1
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Wang Y, Butaney M, Wilder S, Ghani K, Rogers CG, Lane BR. The evolving management of small renal masses. Nat Rev Urol 2024; 21:406-421. [PMID: 38365895 DOI: 10.1038/s41585-023-00848-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/19/2023] [Indexed: 02/18/2024]
Abstract
Small renal masses (SRMs) are a heterogeneous group of tumours with varying metastatic potential. The increasing use and improving quality of abdominal imaging have led to increasingly early diagnosis of incidental SRMs that are asymptomatic and organ confined. Despite improvements in imaging and the growing use of renal mass biopsy, diagnosis of malignancy before treatment remains challenging. Management of SRMs has shifted away from radical nephrectomy, with active surveillance and nephron-sparing surgery taking over as the primary modalities of treatment. The optimal treatment strategy for SRMs continues to evolve as factors affecting short-term and long-term outcomes in this patient cohort are elucidated through studies from prospective data registries. Evidence from rapidly evolving research in biomarkers, imaging modalities, and machine learning shows promise in improving understanding of the biology and management of this patient cohort.
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Affiliation(s)
- Yuzhi Wang
- Vattikuti Urology Institute, Henry Ford Health System, Detroit, MI, USA
| | - Mohit Butaney
- Vattikuti Urology Institute, Henry Ford Health System, Detroit, MI, USA
| | - Samantha Wilder
- Vattikuti Urology Institute, Henry Ford Health System, Detroit, MI, USA
| | - Khurshid Ghani
- Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Craig G Rogers
- Vattikuti Urology Institute, Henry Ford Health System, Detroit, MI, USA
| | - Brian R Lane
- Division of Urology, Corewell Health West, Grand Rapids, MI, USA.
- Department of Surgery, Michigan State University College of Human Medicine, Grand Rapids, MI, USA.
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2
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Ludwig DR, Thacker Y, Luo C, Narra A, Mintz AJ, Siegel CL. CT-derived textural analysis parameters discriminate high-attenuation renal cysts from solid renal neoplasms. Clin Radiol 2023; 78:e782-e790. [PMID: 37586966 DOI: 10.1016/j.crad.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/15/2023] [Accepted: 07/05/2023] [Indexed: 08/18/2023]
Abstract
AIM To assess the utility of textural features on computed tomography (CT) to differentiate high-attenuation cysts from solid renal neoplasms among indeterminate renal lesions detected incidentally on CT. MATERIALS AND METHODS Patients were included if they had an indeterminate renal lesion on CT that was subsequently characterised on ultrasound or magnetic resonance imaging (MRI). Up to three lesions per patient were included if they had a size ≥10 mm and density of 20-70 HU on unenhanced CT or any single phase of contrast-enhanced CT. Cases were categorised as benign or most likely benign cysts (Bosniak II and IIF) versus indeterminate (Bosniak III), mixed solid and cystic (Bosniak IV), or solid renal lesions. A random forest model was generated using 95 textural parameters and four clinical parameters for each lesion. RESULTS Two hundred and thirty-four patients were included who had a total of 278 lesions. Of these, 193 (69%) were benign or most likely benign cysts and 85 (31%) were indeterminate, mixed cystic and solid, or solid renal lesions. The random forest model had an area under the curve of 0.71 (95% confidence interval [CI]: 0.65, 0.78), with a sensitivity and specificity of 81.2% and 38.9%, respectively. CONCLUSION A multivariate model including textural and clinical parameters had moderate overall performance for discriminating benign or likely benign cysts from indeterminate, mixed solid and cystic, or solid renal lesions. This study serves as a proof of concept and may reduce the need for further follow-up by characterising a significant portion of indeterminate lesions on CT as benign.
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Affiliation(s)
- D R Ludwig
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.
| | - Y Thacker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - C Luo
- Division of Public Health Sciences, Washington University School of Medicine, Saint Louis, MO, USA
| | - A Narra
- St George's University School of Medicine, Grenada, West Indies
| | - A J Mintz
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - C L Siegel
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
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3
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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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4
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Zhou Z, Qian X, Hu J, Geng C, Zhang Y, Dou X, Che T, Zhu J, Dai Y. Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma. Front Oncol 2023; 13:1167328. [PMID: 37692840 PMCID: PMC10485140 DOI: 10.3389/fonc.2023.1167328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 07/24/2023] [Indexed: 09/12/2023] Open
Abstract
Objective This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC). Methods A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC). Results The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039). Conclusion The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.
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Affiliation(s)
- Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Xusheng Qian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Jisu Hu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Chen Geng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Yongsheng Zhang
- Department of Pathology, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Xin Dou
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Tuanjie Che
- Key Laboratory of Functional Genomic and Molecular Diagnosis of Gansu Province, Lanzhou, Gansu, China
- Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Jianbing Zhu
- Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
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5
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Mahmud S, Abbas TO, Mushtak A, Prithula J, Chowdhury MEH. Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata. Cancers (Basel) 2023; 15:3189. [PMID: 37370799 DOI: 10.3390/cancers15123189] [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: 05/01/2023] [Revised: 05/30/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
Kidney cancers are one of the most common malignancies worldwide. Accurate diagnosis is a critical step in the management of kidney cancer patients and is influenced by multiple factors including tumor size or volume, cancer types and stages, etc. For malignant tumors, partial or radical surgery of the kidney might be required, but for clinicians, the basis for making this decision is often unclear. Partial nephrectomy could result in patient death due to cancer if kidney removal was necessary, whereas radical nephrectomy in less severe cases could resign patients to lifelong dialysis or need for future transplantation without sufficient cause. Using machine learning to consider clinical data alongside computed tomography images could potentially help resolve some of these surgical ambiguities, by enabling a more robust classification of kidney cancers and selection of optimal surgical approaches. In this study, we used the publicly available KiTS dataset of contrast-enhanced CT images and corresponding patient metadata to differentiate four major classes of kidney cancer: clear cell (ccRCC), chromophobe (chRCC), papillary (pRCC) renal cell carcinoma, and oncocytoma (ONC). We rationalized these data to overcome the high field of view (FoV), extract tumor regions of interest (ROIs), classify patients using deep machine-learning models, and extract/post-process CT image features for combination with clinical data. Regardless of marked data imbalance, our combined approach achieved a high level of performance (85.66% accuracy, 84.18% precision, 85.66% recall, and 84.92% F1-score). When selecting surgical procedures for malignant tumors (RCC), our method proved even more reliable (90.63% accuracy, 90.83% precision, 90.61% recall, and 90.50% F1-score). Using feature ranking, we confirmed that tumor volume and cancer stage are the most relevant clinical features for predicting surgical procedures. Once fully mature, the approach we propose could be used to assist surgeons in performing nephrectomies by guiding the choices of optimal procedures in individual patients with kidney cancer.
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Affiliation(s)
- Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Tariq O Abbas
- Urology Division, Surgery Department, Sidra Medicine, Doha 26999, Qatar
- Department of Surgery, Weill Cornell Medicine-Qatar, Doha 24811, Qatar
- College of Medicine, Qatar University, Doha 2713, Qatar
| | - Adam Mushtak
- Clinical Imaging Department, Hamad Medical Corporation, Doha 3050, Qatar
| | - Johayra Prithula
- Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh
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Shehata M, Abouelkheir RT, Gayhart M, Van Bogaert E, Abou El-Ghar M, Dwyer AC, Ouseph R, Yousaf J, Ghazal M, Contractor S, El-Baz A. Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review. Cancers (Basel) 2023; 15:2835. [PMID: 37345172 DOI: 10.3390/cancers15102835] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.
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Affiliation(s)
- Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Rasha T Abouelkheir
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | | | - Eric Van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Mohamed Abou El-Ghar
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Amy C Dwyer
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA
| | - Rosemary Ouseph
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
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7
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Chung A, Raman SS. Radiologist's Disease: Imaging for Renal Cancer. Urol Clin North Am 2023; 50:161-180. [PMID: 36948664 DOI: 10.1016/j.ucl.2023.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2023]
Abstract
There is a clear benefit of imaging-based differentiation of small indeterminate masses to its subtypes of clear cell renal cell carcinoma (RCC), chromophobe RCC, papillary RCC, fat poor angiomyolipoma and oncocytoma because it helps determine the next step options for the patients. The work thus far in radiology has explored different parameters in computed tomography, MRI, and contrast-enhanced ultrasound with the discovery of many reliable imaging features that suggest certain tissue subtypes. Likert score-based risk stratification systems can help determine management, and new techniques such as perfusion, radiogenomics, single-photon emission tomography, and artificial intelligence can add to the imaging-based evaluation of indeterminate renal masses.
