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Bellin MF, Valente C, Bekdache O, Maxwell F, Balasa C, Savignac A, Meyrignac O. Update on Renal Cell Carcinoma Diagnosis with Novel Imaging Approaches. Cancers (Basel) 2024; 16:1926. [PMID: 38792005 PMCID: PMC11120239 DOI: 10.3390/cancers16101926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/06/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer.
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Affiliation(s)
- Marie-France Bellin
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
- Faculté de Médecine, University of Paris-Saclay, 63 Rue Gabriel Péri, 94276 Le Kremlin-Bicêtre, France
- BioMaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94805 Villejuif, France
| | - Catarina Valente
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
| | - Omar Bekdache
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
| | - Florian Maxwell
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
| | - Cristina Balasa
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
| | - Alexia Savignac
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
| | - Olivier Meyrignac
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
- Faculté de Médecine, University of Paris-Saclay, 63 Rue Gabriel Péri, 94276 Le Kremlin-Bicêtre, France
- BioMaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94805 Villejuif, France
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Mastroianni R, Chiacchio G, Perpepaj L, Tuderti G, Brassetti A, Anceschi U, Ferriero M, Misuraca L, D’Annunzio S, Bove AM, Guaglianone S, Flammia RS, Proietti F, Pula M, Milanese G, Leonardo C, Galosi AB, Simone G. Comparison of Perioperative, Functional, and Oncologic Outcomes of Open vs. Robot-Assisted Off-Clamp Partial Nephrectomy: A Propensity Scored Match Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:2822. [PMID: 38732928 PMCID: PMC11086121 DOI: 10.3390/s24092822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/15/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024]
Abstract
Off-clamp partial nephrectomy represents one of the latest developments in nephron-sparing surgery, with the goal of preserving renal function and reducing ischemia time. The aim of this study was to evaluate and compare the functional, oncologic, and perioperative outcomes between off-clamp robot-assisted partial nephrectomy (off-C RAPN) and off-clamp open partial nephrectomy (off-C OPN) through a propensity score-matched (PSM) analysis. A 1:1 PSM analysis was used to balance variables potentially affecting postoperative outcomes. To report surgical quality, 1 year trifecta was used. Univariable Cox regression analysis was performed to identify predictors of trifecta achievement. The Kaplan-Meier method was used to compare cancer-specific survival (CSS), overall survival (OS), disease-free survival (DFS), and metastasis-free survival (MFS) probabilities between groups. Overall, 542 patients were included. After PSM analysis, two homogeneous cohorts of 147 patients were obtained. The off-C RAPN cohort experienced shorter length of stay (LoS) (3.4 days vs. 5.4 days; p < 0.001), increased likelihoods of achieving 1 year trifecta (89.8% vs. 80.3%; p = 0.03), lower postoperative Clavien-Dindo ≤ 2 complications (1.3% vs. 18.3%, p < 0.001), and lower postoperative transfusion rates (3.4% vs. 12.2%, p = 0.008). At univariable analysis, the surgical approach (off-C RAPN vs. off-C OPN, OR 2.22, 95% CI 1.09-4.46, p = 0.02) was the only predictor of 1 year trifecta achievement. At Kaplan-Meier analysis, no differences were observed between the two groups in terms of OS (log-rank p = 0.451), CSS (log-rank p = 0.476), DFS (log-rank p = 0.678), and MFS (log-rank p = 0.226). Comparing RAPN and OPN in a purely off-clamp scenario, the minimally invasive approach proved to be a feasible and safe surgical approach, with a significantly lower LoS and minor rate of postoperative complications and transfusions as a result of improved surgical quality expressed by higher 1 year trifecta achievement.
