PMCID: PMC11255044 DOI: 10.1007/s00330-023-10542-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 11/29/2023] [Accepted: 12/02/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVES The aims of this study are to develop and validate a clinical decision support system based on demographics, prostate-specific antigen (PSA), microRNA (miRNA), and MRI for the detection of prostate cancer (PCa) and clinical significant (cs) PCa, and to assess if this system performs better compared to MRI alone. METHODS This retrospective, multicenter, observational study included 222 patients (mean age 66, range 46-75 years) who underwent prostate MRI, miRNA (let-7a-5p and miR-103a-3p) assessment, and biopsy. Monoparametric and multiparametric models including age, PSA, miRNA, and MRI outcome were trained on 65% of the data and then validated on the remaining 35% to predict both PCa (any Gleason grade [GG]) and csPCa (GG ≥ 2 vs GG = 1/negative). Accuracy, sensitivity, specificity, positive and negative predictive value (NPV), and area under the receiver operating characteristic curve were calculated. RESULTS MRI outcome was the best predictor in the monoparametric model for both detection of PCa, with sensitivity of 90% (95%CI 73-98%) and NPV of 93% (95%CI 82-98%), and for csPCa identification, with sensitivity of 91% (95%CI 72-99%) and NPV of 95% (95%CI 84-99%). Sensitivity and NPV of PSA + miRNA for the detection of csPCa were not statistically different from the other models including MRI alone. CONCLUSION MRI stand-alone yielded the best prediction models for both PCa and csPCa detection in biopsy-naïve patients. The use of miRNAs let-7a-5p and miR-103a-3p did not improve classification performances compared to MRI stand-alone results. CLINICAL RELEVANCE STATEMENT The use of miRNA (let-7a-5p and miR-103a-3p), PSA, and MRI in a clinical decision support system (CDSS) does not improve MRI stand-alone performance in the detection of PCa and csPCa. KEY POINTS • Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA. • The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance. • Results of this study were in line with previous works on MRI and microRNA.
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Affiliation(s)
- Simone Mazzetti
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Arianna Defeudis
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.
- Department of Surgical Sciences, University of Turin, Turin, Italy.
| | - Giulia Nicoletti
- Department of Surgical Sciences, University of Turin, Turin, Italy
- Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy
| | | | - Stefano De Luca
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Italy
| | - Riccardo Faletti
- Radiology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Paolo Gontero
- Division of Urology, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Matteo Manfredi
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Italy
| | | | | | - Andrea Zitella
- Division of Urology, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Francesco Porpiglia
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Italy
| | - Daniele Regge
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - Valentina Giannini
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Surgical Sciences, University of Turin, Turin, Italy
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2
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Bertelli E, Vizzi M, Marzi C, Pastacaldi S, Cinelli A, Legato M, Ruzga R, Bardazzi F, Valoriani V, Loverre F, Impagliazzo F, Cozzi D, Nardoni S, Facchiano D, Serni S, Masieri L, Minervini A, Agostini S, Miele V. Biparametric vs. Multiparametric MRI in the Detection of Cancer in Transperineal Targeted-Biopsy-Proven Peripheral Prostate Cancer Lesions Classified as PI-RADS Score 3 or 3+1: The Added Value of ADC Quantification. Diagnostics (Basel) 2024; 14:1608. [PMID: 39125483 PMCID: PMC11312064 DOI: 10.3390/diagnostics14151608] [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: 05/31/2024] [Revised: 07/21/2024] [Accepted: 07/25/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Biparametric MRI (bpMRI) has an important role in the diagnosis of prostate cancer (PCa), by reducing the cost and duration of the procedure and adverse reactions. We assess the additional benefit of the ADC map in detecting prostate cancer (PCa). Additionally, we examine whether the ADC value correlates with the presence of clinically significant tumors (csPCa). METHODS 104 peripheral lesions classified as PI-RADS v2.1 score 3 or 3+1 at the mpMRI underwent transperineal MRI/US fusion-guided targeted biopsy. RESULTS The lesions were classified as PI-RADS 3 or 3+1; at histopathology, 30 were adenocarcinomas, 21 of which were classified as csPCa. The ADC threshold that maximized the Youden index in order to predict the presence of a tumor was 1103 (95% CI (990, 1243)), with a sensitivity of 0.8 and a specificity of 0.59; both values were greater than those found using the contrast medium, which were 0.5 and 0.54, respectively. Similar results were also found with csPCa, where the optimal ADC threshold was 1096 (95% CI (988, 1096)), with a sensitivity of 0.86 and specificity of 0.59, compared to 0.49 and 0.59 observed in the mpMRI. CONCLUSIONS Our study confirms the possible use of a quantitative parameter (ADC value) in the risk stratification of csPCa, by reducing the number of biopsies and, therefore, the number of unwarranted diagnoses of PCa and the risk of overtreatment.