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Affiliation(s)
- Alex Chung
- Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Steven S Raman
- David Geffen School of Medicine at UCLA, 757 Westwood Bl, RRMC, Los Angeles, CA, USA.
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8
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Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects. Int J Mol Sci 2023; 24:ijms24054615. [PMID: 36902045 PMCID: PMC10003020 DOI: 10.3390/ijms24054615] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.
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9
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Ohe C, Yoshida T, Amin MB, Uno R, Atsumi N, Yasukochi Y, Ikeda J, Nakamoto T, Noda Y, Kinoshita H, Tsuta K, Higasa K. Deep learning-based predictions of clear and eosinophilic phenotypes in clear cell renal cell carcinoma. Hum Pathol 2023; 131:68-78. [PMID: 36372298 DOI: 10.1016/j.humpath.2022.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022]
Abstract
We have recently shown that histological phenotypes focusing on clear and eosinophilic cytoplasm in clear cell renal cell carcinoma (ccRCC) correlated with prognosis and the response to angiogenesis inhibition and checkpoint blockade. This study aims to objectively show the diagnostic utility of clear or eosinophilic phenotypes of ccRCC by developing an artificial intelligence (AI) model using the TCGA-ccRCC dataset and to demonstrate if the clear or eosinophilic predicted phenotypes correlate with pathological factors and gene signatures associated with angiogenesis and cancer immunity. Before the development of the AI model, histological evaluation using hematoxylin and eosin whole-slide images of the TCGA-ccRCC cohort (n = 435) was performed by a urologic pathologist. The AI model was developed as follows. First, the highest-grade area on each whole slide image was captured for image processing. Second, the selected regions were cropped into tiles. Third, the AI model was trained using transfer learning on a deep convolutional neural network, and clear or eosinophilic predictions were scaled as AI scores. Next, we verified the AI model using a validation cohort (n = 95). Finally, we evaluated the accuracy of the prognostic predictions of the AI model and revealed that the AI model detected clear and eosinophilic phenotypes with high accuracy. The AI model stratified the patients' outcomes, and the predicted eosinophilic phenotypes correlated with adverse clinicopathological characteristics and high immune-related gene signatures. In conclusion, the AI-based histologic subclassification accurately predicted clear or eosinophilic phenotypes of ccRCC, allowing for consistently reproducible stratification for prognostic and therapeutic stratification.
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Affiliation(s)
- Chisato Ohe
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan.
| | - Takashi Yoshida
- Department of Urology and Andrology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Mahul B Amin
- Department of Pathology and Laboratory Medicine, University of Tennessee Health Sciences Center, 930 Madison Avenue, Memphis, TN 38163, USA; Department of Urology, University of Southern California, 1441 Eastlake Avenue, Los Angeles, CA 90033, USA
| | - Rena Uno
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan; Department of Pathology, Hyogo Cancer Center, Akashi, Hyogo 673-8558, Japan
| | - Naho Atsumi
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Yoshiki Yasukochi
- Department of Genome Analysis, Institute of Biomedical Science, Kansai Medical University, Hirakata, Osaka 573-1191, Japan
| | - Junichi Ikeda
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan; Department of Urology and Andrology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Takahiro Nakamoto
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan; Department of Urology and Andrology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Yuri Noda
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Hidefumi Kinoshita
- Department of Urology and Andrology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Koji Tsuta
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Koichiro Higasa
- Department of Genome Analysis, Institute of Biomedical Science, Kansai Medical University, Hirakata, Osaka 573-1191, Japan
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10
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Yang M, He X, Xu L, Liu M, Deng J, Cheng X, Wei Y, Li Q, Wan S, Zhang F, Wu L, Wang X, Song B, Liu M. CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma. Front Oncol 2022; 12:961779. [PMID: 36249050 PMCID: PMC9555088 DOI: 10.3389/fonc.2022.961779] [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: 06/05/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background Clear cell Renal Cell Carcinoma (ccRCC) is the most common malignant tumor in the urinary system and the predominant subtype of malignant renal tumors with high mortality. Biopsy is the main examination to determine ccRCC grade, but it can lead to unavoidable complications and sampling bias. Therefore, non-invasive technology (e.g., CT examination) for ccRCC grading is attracting more and more attention. However, noise labels on CT images containing multiple grades but only one label make prediction difficult. However, noise labels exist in CT images, which contain multiple grades but only one label, making prediction difficult. Aim We proposed a Transformer-based deep learning algorithm with CT images to improve the diagnostic accuracy of grading prediction and to improve the diagnostic accuracy of ccRCC grading. Methods We integrate different training models to improve robustness and predict Fuhrman nuclear grade. Then, we conducted experiments on a collected ccRCC dataset containing 759 patients and used average classification accuracy, sensitivity, specificity, and AreaUnderCurve as indicators to evaluate the quality of research. In the comparative experiments, we further performed various current deep learning algorithms to show the advantages of the proposed method. We collected patients with pathologically proven ccRCC diagnosed from April 2010 to December 2018 as the training and internal test dataset, containing 759 patients. We propose a transformer-based network architecture that efficiently employs convolutional neural networks (CNNs) and self-attention mechanisms to extract a persuasive feature automatically. And then, a nonlinear classifier is applied to classify. We integrate different training models to improve the accuracy and robustness of the model. The average classification accuracy, sensitivity, specificity, and area under curve are used as indicators to evaluate the quality of a model. Results The mean accuracy, sensitivity, specificity, and Area Under Curve achieved by CNN were 82.3%, 89.4%, 83.2%, and 85.7%, respectively. In contrast, the proposed Transformer-based model obtains a mean accuracy of 87.1% with a sensitivity of 91.3%, a specificity of 85.3%, and an Area Under Curve (AUC) of 90.3%. The integrated model acquires a better performance (86.5% ACC and an AUC of 91.2%). Conclusion A transformer-based network performs better than traditional deep learning algorithms in terms of the accuracy of ccRCC prediction. Meanwhile, the transformer has a certain advantage in dealing with noise labels existing in CT images of ccRCC. This method is promising to be applied to other medical tasks (e.g., the grade of neurogliomas and meningiomas).
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Affiliation(s)
- Meiyi Yang
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaopeng He
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Lifeng Xu
- Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, China
| | - Minghui Liu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiali Deng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xuan Cheng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Qian Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Shang Wan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Feng Zhang
- Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, China
| | - Lei Wu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaomin Wang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Ming Liu, ; Bin Song,
| | - Ming Liu
- Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, China
- *Correspondence: Ming Liu, ; Bin Song,
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Alnazer I, Falou O, Bourdon P, Urruty T, Guillevin R, Khalil M, Shahin A, Fernandez-Maloigne C. Usefulness of computed tomography textural analysis in renal cell carcinoma nuclear grading. J Med Imaging (Bellingham) 2022; 9:054501. [PMID: 36120414 PMCID: PMC9467905 DOI: 10.1117/1.jmi.9.5.054501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 08/24/2022] [Indexed: 09/15/2023] Open
Abstract
Purpose: To evaluate the usefulness of computed tomography (CT) texture descriptors integrated with machine-learning (ML) models in the identification of clear cell renal cell carcinoma (ccRCC) and for the first time papillary renal cell carcinoma (pRCC) tumor nuclear grades [World Health Organization (WHO)/International Society of Urologic Pathologists (ISUP) 1, 2, 3, and 4]. Approach: A total of 143 ccRCC and 21 pRCC patients were analyzed in this study. Texture features were extracted from late arterial phase CT images. A complete separation of training/validation and testing subsets from the beginning to the end of the pipeline was adopted. Feature dimension was reduced by collinearity analysis and Gini impurity-based feature selection. The synthetic minority over-sampling technique was employed for imbalanced datasets. The ML classifiers were logistic regression, SVM, RF, multi-layer perceptron, and K -NN. The differentiation between low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and between all grades was assessed for ccRCC and pRCC datasets. The classification performance was assessed and compared by certain metrics. Results: Textures-based classifiers were able to efficiently identify ccRCC and pRCC grades. An accuracy and area under the characteristic operating curve (AUC) up to 91%/0.9, 91%/0.9, 90%/0.9, and 88%/1 were reached when discriminating ccRCC low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and all grades, respectively. An accuracy and AUC up to 96%/1, 81%/0.8, 86%/0.9, and 88%/0.9 were found when differentiating pRCC low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and all grades, respectively. Conclusion: CT texture-based ML models can be used to assist radiologist in predicting the WHO/ISUP grade of ccRCC and pRCC pre-operatively.