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Affiliation(s)
- Riccardo Mastroianni
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Giuseppe Chiacchio
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
- Urology Division, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, 60126 Ancona, Italy; (L.P.); (G.M.); (A.B.G.)
| | - Leonard Perpepaj
- Urology Division, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, 60126 Ancona, Italy; (L.P.); (G.M.); (A.B.G.)
| | - Gabriele Tuderti
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Aldo Brassetti
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Umberto Anceschi
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Mariaconsiglia Ferriero
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Leonardo Misuraca
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Simone D’Annunzio
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Alfredo Maria Bove
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Salvatore Guaglianone
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Rocco Simone Flammia
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Flavia Proietti
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Marco Pula
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Giulio Milanese
- Urology Division, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, 60126 Ancona, Italy; (L.P.); (G.M.); (A.B.G.)
| | - Costantino Leonardo
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Andrea Benedetto Galosi
- Urology Division, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, 60126 Ancona, Italy; (L.P.); (G.M.); (A.B.G.)
| | - Giuseppe Simone
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
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3
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Bolocan VO, Secareanu M, Sava E, Medar C, Manolescu LSC, Cătălin Rașcu AȘ, Costache MG, Radavoi GD, Dobran RA, Jinga V. Convolutional Neural Network Model for Segmentation and Classification of Clear Cell Renal Cell Carcinoma Based on Multiphase CT Images. J Imaging 2023; 9:280. [PMID: 38132698 PMCID: PMC10743786 DOI: 10.3390/jimaging9120280] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/08/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
(1) Background: Computed tomography (CT) imaging challenges in diagnosing renal cell carcinoma (RCC) include distinguishing malignant from benign tissues and determining the likely subtype. The goal is to show the algorithm's ability to improve renal cell carcinoma identification and treatment, improving patient outcomes. (2) Methods: This study uses the European Deep-Health toolkit's Convolutional Neural Network with ECVL, (European Computer Vision Library), and EDDL, (European Distributed Deep Learning Library). Image segmentation utilized U-net architecture and classification with resnet101. The model's clinical efficiency was assessed utilizing kidney, tumor, Dice score, and renal cell carcinoma categorization quality. (3) Results: The raw dataset contains 457 healthy right kidneys, 456 healthy left kidneys, 76 pathological right kidneys, and 84 pathological left kidneys. Preparing raw data for analysis was crucial to algorithm implementation. Kidney segmentation performance was 0.84, and tumor segmentation mean Dice score was 0.675 for the suggested model. Renal cell carcinoma classification was 0.885 accurate. (4) Conclusion and key findings: The present study focused on analyzing data from both healthy patients and diseased renal patients, with a particular emphasis on data processing. The method achieved a kidney segmentation accuracy of 0.84 and mean Dice scores of 0.675 for tumor segmentation. The system performed well in classifying renal cell carcinoma, achieving an accuracy of 0.885, results which indicates that the technique has the potential to improve the diagnosis of kidney pathology.
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Affiliation(s)
- Vlad-Octavian Bolocan
- Department of Fundamental Sciences, Faculty of Midwifery and Nursing, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (V.-O.B.); (C.M.); (M.G.C.)
- Department of Clinical Laboratory of Radiology and Medical Imaging, Clinical Hospital “Prof. Dr. Theodor Burghele”, 050664 Bucharest, Romania; (M.S.); (E.S.)
| | - Mihaela Secareanu
- Department of Clinical Laboratory of Radiology and Medical Imaging, Clinical Hospital “Prof. Dr. Theodor Burghele”, 050664 Bucharest, Romania; (M.S.); (E.S.)
| | - Elena Sava
- Department of Clinical Laboratory of Radiology and Medical Imaging, Clinical Hospital “Prof. Dr. Theodor Burghele”, 050664 Bucharest, Romania; (M.S.); (E.S.)
| | - Cosmin Medar
- Department of Fundamental Sciences, Faculty of Midwifery and Nursing, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (V.-O.B.); (C.M.); (M.G.C.)