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Affiliation(s)
- Elena Bertelli
- Department of Radiology, Careggi University Hospital, 50134 Florence, Italy; (M.V.); (S.P.); (A.C.); (M.L.); (R.R.); (F.B.); (V.V.); (F.L.); (F.I.); (D.C.); (S.A.); (V.M.)
| | - Michele Vizzi
- Department of Radiology, Careggi University Hospital, 50134 Florence, Italy; (M.V.); (S.P.); (A.C.); (M.L.); (R.R.); (F.B.); (V.V.); (F.L.); (F.I.); (D.C.); (S.A.); (V.M.)
| | - Chiara Marzi
- Department of Statistics, Informatics and Applications “G. Parenti” (DiSIA), University of Florence, 50134 Florence, Italy;
| | - Sandro Pastacaldi
- Department of Radiology, Careggi University Hospital, 50134 Florence, Italy; (M.V.); (S.P.); (A.C.); (M.L.); (R.R.); (F.B.); (V.V.); (F.L.); (F.I.); (D.C.); (S.A.); (V.M.)
| | - Alberto Cinelli
- Department of Radiology, Careggi University Hospital, 50134 Florence, Italy; (M.V.); (S.P.); (A.C.); (M.L.); (R.R.); (F.B.); (V.V.); (F.L.); (F.I.); (D.C.); (S.A.); (V.M.)
| | - Martina Legato
- Department of Radiology, Careggi University Hospital, 50134 Florence, Italy; (M.V.); (S.P.); (A.C.); (M.L.); (R.R.); (F.B.); (V.V.); (F.L.); (F.I.); (D.C.); (S.A.); (V.M.)
| | - Ron Ruzga
- Department of Radiology, Careggi University Hospital, 50134 Florence, Italy; (M.V.); (S.P.); (A.C.); (M.L.); (R.R.); (F.B.); (V.V.); (F.L.); (F.I.); (D.C.); (S.A.); (V.M.)
| | - Federico Bardazzi
- Department of Radiology, Careggi University Hospital, 50134 Florence, Italy; (M.V.); (S.P.); (A.C.); (M.L.); (R.R.); (F.B.); (V.V.); (F.L.); (F.I.); (D.C.); (S.A.); (V.M.)
| | - Vittoria Valoriani
- Department of Radiology, Careggi University Hospital, 50134 Florence, Italy; (M.V.); (S.P.); (A.C.); (M.L.); (R.R.); (F.B.); (V.V.); (F.L.); (F.I.); (D.C.); (S.A.); (V.M.)
| | - Francesco Loverre
- Department of Radiology, Careggi University Hospital, 50134 Florence, Italy; (M.V.); (S.P.); (A.C.); (M.L.); (R.R.); (F.B.); (V.V.); (F.L.); (F.I.); (D.C.); (S.A.); (V.M.)
| | - Francesco Impagliazzo
- Department of Radiology, Careggi University Hospital, 50134 Florence, Italy; (M.V.); (S.P.); (A.C.); (M.L.); (R.R.); (F.B.); (V.V.); (F.L.); (F.I.); (D.C.); (S.A.); (V.M.)
| | - Diletta Cozzi
- Department of Radiology, Careggi University Hospital, 50134 Florence, Italy; (M.V.); (S.P.); (A.C.); (M.L.); (R.R.); (F.B.); (V.V.); (F.L.); (F.I.); (D.C.); (S.A.); (V.M.)
| | - Samuele Nardoni
- Unit of Urological Minimally Invasive, Robotic Surgery and Kidney Transplantation, Careggi Hospital, University of Florence, 50134 Florence, Italy; (S.N.); (D.F.); (S.S.); (L.M.)
| | - Davide Facchiano
- Unit of Urological Minimally Invasive, Robotic Surgery and Kidney Transplantation, Careggi Hospital, University of Florence, 50134 Florence, Italy; (S.N.); (D.F.); (S.S.); (L.M.)
| | - Sergio Serni
- Unit of Urological Minimally Invasive, Robotic Surgery and Kidney Transplantation, Careggi Hospital, University of Florence, 50134 Florence, Italy; (S.N.); (D.F.); (S.S.); (L.M.)
- Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy;
| | - Lorenzo Masieri
- Unit of Urological Minimally Invasive, Robotic Surgery and Kidney Transplantation, Careggi Hospital, University of Florence, 50134 Florence, Italy; (S.N.); (D.F.); (S.S.); (L.M.)
- Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy;
| | - Andrea Minervini
- Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy;
- Unit of Oncologic Minimally-Invasive Urology and Andrology, Careggi Hospital, 50134 Florence, Italy
| | - Simone Agostini
- Department of Radiology, Careggi University Hospital, 50134 Florence, Italy; (M.V.); (S.P.); (A.C.); (M.L.); (R.R.); (F.B.); (V.V.); (F.L.); (F.I.); (D.C.); (S.A.); (V.M.)
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, 50134 Florence, Italy; (M.V.); (S.P.); (A.C.); (M.L.); (R.R.); (F.B.); (V.V.); (F.L.); (F.I.); (D.C.); (S.A.); (V.M.)
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Mendes B, Domingues I, Santos J. Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions. J Clin Med 2024; 13:3907. [PMID: 38999473 PMCID: PMC11242211 DOI: 10.3390/jcm13133907] [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: 05/26/2024] [Revised: 06/27/2024] [Accepted: 07/01/2024] [Indexed: 07/14/2024] Open
Abstract
Background: Prostate Cancer (PCa) is asymptomatic at an early stage and often painless, requiring only active surveillance. External Beam Radiotherapy (EBRT) is currently a curative option for localised and locally advanced diseases and a palliative option for metastatic low-volume disease. Although highly effective, especially in a hypofractionation scheme, 17.4% to 39.4% of all patients suffer from cancer recurrence after EBRT. But, radiographic findings also correlate with significant differences in protein expression patterns. In the PCa EBRT workflow, several imaging modalities are available for grading, staging and contouring. Using image data characterisation algorithms (radiomics), one can provide a quantitative analysis of prognostic and predictive treatment outcomes. Methods: This literature review searched for original studies in radiomics for PCa in the context of EBRT. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review includes 73 new studies and analyses datasets, imaging modality, segmentation technique, feature extraction, selection and model building methods. Results: Magnetic Resonance Imaging (MRI) is the preferred imaging modality for radiomic studies in PCa but Computed Tomography (CT), Positron Emission Tomography (PET) and Ultrasound (US) may offer valuable insights on tumour characterisation and treatment response prediction. Conclusions: Most radiomic studies used small, homogeneous and private datasets lacking external validation and variability. Future research should focus on collaborative efforts to create large, multicentric datasets and develop standardised methodologies, ensuring the full potential of radiomics in clinical practice.