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Affiliation(s)
- Israa Alnazer
- Université de Poitiers, XLIM-ICONES, UMR CNRS 7252, Poitiers, France
- Laboratoire commun CNRS/SIEMENS I3M, Poitiers, France
- Lebanese University, AZM Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon
| | - Omar Falou
- Lebanese University, AZM Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon
- American University of Culture and Education, Koura, Lebanon
- Lebanese University, Faculty of Science, Tripoli, Lebanon
- Centre Hospitalier Universitaire de Poitiers, Poitiers, France
| | - Pascal Bourdon
- Université de Poitiers, XLIM-ICONES, UMR CNRS 7252, Poitiers, France
- Laboratoire commun CNRS/SIEMENS I3M, Poitiers, France
| | - Thierry Urruty
- Université de Poitiers, XLIM-ICONES, UMR CNRS 7252, Poitiers, France
- Laboratoire commun CNRS/SIEMENS I3M, Poitiers, France
| | - Rémy Guillevin
- Laboratoire commun CNRS/SIEMENS I3M, Poitiers, France
- Centre Hospitalier Universitaire de Poitiers, Poitiers, France
| | - Mohamad Khalil
- Lebanese University, AZM Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon
| | - Ahmad Shahin
- Lebanese University, AZM Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon
| | - Christine Fernandez-Maloigne
- Université de Poitiers, XLIM-ICONES, UMR CNRS 7252, Poitiers, France
- Laboratoire commun CNRS/SIEMENS I3M, Poitiers, France
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12
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Kong J, He Y, Zhu X, Shao P, Xu Y, Chen Y, Coatrieux JL, Yang G. BKC-Net: Bi-Knowledge Contrastive Learning for renal tumor diagnosis on 3D CT images. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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13
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Chen S, Guo T, Zhang E, Wang T, Jiang G, Wu Y, Wang X, Na R, Zhang N. Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma. Heliyon 2022; 8:e10578. [PMID: 36158103 PMCID: PMC9489730 DOI: 10.1016/j.heliyon.2022.e10578] [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: 06/14/2022] [Revised: 08/14/2022] [Accepted: 09/05/2022] [Indexed: 11/03/2022] Open
Abstract
The sole clinicopathological characteristic is not enough for the prediction of survival of patients with clear cell renal cell carcinoma (ccRCC). However, the survival prediction model constructed by machine learning technology for patients with ccRCC using clinicopathological features is rarely reported yet. In this study, a total of 5878 patients diagnosed as ccRCC from four independent patient cohorts were recruited. The least absolute shrinkage and selection operator analysis was implemented to identify optimal clinicopathological characteristics and calculate each coefficient to construct the prognosis model. In addition, weighted gene co-expression network and gene enrichment analysis associated with risk score were also carried out. Three clinicopathologic features were selected for the construction of the prognosis risk score model as the prognostic factors of ccRCC, including tumor size, tumor grade, and tumor stage. In the CPTAC (Clinical Proteomic Tumor Analysis Consortium) cohort, the General cohort, the SEER (Surveillance, Epidemiology, and End Results) cohort, and the Huashan cohort, patients with high-risk score had worse clinical outcomes than patients with low-risk score (hazard ratio 5.15, 4.64, 3.96, and 5.15, respectively). Further functional enrichment analysis demonstrated that our machine learning-based risk score was significantly connected with some cell proliferation-related pathways, consisting of DNA repair, cell division, and cell cycle. In summary, we developed and validated a machine learning-based prognosis prediction model, which might contribute to clinical decision-making for patients with ccRCC.
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Affiliation(s)
- Siteng Chen
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tuanjie Guo
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Encheng Zhang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Wang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guangliang Jiang
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yishuo Wu
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiang Wang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rong Na
- Department of Surgery, Queen Mary Hospital, The University of Hong Kong, Hong Kong SAR, China
| | - Ning Zhang
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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14
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Prediction of Gestational Diabetes Mellitus under Cascade and Ensemble Learning Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3212738. [PMID: 35875747 PMCID: PMC9303101 DOI: 10.1155/2022/3212738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/18/2022] [Accepted: 05/30/2022] [Indexed: 11/17/2022]
Abstract
Gestational diabetes mellitus (GDM) is one of the risk factors for fetal dysplasia and maternal pregnancy difficulties. Therefore, the prediction of the risk of GDM in advance has become a big demand for millions of families. Therefore, machine learning technology is adopted to study GDM prediction. Firstly, the data is preprocessed, and the mean value is used for outlier processing. After preprocessing of the data, the IV value method is used to screen the features. Of the 83 features in the original sample data, 40 important features are screened out through feature engineering. On this basis, Logistics regression model, Lasso-Logistics, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (Xgboost), Light Gradient Boosting Machine (Lightgbm), and Gradient Boosting Categorical Features (Catboost) are established, and multiple learners are integrated. Finally, the constructed model is tested on data sets. The accuracy of the proposed model is 80.3%, the accuracy is 74.6%, the recall rate is 79.3%, and the running time is only 2.53 seconds. This means that the proposed model is superior to the previous models in terms of accuracy, precision, recall rate, and F1 value, and the time consumption is also in line with the actual engineering requirements. The proposed scheme provides some ideas for the research of machine learning technology in disease prediction.
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15
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Artificial intelligence for renal cancer: From imaging to histology and beyond. Asian J Urol 2022; 9:243-252. [PMID: 36035341 PMCID: PMC9399557 DOI: 10.1016/j.ajur.2022.05.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/07/2022] [Accepted: 05/07/2022] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%–17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.
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16
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Rasmussen R, Sanford T, Parwani AV, Pedrosa I. Artificial Intelligence in Kidney Cancer. Am Soc Clin Oncol Educ Book 2022; 42:1-11. [PMID: 35580292 DOI: 10.1200/edbk_350862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Artificial intelligence is rapidly expanding into nearly all facets of life, particularly within the field of medicine. The diagnosis, characterization, management, and treatment of kidney cancer is ripe with areas for improvement that may be met with the promises of artificial intelligence. Here, we explore the impact of current research work in artificial intelligence for clinicians caring for patients with renal cancer, with a focus on the perspectives of radiologists, pathologists, and urologists. Promising preliminary results indicate that artificial intelligence may assist in the diagnosis and risk stratification of newly discovered renal masses and help guide the clinical treatment of patients with kidney cancer. However, much of the work in this field is still in its early stages, limited in its broader applicability, and hampered by small datasets, the varied appearance and presentation of kidney cancers, and the intrinsic limitations of the rigidly structured tasks artificial intelligence algorithms are trained to complete. Nonetheless, the continued exploration of artificial intelligence holds promise toward improving the clinical care of patients with kidney cancer.