- Department of Clinical Laboratory of Radiology and Medical Imaging, Clinical Hospital “Prof. Dr. Theodor Burghele”, 050664 Bucharest, Romania; (M.S.); (E.S.)
| | - Loredana Sabina Cornelia Manolescu
- Department of Fundamental Sciences, Faculty of Midwifery and Nursing, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (V.-O.B.); (C.M.); (M.G.C.)
| | - Alexandru-Ștefan Cătălin Rașcu
- Department of Urology, Clinical Hospital “Prof. Dr. Theodor Burghele”, Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (A.-Ș.C.R.); (G.D.R.); (V.J.)
- Department of Urology, Clinical Hospital “Prof. Dr. Theodor Burghele”, 050664 Bucharest, Romania
| | - Maria Glencora Costache
- Department of Fundamental Sciences, Faculty of Midwifery and Nursing, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (V.-O.B.); (C.M.); (M.G.C.)
| | - George Daniel Radavoi
- Department of Urology, Clinical Hospital “Prof. Dr. Theodor Burghele”, Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (A.-Ș.C.R.); (G.D.R.); (V.J.)
- Department of Urology, Clinical Hospital “Prof. Dr. Theodor Burghele”, 050664 Bucharest, Romania
| | | | - Viorel Jinga
- Department of Urology, Clinical Hospital “Prof. Dr. Theodor Burghele”, Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (A.-Ș.C.R.); (G.D.R.); (V.J.)
- Department of Urology, Clinical Hospital “Prof. Dr. Theodor Burghele”, 050664 Bucharest, Romania
- Medical Sciences Section, Academy of Romanian Scientists, 050085 Bucharest, Romania
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Rodriguez Peñaranda N, Eissa A, Ferretti S, Bianchi G, Di Bari S, Farinha R, Piazza P, Checcucci E, Belenchón IR, Veccia A, Gomez Rivas J, Taratkin M, Kowalewski KF, Rodler S, De Backer P, Cacciamani GE, De Groote R, Gallagher AG, Mottrie A, Micali S, Puliatti S. Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature. Diagnostics (Basel) 2023; 13:3070. [PMID: 37835812 PMCID: PMC10572445 DOI: 10.3390/diagnostics13193070] [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: 08/22/2023] [Revised: 09/17/2023] [Accepted: 09/24/2023] [Indexed: 10/15/2023] Open
Abstract
The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI's role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine's evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.
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Affiliation(s)
- Natali Rodriguez Peñaranda
- Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy; (N.R.P.); (A.E.); (S.F.); (G.B.); (S.D.B.); (S.M.)
| | - Ahmed Eissa
- Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy; (N.R.P.); (A.E.); (S.F.); (G.B.); (S.D.B.); (S.M.)
- Department of Urology, Faculty of Medicine, Tanta University, Tanta 31527, Egypt
| | - Stefania Ferretti
- Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy; (N.R.P.); (A.E.); (S.F.); (G.B.); (S.D.B.); (S.M.)
| | - Giampaolo Bianchi
- Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy; (N.R.P.); (A.E.); (S.F.); (G.B.); (S.D.B.); (S.M.)
| | - Stefano Di Bari
- Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy; (N.R.P.); (A.E.); (S.F.); (G.B.); (S.D.B.); (S.M.)
| | - Rui Farinha
- Orsi Academy, 9090 Melle, Belgium; (R.F.); (P.D.B.); (R.D.G.); (A.G.G.); (A.M.)
- Urology Department, Lusíadas Hospital, 1500-458 Lisbon, Portugal
| | - Pietro Piazza
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Enrico Checcucci
- Department of Surgery, FPO-IRCCS Candiolo Cancer Institute, 10060 Turin, Italy;
| | - Inés Rivero Belenchón
- Urology and Nephrology Department, Virgen del Rocío University Hospital, 41013 Seville, Spain;
| | - Alessandro Veccia
- Department of Urology, University of Verona, Azienda Ospedaliera Universitaria Integrata, 37126 Verona, Italy;
| | - Juan Gomez Rivas
- Department of Urology, Hospital Clinico San Carlos, 28040 Madrid, Spain;
| | - Mark Taratkin
- Institute for Urology and Reproductive Health, Sechenov University, 119435 Moscow, Russia;
| | - Karl-Friedrich Kowalewski
- Department of Urology and Urosurgery, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany;
| | - Severin Rodler
- Department of Urology, University Hospital LMU Munich, 80336 Munich, Germany;
| | - Pieter De Backer
- Orsi Academy, 9090 Melle, Belgium; (R.F.); (P.D.B.); (R.D.G.); (A.G.G.); (A.M.)