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Affiliation(s)
- Bruno Mendes
- Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), Medical Physics, Radiobiology and Radiological Protection Group, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Faculty of Engineering of the University of Porto (FEUP), R. Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Inês Domingues
- Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), Medical Physics, Radiobiology and Radiological Protection Group, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal
| | - João Santos
- Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), Medical Physics, Radiobiology and Radiological Protection Group, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- School of Medicine and Biomedical Sciences (ICBAS), R. Jorge de Viterbo Ferreira 228, 4050-313 Porto, Portugal
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4
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Li M, Ding N, Yin S, Lu Y, Ji Y, Jin L. Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI). Quant Imaging Med Surg 2024; 14:4893-4902. [PMID: 39022227 PMCID: PMC11250323 DOI: 10.21037/qims-24-587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Accepted: 05/20/2024] [Indexed: 07/20/2024]
Abstract
Background The aggressiveness of prostate cancer (PCa) is crucial in determining treatment method. The purpose of this study was to establish a 2.5-dimensional (2.5D) deep transfer learning (DTL) detection model for the automatic detection of clinically significant PCa (csPCa) based on bi-parametric magnetic resonance imaging (bp-MRI). Methods A total of 231 patients, including 181 with csPCa and 50 with non-clinically significant PCa (non-csPCa), were enrolled. Stratified random sampling was then employed to divide all participants into a training set [185] and a test set [46]. The DTL model was obtained through image acquisition, image segmentation, and model construction. Finally, the diagnostic performance of the 2.5D and 2-dimensional (2D) models in predicting the aggressiveness of PCa was evaluated and compared using receiver operating characteristic (ROC) curves. Results DTL models based on 2D and 2.5D segmentation were established and validated to assess the aggressiveness of PCa. The results demonstrated that the diagnostic efficiency of the DTL model based on 2.5D was superior to that of the 2D model, regardless of whether in a single or combined sequence. Particularly, the 2.5D combined model outperformed other models in differentiating csPCa from non-csPCa. The area under the curve (AUC) values for the 2.5D combined model in the training and test sets were 0.960 and 0.949, respectively. Furthermore, the T2-weighted imaging (T2WI) model showed superiority over the apparent diffusion coefficient (ADC) model, but was not as effective as the combined model, whether based on 2.5D or 2D. Conclusions A DTL model based on 2.5D segmentation was developed to automatically evaluate PCa aggressiveness on bp-MRI, improving the diagnostic performance of the 2D model. The results indicated that the continuous information between adjacent layers can enhance the detection rate of lesions and reduce the misjudgment rate based on the DTL model.
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Affiliation(s)
- Mengjuan Li
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China
| | - Ning Ding
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China
| | - Shengnan Yin
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China
| | - Yan Lu
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China
| | - Yiding Ji
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China
| | - Long Jin
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China
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5
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Pak S, Park SG, Park J, Cho ST, Lee YG, Ahn H. Applications of artificial intelligence in urologic oncology. Investig Clin Urol 2024; 65:202-216. [PMID: 38714511 PMCID: PMC11076794 DOI: 10.4111/icu.20230435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/24/2024] [Accepted: 03/11/2024] [Indexed: 05/10/2024] Open
Abstract
PURPOSE With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology. MATERIALS AND METHODS We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both "urology" and "artificial intelligence" with one of the following: "machine learning," "deep learning," "neural network," "renal cell carcinoma," "kidney cancer," "urothelial carcinoma," "bladder cancer," "prostate cancer," and "robotic surgery." RESULTS A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods. CONCLUSIONS AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.
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Affiliation(s)
- Sahyun Pak
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Sung Gon Park
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | | | - Sung Tae Cho
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Young Goo Lee
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Hanjong Ahn
- Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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6
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Pan N, Shi L, He D, Zhao J, Xiong L, Ma L, Li J, Ai K, Zhao L, Huang G. Prediction of prostate cancer aggressiveness using magnetic resonance imaging radiomics: a dual-center study. Discov Oncol 2024; 15:122. [PMID: 38625419 PMCID: PMC11019191 DOI: 10.1007/s12672-024-00980-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 04/11/2024] [Indexed: 04/17/2024] Open
Abstract
PURPOSE The Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa. MATERIAL AND METHODS A total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic. RESULTS The 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively. CONCLUSIONS MRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.
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Affiliation(s)
- Nini Pan
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Liuyan Shi
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Diliang He
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Jianxin Zhao
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Lianqiu Xiong
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Lili Ma
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Jing Li
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Kai Ai
- Clinical and Technical Support, Philips Healthcare, Xi'an, China
| | - Lianping Zhao
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China
| | - Gang Huang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China.