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Affiliation(s)
- Robert Rasmussen
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Thomas Sanford
- Department of Urology, Upstate Medical University, Syracuse, NY
| | - Anil V Parwani
- Department of Pathology, The Ohio State University, Columbus, OH
| | - Ivan Pedrosa
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX.,Department of Urology, The University of Texas Southwestern Medical Center, Dallas, TX.,Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, TX
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17
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Improving segmentation and classification of renal tumors in small sample 3D CT images using transfer learning with convolutional neural networks. Int J Comput Assist Radiol Surg 2022; 17:1303-1311. [PMID: 35290645 DOI: 10.1007/s11548-022-02587-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 02/24/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE Computed tomography (CT) images can display internal organs of patients and are particularly suitable for preoperative surgical diagnoses. The increasing demands for computer-aided systems in recent years have facilitated the development of many automated algorithms, especially deep convolutional neural networks, to segment organs and tumors or identify diseases from CT images. However, performances of some systems are highly affected by the amount of training data, while the sizes of medical image data sets, especially three-dimensional (3D) data sets, are usually small. This condition limits the application of deep learning. METHODS In this study, given a practical clinical data set that has 3D CT images of 20 patients with renal carcinoma, we designed a pipeline employing transfer learning to alleviate the detrimental effect of the small sample size. A dual-channel fine segmentation network (FS-Net) was constructed to segment kidney and tumor regions, with 210 publicly available 3D images from a competition employed during the training phase. We also built discriminative classifiers to classify the benign and malignant tumors based on the segmented regions, where both handcrafted and deep features were tested. RESULTS Our experimental results showed that the Dice values of segmented kidney and tumor regions were 0.9662 and 0.7685, respectively, which were better than those of state-of-the-art methods. The classification model using radiomics features can classify most of the tumors correctly. CONCLUSIONS The designed FS-Net was demonstrated to be more effective than simply fine-tuning on the practical small size data set given that the model can borrow knowledge from large auxiliary data without diluting the signal in primary data. For the small data set, radiomics features outperformed deep features in the classification of benign and malignant tumors. This work highlights the importance of architecture design in transfer learning, and the proposed pipeline is anticipated to provide a reference and inspiration for small data analysis.
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18
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Gong X, Zheng B, Xu G, Chen H, Chen C. Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer. J Thorac Dis 2022; 13:6240-6251. [PMID: 34992804 PMCID: PMC8662490 DOI: 10.21037/jtd-21-1107] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 09/24/2021] [Indexed: 01/15/2023]
Abstract
Background Accurate prognostic estimation for esophageal cancer (EC) patients plays an important role in the process of clinical decision-making. The objective of this study was to develop an effective model to predict the 5-year survival status of EC patients using machine learning (ML) algorithms. Methods We retrieved the information of patients diagnosed with EC between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) Program, including 24 features. A total of 8 ML models were applied to the selected dataset to classify the EC patients in terms of 5-year survival status, including 3 newly developed gradient boosting models (GBM), XGBoost, CatBoost, and LightGBM, 2 commonly used tree-based models, gradient boosting decision trees (GBDT) and random forest (RF), and 3 other ML models, artificial neural networks (ANN), naive Bayes (NB), and support vector machines (SVM). A 5-fold cross-validation was used in model performance measurement. Results After excluding records with missing data, the final study population comprised 10,588 patients. Feature selection was conducted based on the χ2 test, however, the experiment results showed that the complete dataset provided better prediction of outcomes than the dataset with removal of non-significant features. Among the 8 models, XGBoost had the best performance [area under the receiver operating characteristic (ROC) curve (AUC): 0.852 for XGBoost, 0.849 for CatBoost, 0.850 for LightGBM, 0.846 for GBDT, 0.838 for RF, 0.844 for ANN, 0.833 for NB, and 0.789 for SVM]. The accuracy and logistic loss of XGBoost were 0.875 and 0.301, respectively, which were also the best performances. In the XGBoost model, the SHapley Additive exPlanations (SHAP) value was calculated and the result indicated that the four features: reason no cancer-directed surgery, Surg Prim Site, age, and stage group had the greatest impact on predicting the outcomes. Conclusions The XGBoost model and the complete dataset can be used to construct an accurate prognostic model for patients diagnosed with EC which may be applicable in clinical practice in the future.
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Affiliation(s)
- Xian Gong
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, China
| | - Bin Zheng
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, China
| | - Guobing Xu
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, China
| | - Hao Chen
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, China
| | - Chun Chen
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, China
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19
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Yu Q, Liu J, Lin H, Lei P, Fan B. Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith. Int J Clin Pract 2022; 2022:5478908. [PMID: 36474549 PMCID: PMC9678460 DOI: 10.1155/2022/5478908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/24/2022] [Accepted: 11/05/2022] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE To investigate the clinical application of the three-dimensional (3D) radiomics model of the CT image in the diagnosis and identification of ureteral calculus and phlebolith. METHOD Sixty-one cases of ureteral calculus and 61 cases of phlebolith were retrospectively investigated. The enrolled patients were randomly categorized into the training set (n = 86) and the testing set (n = 36) with a ratio of 7 : 3. The plain CT scan images of all samples were manually segmented by the ITK-SNAP software, followed by radiomics analysis through the Analysis Kit software. A total of 1316 texture features were extracted. Then, the maximum correlation minimum redundancy criterion and the least absolute shrinkage and selection operator algorithm were used for texture feature selection. The feature subset with the most predictability was selected to establish the 3D radiomics model. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) was also calculated. Additionally, the decision curve was used to evaluate the clinical application of the model. RESULTS The 10 selected radiomics features were significantly related to the identification and diagnosis of ureteral calculus and phlebolith. The radiomics model showed good identification efficiency for ureteral calculus and phlebolith in the training set (AUC = 0.98; 95%CI: 0.96-1.00) and testing set (AUC = 0.98; 95%CI: 0.95-1.00). The decision curve thus demonstrated the clinical application of the radiomics model. CONCLUSIONS The 3D radiomics model based on plain CT scan images indicated good performance in the identification and prediction of ureteral calculus and phlebolith and was expected to provide an effective detection method for clinical diagnosis.
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Affiliation(s)
- Qiuyue Yu
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Jiaqi Liu
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Huashan Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha 410005, China
| | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang 550000, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
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20
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Yu W, Liang G, Zeng L, Yang Y, Wu Y. Accuracy of CT texture analysis for differentiating low-grade and high-grade renal cell carcinoma: systematic review and meta-analysis. BMJ Open 2021; 11:e051470. [PMID: 34937716 PMCID: PMC8704996 DOI: 10.1136/bmjopen-2021-051470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVES This study aimed to assess the accuracy of CT texture analysis (CTTA) for differentiating low-grade and high-grade renal cell carcinoma (RCC). DESIGN Systematic review and meta-analysis. DATA SOURCES PubMed, Cochrane Library, Embase, Web of Science, OVID Medline, Science Direct and Springer were searched to identify the included studies. ELIGIBILITY CRITERIA FOR INCLUDING STUDIES Clinical studies that report about the accuracy of CTTA in differentiating low-grade and high-grade RCC. METHODS Multiple databases were searched to identify studies from their inception to 20 October 2021. Two radiologists independently extracted data from the primary studies. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic OR (DOR) were calculated to assess CTTA performance. The summary receiver operating characteristic (SROC) curve was plotted, and the area under the curve (AUC) was calculated to evaluate the accuracy of CTTA in grading RCC. RESULTS This meta-analysis included 11 studies, with 1603 lesions observed in 1601 patients. Values of the pooled sensitivity, specificity, PLR, NLR, DOR were 0.79 (95% CI 0.73 to 0.84), 0.84 (95% CI 0.81 to 0.87), 5.1 (95% CI 4.0 to 6.4), 0.24 (95% CI 0.19 to 0.32) and 21 (95% CI 13 to 33), respectively. The SROC curve showed that the AUC was 0.88 (95% CI 0.84 to 0.90). Deeks' test found no significant publication bias among the studies (p=0.42). CONCLUSIONS The findings of this meta-analysis suggest that CTTA has a high accuracy in differentiating low-grade and high-grade RCC. A standardised methodology and large sample-based study are necessary to certain the diagnostic accuracy of CTTA in RCC grading for clinical decision making.