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Giovanni Enrico Cacciamani
- USC Institute of Urology, Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA;
- AI Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA 90089, USA
| | - Ruben De Groote
- Orsi Academy, 9090 Melle, Belgium; (R.F.); (P.D.B.); (R.D.G.); (A.G.G.); (A.M.)
| | - Anthony G. Gallagher
- Orsi Academy, 9090 Melle, Belgium; (R.F.); (P.D.B.); (R.D.G.); (A.G.G.); (A.M.)
- Faculty of Life and Health Sciences, Ulster University, Derry BT48 7JL, UK
| | - Alexandre Mottrie
- Orsi Academy, 9090 Melle, Belgium; (R.F.); (P.D.B.); (R.D.G.); (A.G.G.); (A.M.)
| | - Salvatore Micali
- Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy; (N.R.P.); (A.E.); (S.F.); (G.B.); (S.D.B.); (S.M.)
| | - Stefano Puliatti
- Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy; (N.R.P.); (A.E.); (S.F.); (G.B.); (S.D.B.); (S.M.)
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Najjar R. Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics (Basel) 2023; 13:2760. [PMID: 37685300 PMCID: PMC10487271 DOI: 10.3390/diagnostics13172760] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/01/2023] [Accepted: 08/10/2023] [Indexed: 09/10/2023] Open
Abstract
This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology-data quality, the 'black box' enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.
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Affiliation(s)
- Reabal Najjar
- Canberra Health Services, Australian Capital Territory 2605, Australia
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Gutiérrez Hidalgo B, Gómez Rivas J, de la Parra I, Marugán MJ, Serrano Á, Hermida Gutiérrez JF, Barrera J, Moreno-Sierra J. The Use of Radiomic Tools in Renal Mass Characterization. Diagnostics (Basel) 2023; 13:2743. [PMID: 37685281 PMCID: PMC10487148 DOI: 10.3390/diagnostics13172743] [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: 06/01/2023] [Revised: 07/26/2023] [Accepted: 08/07/2023] [Indexed: 09/10/2023] Open
Abstract
The incidence of renal mass detection has increased during recent decades, with an increased diagnosis of small renal masses, and a final benign diagnosis in some cases. To avoid unnecessary surgeries, there is an increasing interest in using radiomics tools to predict histological results, using radiological features. We performed a narrative review to evaluate the use of radiomics in renal mass characterization. Conventional images, such as computed tomography (CT) and magnetic resonance (MR), are the most common diagnostic tools in renal mass characterization. Distinguishing between benign and malignant tumors in small renal masses can be challenging using conventional methods. To improve subjective evaluation, the interest in using radiomics to obtain quantitative parameters from medical images has increased. Several studies have assessed this novel tool for renal mass characterization, comparing its ability to distinguish benign to malign tumors, the results in differentiating renal cell carcinoma subtypes, or the correlation with prognostic features, with other methods. In several studies, radiomic tools have shown a good accuracy in characterizing renal mass lesions. However, due to the heterogeneity in the radiomic model building, prospective and external validated studies are needed.