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Doo FX, Vosshenrich J, Cook TS, Moy L, Almeida EP, Woolen SA, Gichoya JW, Heye T, Hanneman K. Environmental Sustainability and AI in Radiology: A Double-Edged Sword. Radiology 2024; 310:e232030. [PMID: 38411520 PMCID: PMC10902597 DOI: 10.1148/radiol.232030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/21/2023] [Accepted: 11/17/2023] [Indexed: 02/28/2024]
Abstract
According to the World Health Organization, climate change is the single biggest health threat facing humanity. The global health care system, including medical imaging, must manage the health effects of climate change while at the same time addressing the large amount of greenhouse gas (GHG) emissions generated in the delivery of care. Data centers and computational efforts are increasingly large contributors to GHG emissions in radiology. This is due to the explosive increase in big data and artificial intelligence (AI) applications that have resulted in large energy requirements for developing and deploying AI models. However, AI also has the potential to improve environmental sustainability in medical imaging. For example, use of AI can shorten MRI scan times with accelerated acquisition times, improve the scheduling efficiency of scanners, and optimize the use of decision-support tools to reduce low-value imaging. The purpose of this Radiology in Focus article is to discuss this duality at the intersection of environmental sustainability and AI in radiology. Further discussed are strategies and opportunities to decrease AI-related emissions and to leverage AI to improve sustainability in radiology, with a focus on health equity. Co-benefits of these strategies are explored, including lower cost and improved patient outcomes. Finally, knowledge gaps and areas for future research are highlighted.
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Affiliation(s)
- Florence X. Doo
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Jan Vosshenrich
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Tessa S. Cook
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Linda Moy
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Eduardo P.R.P. Almeida
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Sean A. Woolen
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Judy Wawira Gichoya
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Tobias Heye
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Kate Hanneman
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
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8
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Nicoletti G, Mazzetti S, Maimone G, Cignini V, Cuocolo R, Faletti R, Gatti M, Imbriaco M, Longo N, Ponsiglione A, Russo F, Serafini A, Stanzione A, Regge D, Giannini V. Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI. Cancers (Basel) 2024; 16:203. [PMID: 38201630 PMCID: PMC10778513 DOI: 10.3390/cancers16010203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/19/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
In the last years, several studies demonstrated that low-aggressive (Grade Group (GG) ≤ 2) and high-aggressive (GG ≥ 3) prostate cancers (PCas) have different prognoses and mortality. Therefore, the aim of this study was to develop and externally validate a radiomic model to noninvasively classify low-aggressive and high-aggressive PCas based on biparametric magnetic resonance imaging (bpMRI). To this end, 283 patients were retrospectively enrolled from four centers. Features were extracted from apparent diffusion coefficient (ADC) maps and T2-weighted (T2w) sequences. A cross-validation (CV) strategy was adopted to assess the robustness of several classifiers using two out of the four centers. Then, the best classifier was externally validated using the other two centers. An explanation for the final radiomics signature was provided through Shapley additive explanation (SHAP) values and partial dependence plots (PDP). The best combination was a naïve Bayes classifier trained with ten features that reached promising results, i.e., an area under the receiver operating characteristic (ROC) curve (AUC) of 0.75 and 0.73 in the construction and external validation set, respectively. The findings of our work suggest that our radiomics model could help distinguish between low- and high-aggressive PCa. This noninvasive approach, if further validated and integrated into a clinical decision support system able to automatically detect PCa, could help clinicians managing men with suspicion of PCa.
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Affiliation(s)
- Giulia Nicoletti
- Department of Electronics and Telecommunications, Polytechnic of Turin, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy;
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Simone Mazzetti
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
| | - Giovanni Maimone
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
| | - Valentina Cignini
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Via Salvador Allende, 43, 84081 Baronissi, Italy;
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy; (M.I.); (A.P.)
| | - Nicola Longo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, 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; (M.I.); (A.P.)
| | - Filippo Russo
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
| | - Alessandro Serafini
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy; (M.I.); (A.P.)
| | - Daniele Regge
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
- Department of Translational Research, Via Risorgimento, 36, University of Pisa, 56126 Pisa, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
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9
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Stefano A, Bertelli E, Comelli A, Gatti M, Stanzione A. Editorial: Radiomics and radiogenomics in genitourinary oncology: artificial intelligence and deep learning applications. FRONTIERS IN RADIOLOGY 2023; 3:1325594. [PMID: 38192376 PMCID: PMC10773800 DOI: 10.3389/fradi.2023.1325594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 12/06/2023] [Indexed: 01/10/2024]
Affiliation(s)
- Alessandro Stefano
- Institute ofMolecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, Italy
| | - Elena Bertelli
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | | | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
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10
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Khanfari H, Mehranfar S, Cheki M, Mohammadi Sadr M, Moniri S, Heydarheydari S, Rezaeijo SM. Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI. BMC Med Imaging 2023; 23:195. [PMID: 37993801 PMCID: PMC10664625 DOI: 10.1186/s12880-023-01140-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 10/26/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND The purpose of this study is to investigate the use of radiomics and deep features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading prostate cancer. We propose a novel approach called multi-flavored feature extraction or tensor, which combines four mpMRI images using eight different fusion techniques to create 52 images or datasets for each patient. We evaluate the effectiveness of this approach in grading prostate cancer and compare it to traditional methods. METHODS We used the PROSTATEx-2 dataset consisting of 111 patients' images from T2W-transverse, T2W-sagittal, DWI, and ADC images. We used eight fusion techniques to merge T2W, DWI, and ADC images, namely Laplacian Pyramid, Ratio of the low-pass pyramid, Discrete Wavelet Transform, Dual-Tree Complex Wavelet Transform, Curvelet Transform, Wavelet Fusion, Weighted Fusion, and Principal Component Analysis. Prostate cancer images were manually segmented, and radiomics features were extracted using the Pyradiomics library in Python. We also used an Autoencoder for deep feature extraction. We used five different feature sets to train the classifiers: all radiomics features, all deep features, radiomics features linked with PCA, deep features linked with PCA, and a combination of radiomics and deep features. We processed the data, including balancing, standardization, PCA, correlation, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Finally, we used nine classifiers to classify different Gleason grades. RESULTS Our results show that the SVM classifier with deep features linked with PCA achieved the most promising results, with an AUC of 0.94 and a balanced accuracy of 0.79. Logistic regression performed best when using only the deep features, with an AUC of 0.93 and balanced accuracy of 0.76. Gaussian Naive Bayes had lower performance compared to other classifiers, while KNN achieved high performance using deep features linked with PCA. Random Forest performed well with the combination of deep features and radiomics features, achieving an AUC of 0.94 and balanced accuracy of 0.76. The Voting classifiers showed higher performance when using only the deep features, with Voting 2 achieving the highest performance, with an AUC of 0.95 and balanced accuracy of 0.78. CONCLUSION Our study concludes that the proposed multi-flavored feature extraction or tensor approach using radiomics and deep features can be an effective method for grading prostate cancer. Our findings suggest that deep features may be more effective than radiomics features alone in accurately classifying prostate cancer.