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Affiliation(s)
- Wei Yu
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Gao Liang
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Lichuan Zeng
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yang Yang
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yinghua Wu
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
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21
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Luo S, Wei R, Lu S, Lai S, Wu J, Wu Z, Pang X, Wei X, Jiang X, Zhen X, Yang R. Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis. Eur Radiol 2021; 32:2340-2350. [PMID: 34636962 DOI: 10.1007/s00330-021-08322-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/18/2021] [Accepted: 09/09/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To investigate the influence of different volume of interest (VOI) delineation strategies on machine learning-based predictive models for discrimination between low and high nuclear grade clear cell renal cell carcinoma (ccRCC) on dynamic contrast-enhanced CT. METHODS This study retrospectively collected 177 patients with pathologically proven ccRCC (124 low-grade; 53 high-grade). Tumor VOI was manually segmented, followed by artificially introducing uncertainties as: (i) contour-focused VOI, (ii) margin erosion of 2 or 4 mm, and (iii) margin dilation (2, 4, or 6 mm) inclusive of perirenal fat, peritumoral renal parenchyma, or both. Radiomics features were extracted from four-phase CT images (unenhanced phase (UP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP)). Different combinations of four-phasic features for each VOI delineation strategy were used to build 176 classification models. The best VOI delineation strategy and superior CT phase were identified and the top-ranked features were analyzed. RESULTS Features extracted from UP and EP outperformed features from other single/combined phase(s). Shape features and first-order statistics features exhibited superior discrimination. The best performance (ACC 81%, SEN 67%, SPE 87%, AUC 0.87) was achieved with radiomics features extracted from UP and EP based on contour-focused VOI. CONCLUSION Shape and first-order features extracted from UP + EP images are better feature representations. Contour-focused VOI erosion by 2 mm or dilation by 4 mm within peritumor renal parenchyma exerts limited impact on discriminative performance. It provides a reference for segmentation tolerance in radiomics-based modeling for ccRCC nuclear grading. KEY POINTS • Lesion delineation uncertainties are tolerated within a VOI erosion range of 2 mm or dilation range of 4 mm within peritumor renal parenchyma for radiomics-based ccRCC nuclear grading. • Radiomics features extracted from unenhanced phase and excretory phase are superior to other single/combined phase(s) at differentiating high vs low nuclear grade ccRCC. • Shape features and first-order statistics features showed superior discriminative capability compared to texture features.
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Affiliation(s)
- Shiwei Luo
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China
| | - Ruili Wei
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China
| | - Songlin Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, Guangdong, 510520, China
| | - Jialiang Wu
- Department of Radiology, University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, 518000, China
| | - Zhe Wu
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China
| | - Xinrui Pang
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China
| | - Xinqing Jiang
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.
| | - Ruimeng Yang
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China.
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22
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Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC). Abdom Radiol (NY) 2021; 46:4278-4288. [PMID: 33855609 DOI: 10.1007/s00261-021-03083-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/22/2021] [Accepted: 03/31/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE The purpose of this study was to evaluate the use of CT radiomics features and machine learning analysis to identify aggressive tumor features, including high nuclear grade (NG) and sarcomatoid (sarc) features, in large renal cell carcinomas (RCCs). METHODS CT-based volumetric radiomics analysis was performed on non-contrast (NC) and portal venous (PV) phase multidetector computed tomography images of large (> 7 cm) untreated RCCs in 141 patients (46W/95M, mean age 60 years). Machine learning analysis was applied to the extracted radiomics data to evaluate for association with high NG (grade 3-4), with multichannel analysis for NG performed in a subset of patients (n = 80). A similar analysis was performed in a sarcomatoid rich cohort (n = 43, 31M/12F, mean age 63.7 years) using size-matched non-sarcomatoid controls (n = 49) for identification of sarcomatoid change. RESULTS The XG Boost Model performed best on the tested data. After manual and machine feature extraction, models consisted of 3, 7, 5, 10 radiomics features for NC sarc, PV sarc, NC NG and PV NG, respectively. The area under the receiver operating characteristic curve (AUC) for these models was 0.59, 0.65, 0.69 and 0.58 respectively. The multichannel NG model extracted 6 radiomic features using the feature selection strategy and showed an AUC of 0.67. CONCLUSIONS Statistically significant but weak associations between aggressive tumor features (high nuclear grade, sarcomatoid features) in large RCC were identified using 3D radiomics and machine learning analysis.
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Tang L, Li W, Zeng X, Wang R, Yang X, Luo G, Chen Q, Wang L, Song B. Value of artificial intelligence model based on unenhanced computed tomography of urinary tract for preoperative prediction of calcium oxalate monohydrate stones in vivo. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1129. [PMID: 34430570 PMCID: PMC8350703 DOI: 10.21037/atm-21-965] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 06/03/2021] [Indexed: 02/05/2023]
Abstract
Background Urolithiasis is a global disease with a high incidence and recurrence rate, and stone composition is closely related to the choice of treatment and preventive measures. Calcium oxalate monohydrate (COM) is the most common in clinical practice, which is hard and difficult to fragment. Preoperative identification of its components and selection of effective surgical methods can reduce the risk of patients having a second operation. Methods that can be used for stone composition analysis include infrared spectroscopy, X-ray diffraction, and polarized light microscopy, but they are all performed on stone specimens in vitro after surgery. This study aimed to design and develop an artificial intelligence (AI) model based on unenhanced computed tomography (CT) images of the urinary tract, and to investigate the predictive ability of the model for COM stones in vivo preoperatively, so as to provide surgeons with more accurate diagnostic information. Methods Preoperative unenhanced CT images of patients with urinary calculi whose components were determined by infrared spectroscopy in a single center were retrospectively analyzed, including 337 cases of COM stones and 170 of non-COM stones. All images were manually segmented and the image features were extracted, and randomly divided into the training and testing sets in a ratio of 7:3. The least absolute shrinkage and selection operation algorithm (LASSO) was used to construct the AI model, and classification of the training and testing sets was carried out. Results A total of 1,218 radiomics imaging features were extracted, and 8 features with non-zero coefficients were finally obtained. The sensitivity, specificity and accuracy of the AI model were 90.5%, 84.3% and 88.5% for the training set, and 90.1%, 84.3% and 88.3% for the testing set. The area under the curve was 0.935 for the training set and 0.933 for the testing set. Conclusions The AI model based on unenhanced CT images of the urinary tract can predict COM and non-COM stones in vivo preoperatively, and the model has high sensitivity, specificity and accuracy.
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Affiliation(s)
- Lei Tang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Wuchao Li
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Xianchun Zeng
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Rongpin Wang
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Xiushu Yang
- Department of Urological Surgery, Guizhou Provincial People's Hospital, Guiyang, China
| | - Guangheng Luo
- Department of Urological Surgery, Guizhou Provincial People's Hospital, Guiyang, China
| | - Qijian Chen
- College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Lihui Wang
- College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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Stanzione A, Ricciardi C, Cuocolo R, Romeo V, Petrone J, Sarnataro M, Mainenti PP, Improta G, De Rosa F, Insabato L, Brunetti A, Maurea S. MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study. J Digit Imaging 2021; 33:879-887. [PMID: 32314070 DOI: 10.1007/s10278-020-00336-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The Fuhrman nuclear grade is a recognized prognostic factor for patients with clear cell renal cell carcinoma (CCRCC) and its pre-treatment evaluation significantly affects decision-making in terms of management. In this study, we aimed to assess the feasibility of a combined approach of radiomics and machine learning based on MR images for a non-invasive prediction of Fuhrman grade, specifically differentiation of high- from low-grade tumor and grade assessment. Images acquired on a 3-Tesla scanner (T2-weighted and post-contrast) from 32 patients (20 with low-grade and 12 with high-grade tumor) were annotated to generate volumes of interest enclosing CCRCC lesions. After image resampling, normalization, and filtering, 2438 features were extracted. A two-step feature reduction process was used to between 1 and 7 features depending on the algorithm employed. A J48 decision tree alone and in combination with ensemble learning methods were used. In the differentiation between high- and low-grade tumors, all the ensemble methods achieved an accuracy greater than 90%. On the other end, the best results in terms of accuracy (84.4%) in the assessment of tumor grade were achieved by the random forest. These evidences support the hypothesis that a combined radiomic and machine learning approach based on MR images could represent a feasible tool for the prediction of Fuhrman grade in patients affected by CCRCC.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Carlo Ricciardi
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy.