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Affiliation(s)
- Beatriz Gutiérrez Hidalgo
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Juan Gómez Rivas
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Irene de la Parra
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - María Jesús Marugán
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Álvaro Serrano
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Juan Fco Hermida Gutiérrez
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Jerónimo Barrera
- Radiodiagnosis Department, Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain
| | - Jesús Moreno-Sierra
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
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Di Dio M, Barbuto S, Bisegna C, Bellin A, Boccia M, Amparore D, Verri P, Busacca G, Sica M, De Cillis S, Piramide F, Zaccone V, Piana A, Alba S, Volpi G, Fiori C, Porpiglia F, Checcucci E. Artificial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3D ®) Models in Surgical Planning of Challenging Robotic Nephron-Sparing Surgery: A Case Report and Snapshot of the State-of-the-Art with Possible Future Implications. Diagnostics (Basel) 2023; 13:2320. [PMID: 37510065 PMCID: PMC10377834 DOI: 10.3390/diagnostics13142320] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/02/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Recently, 3D models (3DM) gained popularity in urology, especially in nephron-sparing interventions (NSI). Up to now, the application of artificial intelligence (AI) techniques alone does not allow us to obtain a 3DM adequate to plan a robot-assisted partial nephrectomy (RAPN). Integration of AI with computer vision algorithms seems promising as it allows to speed up the process. Herein, we present a 3DM realized with the integration of AI and a computer vision approach (CVA), displaying the utility of AI-based Hyper Accuracy Three-dimensional (HA3D®) models in preoperative planning and intraoperative decision-making process of challenging robotic NSI. A 54-year-old Caucasian female with no past medical history was referred to the urologist for incidental detection of the right renal mass. Preoperative contrast-enhanced abdominal CT confirmed a 35 × 25 mm lesion on the anterior surface of the upper pole (PADUA 7), with no signs of distant metastasis. CT images in DICOM format were processed to obtain a HA3D® model. RAPN was performed using Da Vinci Xi surgical system in a three-arm configuration. The enucleation strategy was achieved after selective clamping of the tumor-feeding artery. Overall operative time was 85 min (14 min of warm ischemia time). No intra-, peri- and post-operative complications were recorded. Histopathological examination revealed a ccRCC (stage pT1aNxMx). AI is breaking new ground in medical image analysis panorama, with enormous potential in organ/tissue classification and segmentation, thus obtaining 3DM automatically and repetitively. Realized with the integration of AI and CVA, the results of our 3DM were accurate as demonstrated during NSI, proving the potentialities of this approach for HA3D® models' reconstruction.
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Affiliation(s)
- Michele Di Dio
- Division of Urology, Department of Surgery, SS Annunziata Hospital, 87100 Cosenza, Italy
| | - Simona Barbuto
- Department of Oncology, Division of Urology, San Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy
| | - Claudio Bisegna
- Division of Urology, Department of Surgery, SS Annunziata Hospital, 87100 Cosenza, Italy
| | - Andrea Bellin
- Department of Oncology, Division of Urology, San Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy
| | - Mario Boccia
- Department of Oncology, Division of Urology, San Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy
| | - Daniele Amparore
- Department of Oncology, Division of Urology, San Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy
| | - Paolo Verri
- Department of Oncology, Division of Urology, San Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy
| | - Giovanni Busacca
- Department of Oncology, Division of Urology, San Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy
| | - Michele Sica
- Department of Oncology, Division of Urology, San Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy
| | - Sabrina De Cillis
- Department of Oncology, Division of Urology, San Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy
| | - Federico Piramide
- Department of Oncology, Division of Urology, San Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy
| | - Vincenzo Zaccone
- Division of Urology, Department of Surgery, SS Annunziata Hospital, 87100 Cosenza, Italy
| | - Alberto Piana
- Department of Oncology, Division of Urology, San Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy
- Romolo Hospital, 88821 Rocca di Neto, Italy
| | | | - Gabriele Volpi
- Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy
| | - Cristian Fiori
- Department of Oncology, Division of Urology, San Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy
| | - Francesco Porpiglia
- Department of Oncology, Division of Urology, San Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy
| | - Enrico Checcucci
- Department of Oncology, Division of Urology, San Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy
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8
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Ferro M, Crocetto F, Barone B, del Giudice F, Maggi M, Lucarelli G, Busetto GM, Autorino R, Marchioni M, Cantiello F, Crocerossa F, Luzzago S, Piccinelli M, Mistretta FA, Tozzi M, Schips L, Falagario UG, Veccia A, Vartolomei MD, Musi G, de Cobelli O, Montanari E, Tătaru OS. Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review. Ther Adv Urol 2023; 15:17562872231164803. [PMID: 37113657 PMCID: PMC10126666 DOI: 10.1177/17562872231164803] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/04/2023] [Indexed: 04/29/2023] Open
Abstract
Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.