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Affiliation(s)
- Hasan Khanfari
- Department of Mechanical Engineering, Petroleum University of Technology, Ahvaz, Iran
| | - Saeed Mehranfar
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Mohsen Cheki
- Department of Medical Imaging and Radiation Sciences, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mahmoud Mohammadi Sadr
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Samir Moniri
- Department of Medical Imaging and Radiation Sciences, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Sahel Heydarheydari
- Department of Medical Imaging and Radiation Sciences, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Seyed Masoud Rezaeijo
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
- Cancer Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
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11
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Demircioğlu A. Deep Features from Pretrained Networks Do Not Outperform Hand-Crafted Features in Radiomics. Diagnostics (Basel) 2023; 13:3266. [PMID: 37892087 PMCID: PMC10606594 DOI: 10.3390/diagnostics13203266] [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: 09/14/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
In radiomics, utilizing features extracted from pretrained deep networks could result in models with a higher predictive performance than those relying on hand-crafted features. This study compared the predictive performance of models trained with either deep features, hand-crafted features, or a combination of these features in terms of the area under the receiver-operating characteristic curve (AUC) and other metrics. We trained models on ten radiological datasets using five feature selection methods and three classifiers. Our results indicate that models based on deep features did not show an improved AUC compared to those utilizing hand-crafted features (deep: AUC 0.775, hand-crafted: AUC 0.789; p = 0.28). Including morphological features alongside deep features led to overall improvements in prediction performance for all models (+0.02 gain in AUC; p < 0.001); however, the best model did not benefit from this (+0.003 gain in AUC; p = 0.57). Using all hand-crafted features in addition to the deep features resulted in a further overall improvement (+0.034 in AUC; p < 0.001), but only a minor improvement could be observed for the best model (deep: AUC 0.798, hand-crafted: AUC 0.789; p = 0.92). Furthermore, our results show that models based on deep features extracted from networks pretrained on medical data have no advantage in predictive performance over models relying on features extracted from networks pretrained on ImageNet data. Our study contributes a benchmarking analysis of models trained on hand-crafted and deep features from pretrained networks across multiple datasets. It also provides a comprehensive understanding of their applicability and limitations in radiomics. Our study shows, in conclusion, that models based on features extracted from pretrained deep networks do not outperform models trained on hand-crafted ones.
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147 Essen, Germany
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12
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Duenweg SR, Bobholz SA, Barrett MJ, Lowman AK, Winiarz A, Nath B, Stebbins M, Bukowy J, Iczkowski KA, Jacobsohn KM, Vincent-Sheldon S, LaViolette PS. T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence. Cancers (Basel) 2023; 15:4437. [PMID: 37760407 PMCID: PMC10526331 DOI: 10.3390/cancers15184437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.
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Affiliation(s)
- Savannah R. Duenweg
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Samuel A. Bobholz
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Michael J. Barrett
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Allison K. Lowman
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Aleksandra Winiarz
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Biprojit Nath
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Margaret Stebbins
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - John Bukowy
- Department of Electrical Engineering and Computer Science, Milwaukee School of Engineering, 1025 N Broadway, Milwaukee, WI 53202, USA
| | - Kenneth A. Iczkowski
- Department of Pathology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA;
| | - Kenneth M. Jacobsohn
- Department of Urology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Stephanie Vincent-Sheldon
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Peter S. LaViolette
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
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13
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Pachetti E, Colantonio S. 3D-Vision-Transformer Stacking Ensemble for Assessing Prostate Cancer Aggressiveness from T2w Images. Bioengineering (Basel) 2023; 10:1015. [PMID: 37760117 PMCID: PMC10525095 DOI: 10.3390/bioengineering10091015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/27/2023] [Accepted: 08/20/2023] [Indexed: 09/29/2023] Open
Abstract
Vision transformers represent the cutting-edge topic in computer vision and are usually employed on two-dimensional data following a transfer learning approach. In this work, we propose a trained-from-scratch stacking ensemble of 3D-vision transformers to assess prostate cancer aggressiveness from T2-weighted images to help radiologists diagnose this disease without performing a biopsy. We trained 18 3D-vision transformers on T2-weighted axial acquisitions and combined them into two- and three-model stacking ensembles. We defined two metrics for measuring model prediction confidence, and we trained all the ensemble combinations according to a five-fold cross-validation, evaluating their accuracy, confidence in predictions, and calibration. In addition, we optimized the 18 base ViTs and compared the best-performing base and ensemble models by re-training them on a 100-sample bootstrapped training set and evaluating each model on the hold-out test set. We compared the two distributions by calculating the median and the 95% confidence interval and performing a Wilcoxon signed-rank test. The best-performing 3D-vision-transformer stacking ensemble provided state-of-the-art results in terms of area under the receiving operating curve (0.89 [0.61-1]) and exceeded the area under the precision-recall curve of the base model of 22% (p < 0.001). However, it resulted to be less confident in classifying the positive class.