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Jessica Petrone
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Michela Sarnataro
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging of the National Research Council (CNR), Naples, Italy
| | - Giovanni Improta
- Department of Public Health, University of Naples "Federico II", Naples, Italy
| | - Filippo De Rosa
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Luigi Insabato
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
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Kuusk T, Neves JB, Tran M, Bex A. Radiomics to better characterize small renal masses. World J Urol 2021; 39:2861-2868. [PMID: 33495866 DOI: 10.1007/s00345-021-03602-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 01/11/2021] [Indexed: 12/23/2022] Open
Abstract
PURPOSE Radiomics is a specific field of medical research that uses programmable recognition tools to extract objective information from standard images to combine with clinical data, with the aim of improving diagnostic, prognostic, and predictive accuracy beyond standard visual interpretation. We performed a narrative review of radiomic applications that may support improved characterization of small renal masses (SRM). The main focus of the review was to identify and discuss methods which may accurately differentiate benign from malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat (fat-poor AML) and oncocytoma. Furthermore, prediction of grade, sarcomatoid features, and gene mutations would be of importance in terms of potential clinical utility in prognostic stratification and selecting personalised patient management strategies. METHODS A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 September 2020 to identify the English literature relevant to radiomics applications in renal tumour assessment. In total, 42 articles were included in the analysis in 3 main categories related to SRM: prediction of benign versus malignant SRM, subtypes, and nuclear grade, and other features of aggressiveness. CONCLUSION Overall, studies reported the superiority of radiomics over expert radiological assessment, but were mainly of retrospective design and therefore of low-quality evidence. However, it is clear that radiomics is an attractive modality that has the potential to improve the non-invasive diagnostic accuracy of SRM imaging and prediction of its natural behaviour. Further prospective validation studies of radiomics are needed to augment management algorithms of SRM.
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Affiliation(s)
- Teele Kuusk
- Urology Department, Darent Valley Hospital, Dartford and Gravesham NHS Trust, Dartford, UK
- Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK
| | - Joana B Neves
- Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK
| | - Maxine Tran
- Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK
- UCL Division of Surgery and Interventional Science, London, UK
| | - Axel Bex
- Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK.
- UCL Division of Surgery and Interventional Science, London, UK.
- Surgical Oncology Division, Urology Department, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
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Lee M, Wei S, Anaokar J, Uzzo R, Kutikov A. Kidney cancer management 3.0: can artificial intelligence make us better? Curr Opin Urol 2021; 31:409-415. [PMID: 33882560 DOI: 10.1097/mou.0000000000000881] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence holds tremendous potential for disrupting clinical medicine. Here we review the current role of artificial intelligence in the kidney cancer space. RECENT FINDINGS Machine learning and deep learning algorithms have been developed using information extracted from radiomic, histopathologic, and genomic datasets of patients with renal masses. SUMMARY Although artificial intelligence applications in medicine are still in their infancy, they already hold immediate promise to improve accuracy of renal mass characterization, grade, and prognostication. As algorithms become more robust and generalizable, artificial intelligence is poised to significantly disrupt kidney cancer care.
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Affiliation(s)
| | | | - Jordan Anaokar
- Department of Diagnostic Imaging, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA
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Brodie A, Dai N, Teoh JYC, Decaestecker K, Dasgupta P, Vasdev N. Artificial intelligence in urological oncology: An update and future applications. Urol Oncol 2021; 39:379-399. [PMID: 34024704 DOI: 10.1016/j.urolonc.2021.03.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 12/20/2020] [Accepted: 03/21/2021] [Indexed: 01/16/2023]
Abstract
There continues to be rapid developments and research in the field of Artificial Intelligence (AI) in Urological Oncology worldwide. In this review we discuss the basics of AI, application of AI per tumour group (Renal, Prostate and Bladder Cancer) and application of AI in Robotic Urological Surgery. We also discuss future applications of AI being developed with the benefits to patients with Urological Oncology.
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Affiliation(s)
- Andrew Brodie
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Nick Dai
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Jeremy Yuen-Chun Teoh
- S.H. Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Prokar Dasgupta
- Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Nikhil Vasdev
- Hertfordshire and Bedfordshire Urological Cancer Centre, Department of Urology, Lister Hospital, Stevenage, United Kingdom; School of Medicine and Life Sciences, University of Hertfordshire, Hatfield, United Kingdom.
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28
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Han D, Yu Y, He T, Yu N, Dang S, Wu H, Ren J, Duan X. Effect of radiomics from different virtual monochromatic images in dual-energy spectral CT on the WHO/ISUP classification of clear cell renal cell carcinoma. Clin Radiol 2021; 76:627.e23-627.e29. [PMID: 33985770 DOI: 10.1016/j.crad.2021.02.033] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Accepted: 02/10/2021] [Indexed: 12/24/2022]
Abstract
AIM To investigate the effect of radiomics obtained from different virtual monochromatic images (VMIs) in dual-energy spectral computed tomography (CT) on the World Health Organization/International Association for Urological Pathology (WHO/ISUP) classification of clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS A retrospective study of 99 ccRCC patients who underwent contrast-enhanced dual-energy CT was undertaken. ccRCC was confirmed at surgery or biopsy and graded according to the WHO/ISUP pathological grading criteria as low grade (n=68, grade I and II) or high grade (n=31, grade III and IV). Radiomics risk scores (RRSs) for differentiating high and low grades of ccRCC were constructed from 11 sets of VMI in (40-140 keV, 10 keV interval) the cortical phase. Receiver operating characteristic (ROC) curves were drawn and the area under the curves (AUCs) was calculated to evaluate the discriminatory power of RRS for each VMI. The Hosmer-Lemeshow test was used to evaluate the goodness-of-fit of each model and the decision curve was used to analyse its net benefit to patients. RESULTS The AUC values for distinguishing low-from high-grade ccRCC with RRS of 40-140 keV VMIs were all >0.920. The Hosmer-Lemeshow test showed that the p-values of RRS of VMIs were >0.05, suggesting good fits. In the decision curve analysis, RRS from the 40-140 keV VMIs had similar decision curves and provided better net benefits than considering all patients either as high-grade or low-grade. CONCLUSIONS The RRS obtained from multiple VMIs in dual-energy spectral CT have high diagnostic efficiencies for distinguishing between low- and high-grade ccRCC with no significant differences between different VMIs.
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Affiliation(s)
- D Han
- Department of Medical Image, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Y Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - T He
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - N Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - S Dang
- Department of Medical Image, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - H Wu
- Pathology Department, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - J Ren
- GE Healthcare China, Beijing, China
| | - X Duan
- Department of Medical Image, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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29
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Bhandari A, Ibrahim M, Sharma C, Liong R, Gustafson S, Prior M. CT-based radiomics for differentiating renal tumours: a systematic review. Abdom Radiol (NY) 2021; 46:2052-2063. [PMID: 33136182 DOI: 10.1007/s00261-020-02832-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 10/06/2020] [Accepted: 10/12/2020] [Indexed: 12/24/2022]
Abstract
PURPOSE Differentiating renal tumours into grades and tumour subtype from medical imaging is important for patient management; however, there is an element of subjectivity when performed qualitatively. Quantitative analysis such as radiomics may provide a more objective approach. The purpose of this article is to systematically review the literature on computed tomography (CT) radiomics for grading and differentiating renal tumour subtypes. An educational perspective will also be provided. METHODS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was followed. PubMed, Scopus and Web of Science were searched for relevant articles. The quality of each study was assessed using the Radiomic Quality Score (RQS). RESULTS 13 studies were found. The main outcomes were prediction of pathological grade and differentiating between renal tumour types, measured as area under the curve (AUC) for either the receiver operator curve or precision recall curve. Features extracted to predict pathological grade or tumour subtype included shape, intensity, texture and wavelet (a type of higher order feature). Four studies differentiated between low-grade and high-grade clear cell renal cell cancer (RCC) with good performance (AUC = 0.82-0.978). One other study differentiated low- and high-grade chromophobe with AUC = 0.84. Finally, eight studies used radiomics to differentiate between tumour types such as clear cell RCC, fat-poor angiomyolipoma, papillary RCC, chromophobe RCC and renal oncocytoma with high levels of performance (AUC 0.82-0.96). CONCLUSION Renal tumours can be pathologically classified using CT-based radiomics with good performance. The main radiomic feature used for tumour differentiation was texture. Fuhrman was the most common pathologic grading system used in the reviewed studies. Renal tumour grading studies should be extended beyond clear cell RCC and chromophobe RCC. Further research with larger prospective studies, performed in the clinical setting, across multiple institutions would help with clinical translation to the radiologist's workstation.