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Affiliation(s)
| | - Felice Crocetto
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Biagio Barone
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Francesco del Giudice
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation
Unit, Department of Emergency and Organ Transplantation, University of Bari,
Bari, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ
Transplantation, University of Foggia, Foggia, Italy
| | | | - Michele Marchioni
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti,
Italy
| | - Francesco Cantiello
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Fabio Crocerossa
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Stefano Luzzago
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Mattia Piccinelli
- Cancer Prognostics and Health Outcomes Unit,
Division of Urology, University of Montréal Health Center, Montréal, QC,
Canada
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Tozzi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Luigi Schips
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
| | | | - Alessandro Veccia
- Urology Unit, Azienda Ospedaliera
Universitaria Integrata Verona, University of Verona, Verona, 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
| | - Gennaro Musi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Ottavio de Cobelli
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca’
Granda – Ospedale Maggiore Policlinico, Department of Clinical Sciences and
Community Health, University of Milan, Milan, Italy
| | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral
Studies (IOSUD), George Emil Palade University of Medicine, Pharmacy,
Science and Technology of Târgu Mures, Târgu Mures, Romania
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9
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Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions. LIFE (BASEL, SWITZERLAND) 2022; 12:life12121991. [PMID: 36556356 PMCID: PMC9786074 DOI: 10.3390/life12121991] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/18/2022] [Accepted: 11/25/2022] [Indexed: 11/30/2022]
Abstract
The World Health Organization (WHO), in their 2022 report, identified cancer as one of the leading causes of death, accounting for about 16% of deaths worldwide. The Cancer-Moonshot community aims to reduce the cancer death rate by half in the next 25 years and wants to improve the lives of cancer-affected people. Cancer mortality can be reduced if detected early and treated appropriately. Cancers like breast cancer and cervical cancer have high cure probabilities when treated early in accordance with best practices. Integration of artificial intelligence (AI) into cancer research is currently addressing many of the challenges where medical experts fail to bring cancer to control and cure, and the outcomes are quite encouraging. AI offers many tools and platforms to facilitate more understanding and tackling of this life-threatening disease. AI-based systems can help pathologists in diagnosing cancer more accurately and consistently, reducing the case error rates. Predictive-AI models can estimate the likelihood for a person to get cancer by identifying the risk factors. Big data, together with AI, can enable medical experts to develop customized treatments for cancer patients. The side effects from this kind of customized therapy will be less severe in comparison with the generalized therapies. However, many of these AI tools will remain ineffective in fighting against cancer and saving the lives of millions of patients unless they are accessible and understandable to biologists, oncologists, and other medical cancer researchers. This paper presents the trends, challenges, and future directions of AI in cancer research. We hope that this paper will be of help to both medical experts and technical experts in getting a better understanding of the challenges and research opportunities in cancer diagnosis and treatment.
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Ong W, Zhu L, Zhang W, Kuah T, Lim DSW, Low XZ, Thian YL, Teo EC, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A, Hallinan JTPD. Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis. Cancers (Basel) 2022; 14:4025. [PMID: 36011018 PMCID: PMC9406500 DOI: 10.3390/cancers14164025] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/10/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022] Open
Abstract
Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Lei Zhu
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Wenqiao Zhang
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Tricia Kuah
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Desmond Shi Wei Lim
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Yee Liang Thian
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Balamurugan A. Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore 119074, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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