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Affiliation(s)
- Eva Pachetti
- “Alessandro Faedo” Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), 56127 Pisa, Italy;
- Department of Information Engineering (DII), University of Pisa, 56122 Pisa, Italy
| | - Sara Colantonio
- “Alessandro Faedo” Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), 56127 Pisa, Italy;
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14
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Lee HW, Kim E, Na I, Kim CK, Seo SI, Park H. Novel Multiparametric Magnetic Resonance Imaging-Based Deep Learning and Clinical Parameter Integration for the Prediction of Long-Term Biochemical Recurrence-Free Survival in Prostate Cancer after Radical Prostatectomy. Cancers (Basel) 2023; 15:3416. [PMID: 37444526 DOI: 10.3390/cancers15133416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/19/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
Radical prostatectomy (RP) is the main treatment of prostate cancer (PCa). Biochemical recurrence (BCR) following RP remains the first sign of aggressive disease; hence, better assessment of potential long-term post-RP BCR-free survival is crucial. Our study aimed to evaluate a combined clinical-deep learning (DL) model using multiparametric magnetic resonance imaging (mpMRI) for predicting long-term post-RP BCR-free survival in PCa. A total of 437 patients with PCa who underwent mpMRI followed by RP between 2008 and 2009 were enrolled; radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced sequences by manually delineating the index tumors. Deep features from the same set of imaging were extracted using a deep neural network based on pretrained EfficentNet-B0. Here, we present a clinical model (six clinical variables), radiomics model, DL model (DLM-Deep feature), combined clinical-radiomics model (CRM-Multi), and combined clinical-DL model (CDLM-Deep feature) that were built using Cox models regularized with the least absolute shrinkage and selection operator. We compared their prognostic performances using stratified fivefold cross-validation. In a median follow-up of 61 months, 110/437 patients experienced BCR. CDLM-Deep feature achieved the best performance (hazard ratio [HR] = 7.72), followed by DLM-Deep feature (HR = 4.37) or RM-Multi (HR = 2.67). CRM-Multi performed moderately. Our results confirm the superior performance of our mpMRI-derived DL algorithm over conventional radiomics.
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Affiliation(s)
- Hye Won Lee
- Samsung Medical Center, Department of Urology, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Eunjin Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Inye Na
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Seong Il Seo
- Samsung Medical Center, Department of Urology, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Hyunjin Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, Republic of Korea
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15
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Rodrigues A, Rodrigues N, Santinha J, Lisitskaya MV, Uysal A, Matos C, Domingues I, Papanikolaou N. Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness. Sci Rep 2023; 13:6206. [PMID: 37069257 PMCID: PMC10110526 DOI: 10.1038/s41598-023-33339-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 04/11/2023] [Indexed: 04/19/2023] Open
Abstract
There is a growing piece of evidence that artificial intelligence may be helpful in the entire prostate cancer disease continuum. However, building machine learning algorithms robust to inter- and intra-radiologist segmentation variability is still a challenge. With this goal in mind, several model training approaches were compared: removing unstable features according to the intraclass correlation coefficient (ICC); training independently with features extracted from each radiologist's mask; training with the feature average between both radiologists; extracting radiomic features from the intersection or union of masks; and creating a heterogeneous dataset by randomly selecting one of the radiologists' masks for each patient. The classifier trained with this last resampled dataset presented with the lowest generalization error, suggesting that training with heterogeneous data leads to the development of the most robust classifiers. On the contrary, removing features with low ICC resulted in the highest generalization error. The selected radiomics dataset, with the randomly chosen radiologists, was concatenated with deep features extracted from neural networks trained to segment the whole prostate. This new hybrid dataset was then used to train a classifier. The results revealed that, even though the hybrid classifier was less overfitted than the one trained with deep features, it still was unable to outperform the radiomics model.
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Affiliation(s)
- Ana Rodrigues
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
- Faculty of Medicine, University of Porto, Porto, Portugal.