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Hussain MA, Hamarneh G, Garbi R. Learnable image histograms-based deep radiomics for renal cell carcinoma grading and staging. Comput Med Imaging Graph 2021; 90:101924. [PMID: 33895621 DOI: 10.1016/j.compmedimag.2021.101924] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/07/2021] [Accepted: 04/05/2021] [Indexed: 12/11/2022]
Abstract
Fuhrman cancer grading and tumor-node-metastasis (TNM) cancer staging systems are typically used by clinicians in the treatment planning of renal cell carcinoma (RCC), a common cancer in men and women worldwide. Pathologists typically use percutaneous renal biopsy for RCC grading, while staging is performed by volumetric medical image analysis before renal surgery. Recent studies suggest that clinicians can effectively perform these classification tasks non-invasively by analyzing image texture features of RCC from computed tomography (CT) data. However, image feature identification for RCC grading and staging often relies on laborious manual processes, which is error prone and time-intensive. To address this challenge, this paper proposes a learnable image histogram in the deep neural network framework that can learn task-specific image histograms with variable bin centers and widths. The proposed approach enables learning statistical context features from raw medical data, which cannot be performed by a conventional convolutional neural network (CNN). The linear basis function of our learnable image histogram is piece-wise differentiable, enabling back-propagating errors to update the variable bin centers and widths during training. This novel approach can segregate the CT textures of an RCC in different intensity spectra, which enables efficient Fuhrman low (I/II) and high (III/IV) grading as well as RCC low (I/II) and high (III/IV) staging. The proposed method is validated on a clinical CT dataset of 159 patients from The Cancer Imaging Archive (TCIA) database, and it demonstrates 80% and 83% accuracy in RCC grading and staging, respectively.
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Affiliation(s)
| | - Ghassan Hamarneh
- Medical Image Analysis Lab, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
| | - Rafeef Garbi
- BiSICL, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
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31
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Tang X, Pang T, Yan WF, Qian WL, Gong YL, Yang ZG. The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy. Front Oncol 2021; 11:591502. [PMID: 33747910 PMCID: PMC7973240 DOI: 10.3389/fonc.2021.591502] [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: 08/10/2020] [Accepted: 01/07/2021] [Indexed: 02/05/2023] Open
Abstract
Background and purpose Radiomics is an emerging field of quantitative imaging. The prognostic value of radiomics analysis in patients with localized clear cell renal cell carcinoma (ccRCC) after nephrectomy remains unknown. Methods Computed tomography images of 167 eligible cases were obtained from the Cancer Imaging Archive database. Radiomics features were extracted from the region of interest contoured manually for each patient. Hierarchical clustering was performed to divide patients into distinct groups. Prognostic assessments were performed by Kaplan–Meier curves, COX regression, and least absolute shrinkage and selection operator COX regression. Besides, transcriptome mRNA data were also included in the prognostic analyses. Endpoints were overall survival (OS) and disease-free survival (DFS). Concordance index (C-index), decision curve analysis and calibration curves with 1,000 bootstrapping replications were used for model’s validation. Results Hierarchical clustering groups from nephrographic features and mRNA can divide patients into different prognostic groups while clustering groups from corticomedullary or unenhanced phase couldn’t distinguish patients’ prognosis. In multivariate analyses, 11 OS-predicting and eight DFS-predicting features were identified in nephrographic phase. Similarly, seven OS-predictors and seven DFS-predictors were confirmed in mRNA data. In contrast, limited prognostic features were found in corticomedullary (two OS-predictor and two DFS-predictors) and unenhanced phase (one OS-predictors and two DFS-predictors). Prognostic models combining both nephrographic features and mRNA showed improved C-index than any model alone (C-index: 0.927 and 0.879 for OS- and DFS-predicting, respectively). In addition, decision curves and calibration curves also revealed the great performance of the novel models. Conclusion We firstly investigated the prognostic significance of preoperative radiomics signatures in ccRCC patients. Radiomics features obtained from nephrographic phase had stronger predictive ability than features from corticomedullary or unenhanced phase. Multi-omics models combining radiomics and transcriptome data could further increase the predictive accuracy.
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Affiliation(s)
- Xin Tang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Thoracic Oncology and State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Pang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Wei-Feng Yan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Wen-Lei Qian
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - You-Ling Gong
- Department of Thoracic Oncology and State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhi-Gang Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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Lai S, Sun L, Wu J, Wei R, Luo S, Ding W, Liu X, Yang R, Zhen X. Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma. Cancer Manag Res 2021; 13:999-1008. [PMID: 33568946 PMCID: PMC7869703 DOI: 10.2147/cmar.s290327] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 01/08/2021] [Indexed: 11/23/2022] Open
Abstract
Objective To investigate the predictive performance of different machine learning models for the discrimination of low and high nuclear grade clear cell renal cell carcinoma (ccRCC) by using multiphase computed tomography (CT)-based radiomic features. Materials and Methods A total of 137 consecutive patients with pathologically proven ccRCC (including 96 low-grade [grade 1 or 2] and 41 high-grade [grade 3 or 4] ccRCC) from January 2011 to January 2019 were enrolled in this retrospective study. Target region of interest (ROI) delineation followed by texture extraction was performed on a representative slice with the largest section of the tumor on the four-phase (unenhanced phase [UP], corticomedullary phase [CMP], nephrographic phase [NP] and excretory phase [EP]) CT images. Fifteen concatenations of the four-phase features were fed into 176 classification models (built with 8 classifiers and 22 feature selection methods), the classification performances of the 2640 resultant discriminative models were compared, and the top-ranked features were analyzed. Results Image features extracted from the unenhanced phase (UP) CT images demonstrated a dominant classification performance over features from the other three phases. The discriminative model "Bagging + CMIM" achieved the highest classification AUC of 0.75. The top-ranked features from the UP included one shape-based feature and five first-order statistical features. Conclusion Image features extracted from the UP are more effective than other CT phases in differentiating low and high nuclear grade ccRCC based on machine learning-based classification modeling.
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Affiliation(s)
- Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, Guangdong, 510520, People's Republic of China
| | - Lei Sun
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Jialiang Wu
- Department of Radiology, The University of Hong Kong Shenzhen Hospital, Shenzhen, Guangdong, 518000, People's Republic of China
| | - Ruili Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Shiwei Luo
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Wenshuang Ding
- Department of Pathology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Xilong Liu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Ruimeng Yang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
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Moldovanu CG, Boca B, Lebovici A, Tamas-Szora A, Feier DS, Crisan N, Andras I, Buruian MM. Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features. J Pers Med 2020; 11:jpm11010008. [PMID: 33374569 PMCID: PMC7822466 DOI: 10.3390/jpm11010008] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 12/19/2020] [Accepted: 12/21/2020] [Indexed: 12/11/2022] Open
Abstract
Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92-1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.
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Affiliation(s)
- Claudia-Gabriela Moldovanu
- Department of Radiology and Medical Imaging, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (C.-G.M.); (M.M.B.)
- Department of Radiology, Emergency Clinical County Hospital of Cluj-Napoca, 400006 Cluj-Napoca, Romania;
| | - Bianca Boca
- Department of Radiology and Medical Imaging, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (C.-G.M.); (M.M.B.)
- Department of Radiology, Emergency Clinical County Hospital of Cluj-Napoca, 400006 Cluj-Napoca, Romania;
- Correspondence: (B.B.); (A.L.)
| | - Andrei Lebovici
- Department of Radiology, Emergency Clinical County Hospital of Cluj-Napoca, 400006 Cluj-Napoca, Romania;
- Department of Radiology, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
- Correspondence: (B.B.); (A.L.)
| | - Attila Tamas-Szora
- Department of Radiology, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania;
| | - Diana Sorina Feier
- Department of Radiology, Emergency Clinical County Hospital of Cluj-Napoca, 400006 Cluj-Napoca, Romania;
- Department of Radiology, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Nicolae Crisan
- Department of Urology, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (N.C.); (I.A.)
| | - Iulia Andras
- Department of Urology, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (N.C.); (I.A.)
| | - Mircea Marian Buruian
- Department of Radiology and Medical Imaging, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (C.-G.M.); (M.M.B.)
- Department of Radiology, Emergency Clinical County Hospital Târgu Mureș, 540136 Târgu Mureș, Romania
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Hancock JT, Khoshgoftaar TM. CatBoost for big data: an interdisciplinary review. JOURNAL OF BIG DATA 2020; 7:94. [PMID: 33169094 PMCID: PMC7610170 DOI: 10.1186/s40537-020-00369-8] [Citation(s) in RCA: 146] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 10/19/2020] [Indexed: 05/25/2023]
Abstract
Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT's in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost's effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.