| | - Nuno Rodrigues
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
- LASIGE, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - João Santinha
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
- Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | - Maria V Lisitskaya
- Cand. of Sci. (Med.), Radiologist at Radiology Department with CT and MRI, Medical Research and Educational Center, Lomonosov Moscow State University, Moscow, Russia
| | - Aycan Uysal
- Gulhane Medical School, University of Health Sciences, Ankara, Turkey
| | - Celso Matos
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Inês Domingues
- Instituto Politécnico de Coimbra, Instituto Superior de Engenharia, Rua Pedro Nunes-Quinta da Nora, 3030-199, Coimbra, Portugal
- Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP): Grupo de Física Médica, Radiobiologia e Protecção Radiológica, Porto, Portugal
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16
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Stanzione A, Ponsiglione A, Alessandrino F, Brembilla G, Imbriaco M. Beyond diagnosis: is there a role for radiomics in prostate cancer management? Eur Radiol Exp 2023; 7:13. [PMID: 36907973 PMCID: PMC10008761 DOI: 10.1186/s41747-023-00321-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/05/2023] [Indexed: 03/13/2023] Open
Abstract
The role of imaging in pretreatment staging and management of prostate cancer (PCa) is constantly evolving. In the last decade, there has been an ever-growing interest in radiomics as an image analysis approach able to extract objective quantitative features that are missed by human eye. However, most of PCa radiomics studies have been focused on cancer detection and characterisation. With this narrative review we aimed to provide a synopsis of the recently proposed potential applications of radiomics for PCa with a management-based approach, focusing on primary treatments with curative intent and active surveillance as well as highlighting on recurrent disease after primary treatment. Current evidence is encouraging, with radiomics and artificial intelligence appearing as feasible tools to aid physicians in planning PCa management. However, the lack of external independent datasets for validation and prospectively designed studies casts a shadow on the reliability and generalisability of radiomics models, delaying their translation into clinical practice.Key points• Artificial intelligence solutions have been proposed to streamline prostate cancer radiotherapy planning.• Radiomics models could improve risk assessment for radical prostatectomy patient selection.• Delta-radiomics appears promising for the management of patients under active surveillance.• Radiomics might outperform current nomograms for prostate cancer recurrence risk assessment.• Reproducibility of results, methodological and ethical issues must still be faced before clinical implementation.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | | | - Giorgio Brembilla
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
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Liao J, Li X, Gan Y, Han S, Rong P, Wang W, Li W, Zhou L. Artificial intelligence assists precision medicine in cancer treatment. Front Oncol 2023; 12:998222. [PMID: 36686757 PMCID: PMC9846804 DOI: 10.3389/fonc.2022.998222] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/22/2022] [Indexed: 01/06/2023] Open
Abstract
Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.
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Affiliation(s)
- Jinzhuang Liao
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaoying Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yu Gan
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Shuangze Han
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Pengfei Rong
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Wang
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Li Zhou
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,Department of Pathology, The Xiangya Hospital of Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
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Awotunde JB, Imoize AL, Ayoade OB, Abiodun MK, Do DT, Silva A, Sur SN. An Enhanced Hyper-Parameter Optimization of a Convolutional Neural Network Model for Leukemia Cancer Diagnosis in a Smart Healthcare System. SENSORS (BASEL, SWITZERLAND) 2022; 22:9689. [PMID: 36560057 PMCID: PMC9785310 DOI: 10.3390/s22249689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
Healthcare systems in recent times have witnessed timely diagnoses with a high level of accuracy. Internet of Medical Things (IoMT)-enabled deep learning (DL) models have been used to support medical diagnostics in real time, thus resolving the issue of late-stage diagnosis of various diseases and increasing performance accuracy. The current approach for the diagnosis of leukemia uses traditional procedures, and in most cases, fails in the initial period. Hence, several patients suffering from cancer have died prematurely due to the late discovery of cancerous cells in blood tissue. Therefore, this study proposes an IoMT-enabled convolutional neural network (CNN) model to detect malignant and benign cancer cells in the patient's blood tissue. In particular, the hyper-parameter optimization through radial basis function and dynamic coordinate search (HORD) optimization algorithm was used to search for optimal values of CNN hyper-parameters. Utilizing the HORD algorithm significantly increased the effectiveness of finding the best solution for the CNN model by searching multidimensional hyper-parameters. This implies that the HORD method successfully found the values of hyper-parameters for precise leukemia features. Additionally, the HORD method increased the performance of the model by optimizing and searching for the best set of hyper-parameters for the CNN model. Leukemia datasets were used to evaluate the performance of the proposed model using standard performance indicators. The proposed model revealed significant classification accuracy compared to other state-of-the-art models.
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Affiliation(s)
- Joseph Bamidele Awotunde
- Department of Computer Science, Faculty of Information and Communication Sciences, University of Ilorin, Ilorin 240003, Nigeria
| | - Agbotiname Lucky Imoize
- Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria
- Department of Electrical Engineering and Information Technology, Institute of Digital Communication, Ruhr University, 44801 Bochum, Germany
| | - Oluwafisayo Babatope Ayoade
- Department of Computing and Information Science, School of Pure & Applied Sciences, College of Science, Bamidele Olumilua University of Education, Science & Technology, Ikere-Ekiti 361264, Nigeria
| | | | - Dinh-Thuan Do
- Department of Computer Science and Information Engineering, College of Information and Electrical Engineering, Asia University, Taichung 41354, Taiwan
| | - Adão Silva
- Instituto de Telecomunicações (IT) and Departamento de Eletrónica, Telecomunicações e Informática (DETI), University of Aveiro, 3810-193 Aveiro, Portugal
| | - Samarendra Nath Sur
- Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Rangpo 737136, Sikkim, India
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Borgheresi R, Barucci A, Colantonio S, Aghakhanyan G, Assante M, Bertelli E, Carlini E, Carpi R, Caudai C, Cavallero D, Cioni D, Cirillo R, Colcelli V, Dell’Amico A, Di Gangi D, Erba PA, Faggioni L, Falaschi Z, Gabelloni M, Gini R, Lelii L, Liò P, Lorito A, Lucarini S, Manghi P, Mangiacrapa F, Marzi C, Mazzei MA, Mercatelli L, Mirabile A, Mungai F, Miele V, Olmastroni M, Pagano P, Paiar F, Panichi G, Pascali MA, Pasquinelli F, Shortrede JE, Tumminello L, Volterrani L, Neri E. NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients. Eur Radiol Exp 2022; 6:53. [PMID: 36344838 PMCID: PMC9640522 DOI: 10.1186/s41747-022-00306-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/21/2022] [Indexed: 11/09/2022] Open
Abstract
NAVIGATOR is an Italian regional project boosting precision medicine in oncology with the aim of making it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project’s goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e., standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence.
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20
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Corsi A, De Bernardi E, Bonaffini PA, Franco PN, Nicoletta D, Simonini R, Ippolito D, Perugini G, Occhipinti M, Da Pozzo LF, Roscigno M, Sironi S. Radiomics in PI-RADS 3 Multiparametric MRI for Prostate Cancer Identification: Literature Models Re-Implementation and Proposal of a Clinical-Radiological Model. J Clin Med 2022; 11:6304. [PMID: 36362530 PMCID: PMC9656103 DOI: 10.3390/jcm11216304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/18/2022] [Accepted: 10/22/2022] [Indexed: 10/29/2023] Open
Abstract
PI-RADS 3 prostate lesions clinical management is still debated, with high variability among different centers. Identifying clinically significant tumors among PI-RADS 3 is crucial. Radiomics applied to multiparametric MR (mpMR) seems promising. Nevertheless, reproducibility assessment by external validation is required. We retrospectively included all patients with at least one PI-RADS 3 lesion (PI-RADS v2.1) detected on a 3T prostate MRI scan at our Institution (June 2016-March 2021). An MRI-targeted biopsy was used as ground truth. We assessed reproducible mpMRI radiomic features found in the literature. Then, we proposed a new model combining PSA density and two radiomic features (texture regularity (T2) and size zone heterogeneity (ADC)). All models were trained/assessed through 100-repetitions 5-fold cross-validation. Eighty patients were included (26 with GS ≥ 7). In total, 9/20 T2 features (Hector's model) and 1 T2 feature (Jin's model) significantly correlated to biopsy on our dataset. PSA density alone predicted clinically significant tumors (sensitivity: 66%; specificity: 71%). Our model obtained a sensitivity of 80% and a specificity of 76%. Standard-compliant works with detailed methodologies achieve comparable radiomic feature sets. Therefore, efforts to facilitate reproducibility are needed, while complex models and imaging protocols seem not, since our model combining PSA density and two radiomic features from routinely performed sequences appeared to differentiate clinically significant cancers.
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Affiliation(s)
- Andrea Corsi
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Elisabetta De Bernardi
- Medicine and Surgery Department, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy
- Interdepartmental Research Centre Bicocca Bioinformatics Biostatistics and Bioimaging Centre-B4, University of Milano-Bicocca, Via Follereau 3, 20854 Vedano al Lambro, Italy
| | - Pietro Andrea Bonaffini
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Paolo Niccolò Franco
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Dario Nicoletta
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Roberto Simonini
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Davide Ippolito
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
- Department of Radiology, San Gerardo Hospital, Via G. B. Pergolesi 33, 20900 Monza, Italy
| | - Giovanna Perugini
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
| | | | - Luigi Filippo Da Pozzo
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
- Department of Urology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
| | - Marco Roscigno
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
- Department of Urology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
| | - Sandro Sironi
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
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21
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Li C, Li W, Liu C, Zheng H, Cai J, Wang S. Artificial intelligence in multi-parametric magnetic resonance imaging: A review. Med Phys 2022; 49:e1024-e1054. [PMID: 35980348 DOI: 10.1002/mp.15936] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/01/2022] [Accepted: 08/04/2022] [Indexed: 11/06/2022] Open
Abstract
Multi-parametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availability of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Cheng Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.,Peng Cheng Laboratory, Shenzhen, 518066, China.,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
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22
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Li L, Gu L, Kang B, Yang J, Wu Y, Liu H, Lai S, Wu X, Jiang J. Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions. Front Oncol 2022; 12:934108. [PMID: 35865467 PMCID: PMC9295912 DOI: 10.3389/fonc.2022.934108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/07/2022] [Indexed: 12/24/2022] Open
Abstract
ObjectiveTo compare the performance of different imaging classifiers in the prospective diagnosis of prostate diseases based on multiparameter MRI.MethodsA total of 238 patients with pathological outcomes were enrolled from September 2019 to July 2021, including 142 in the training set and 96 in the test set. After the regions of interest were manually segmented, decision tree (DT), Gaussian naive Bayes (GNB), XGBoost, logistic regression, random forest (RF) and support vector machine classifier (SVC) models were established on the training set and tested on the independent test set. The prospective diagnostic performance of each classifier was compared by using the AUC, F1-score and Brier score.ResultsIn the patient-based data set, the top three classifiers of combined sequences in terms of the AUC were logistic regression (0.865), RF (0.862), and DT (0.852); RF “was significantly different from the other two classifiers (P =0.022, P =0.005), while logistic regression and DT had no statistical significance (P =0.802). In the lesions-based data set, the top three classifiers of combined sequences in terms of the AUC were RF (0.931), logistic regression (0.922) and GNB (0.922). These three classifiers were significantly different from.ConclusionThe results of this experiment show that radiomics has a high diagnostic efficiency for prostate lesions. The RF classifier generally performed better overall than the other classifiers in the experiment. The XGBoost and logistic regression models also had high classification value in the lesions-based data set.
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Affiliation(s)
- Linghao Li
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Lili Gu
- Department of Pain, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Bin Kang
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Jiaojiao Yang
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Ying Wu
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Hao Liu
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Shasha Lai
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Xueting Wu
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Jian Jiang
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
- *Correspondence: Jian Jiang,
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Diffusion-Weighted MRI in the Genitourinary System. J Clin Med 2022; 11:jcm11071921. [PMID: 35407528 PMCID: PMC9000195 DOI: 10.3390/jcm11071921] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 03/25/2022] [Accepted: 03/28/2022] [Indexed: 12/12/2022] Open
Abstract
Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.
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