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Affiliation(s)
- John T. Hancock
- Florida Atlantic University, 777 Glades Road, Boca Raton, FL USA
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Ma R, Vanstrum EB, Lee R, Chen J, Hung AJ. Machine learning in the optimization of robotics in the operative field. Curr Opin Urol 2020; 30:808-816. [PMID: 32925312 PMCID: PMC7735438 DOI: 10.1097/mou.0000000000000816] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
PURPOSE OF REVIEW The increasing use of robotics in urologic surgery facilitates collection of 'big data'. Machine learning enables computers to infer patterns from large datasets. This review aims to highlight recent findings and applications of machine learning in robotic-assisted urologic surgery. RECENT FINDINGS Machine learning has been used in surgical performance assessment and skill training, surgical candidate selection, and autonomous surgery. Autonomous segmentation and classification of surgical data have been explored, which serves as the stepping-stone for providing real-time surgical assessment and ultimately, improve surgical safety and quality. Predictive machine learning models have been created to guide appropriate surgical candidate selection, whereas intraoperative machine learning algorithms have been designed to provide 3-D augmented reality and real-time surgical margin checks. Reinforcement-learning strategies have been utilized in autonomous robotic surgery, and the combination of expert demonstrations and trial-and-error learning by the robot itself is a promising approach towards autonomy. SUMMARY Robot-assisted urologic surgery coupled with machine learning is a burgeoning area of study that demonstrates exciting potential. However, further validation and clinical trials are required to ensure the safety and efficacy of incorporating machine learning into surgical practice.
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Affiliation(s)
- Runzhuo Ma
- Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California, USA
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Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency. AJR Am J Roentgenol 2020; 215:1113-1122. [PMID: 32960663 DOI: 10.2214/ajr.20.22847] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items. MATERIALS AND METHODS. A systematic literature search was conducted using PubMed to identify original research studies about the application of AI to renal mass characterization. Besides baseline study characteristics, a total of 15 methodologic quality items were extracted and evaluated on the basis of the following four main categories: modeling, performance evaluation, clinical utility, and transparency items. The qualitative synthesis was presented using descriptive statistics with an accompanying narrative. RESULTS. Thirty studies were included in this systematic review. Overall, the methodologic quality items were mostly favorable for modeling (63%) and performance evaluation (63%). Even so, the studies (57%) more frequently constructed their work on nonrobust features. Furthermore, only a few studies (10%) had a generalizability assessment with independent or external validation. The studies were mostly unsuccessful in terms of clinical utility evaluation (89%) and transparency (97%) items. For clinical utility, the interesting findings were lack of comparisons with both radiologists' evaluation (87%) and traditional models (70%) in most of the studies. For transparency, most studies (97%) did not share their data with the public. CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.
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Lopes Vendrami C, McCarthy RJ, Villavicencio CP, Miller FH. Predicting common solid renal tumors using machine learning models of classification of radiologist-assessed magnetic resonance characteristics. Abdom Radiol (NY) 2020; 45:2797-2809. [PMID: 32666233 DOI: 10.1007/s00261-020-02637-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 06/23/2020] [Accepted: 07/04/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE Solid renal masses (SRM) are difficult to differentiate based on standard MR features. The purpose of this study was to assess MR imaging features of SRM to evaluate performance of ensemble methods of classifying SRM subtypes. MATERIALS AND METHODS MR images of SRM (n = 330) were retrospectively evaluated for standard and multiparametric (mp) features. Models of MR features for predicting malignant and benign lesions as well as subtyping SRM were developed using a training dataset and performance was evaluated in a test data-set using recursive partitioning (RP), gradient booting machine (GBM), and random forest (RF) methods. RESULTS In the test dataset, GBM and RF models demonstrated an accuracy of 86% (95% CI 75% to 93%) for predicting benign versus malignant SRM compared to 83% (95% CI 71% to 91%) for the RP model. RF had the greatest accuracy in predicting SRM subtypes, 81.2% (95% CI 69.5% to 89.9%) compared with GBM 73.4% (95% CI 60.9% to 83.7%) or RP 70.3% (95% CI 57.6% to 81.1%). Marginal homogeneity was reduced by the RF model compared with the RP model (P < 0.001), but not the GBM model (P = 0.135). All models had high sensitivity and specificity for clear cell and papillary renal cell carcinomas (RCC), but performed less well in differentiating chromophobe RCC, oncocytomas, and fat-poor angiomyolipomas. CONCLUSION Ensemble methods for prediction of SRM from radiologist-assessed image characteristics have high accuracy for distinguishing benign and malignant lesions. SRM subtype classification is limited by the ability to categorize chromophobe RCCs, oncocytomas, and fat-poor angiomyolipomas.
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Affiliation(s)
- Camila Lopes Vendrami
- Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N. St. Clair St. Suite 800, Chicago, IL, 60611, USA
| | - Robert J McCarthy
- Department of Anesthesiology, Rush University, Chicago, IL, 60612, USA
| | - Carolina Parada Villavicencio
- Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N. St. Clair St. Suite 800, Chicago, IL, 60611, USA
| | - Frank H Miller
- Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N. St. Clair St. Suite 800, Chicago, IL, 60611, USA.
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Machine Learning in Radiomic Renal Mass Characterization: Fundamentals, Applications, Challenges, and Future Directions. AJR Am J Roentgenol 2020; 215:920-928. [PMID: 32783560 DOI: 10.2214/ajr.19.22608] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
OBJECTIVE. The purpose of this study is to provide an overview of the traditional machine learning (ML)-based and deep learning-based radiomic approaches, with focus placed on renal mass characterization. CONCLUSION. ML currently has a very low barrier to entry into general medical practice because of the availability of many open-source, free, and easy-to-use toolboxes. Therefore, it should not be surprising to see its related applications in renal mass characterization. A wider picture of the previous works might be beneficial to move this field forward.
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Han D, Yu Y, Yu N, Dang S, Wu H, Jialiang R, He T. Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT. Br J Radiol 2020; 93:20200131. [PMID: 32706977 DOI: 10.1259/bjr.20200131] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE Comparing the prediction models for the ISUP/WHO grade of clear cell renal cell carcinoma (ccRCC) based on CT radiomics and conventional contrast-enhanced CT (CECT). METHODS The corticomedullary phase images of 119 cases of low-grade (I and II) and high-grade (III and IV) ccRCC based on 2016 ISUP/WHO pathological grading criteria were analyzed retrospectively. The patients were randomly divided into training and validation set by stratified sampling according to 7:3 ratio. Prediction models of ccRCC differentiation were constructed using CT radiomics and conventional CECT findings in the training setandwere validated using validation set. The discrimination, calibration, net reclassification index (NRI) and integrated discrimination improvement index (IDI) of the two prediction models were further compared. The decision curve was used to analyze the net benefit of patients under different probability thresholds of the two models. RESULTS In the training set, the C-statistics of radiomics prediction model was statistically higher than that of CECT (p < 0.05), with NRI of 9.52% and IDI of 21.6%, both with statistical significance (p < 0.01).In the validation set, the C-statistics of radiomics prediction model was also higher but did not show statistical significance (p = 0.07). The NRI and IDI was 14.29 and 33.7%, respectively, both statistically significant (p < 0.01). Validation set decision curve analysis showed the net benefit improvement of CT radiomics prediction model in the range of 3-81% over CECT. CONCLUSION The prediction model using CT radiomics in corticomedullary phase is more effective for ccRCC ISUP/WHO grade than conventional CECT. ADVANCES IN KNOWLEDGE As a non-invasive analysis method, radiomics can predict the ISUP/WHO grade of ccRCC more effectively than traditional enhanced CT.
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Affiliation(s)
- Dong Han
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Yong Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Nan Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Shan Dang
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Hongpei Wu
- Department of Pathology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | | | - Taiping He
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
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Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature. Cancers (Basel) 2020; 12:cancers12061387. [PMID: 32481542 PMCID: PMC7352711 DOI: 10.3390/cancers12061387] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 05/22/2020] [Accepted: 05/26/2020] [Indexed: 12/21/2022] Open
Abstract
Radiomics texture analysis offers objective image information that could otherwise not be obtained by radiologists′ subjective radiological interpretation. We investigated radiomics applications in renal tumor assessment and provide a comprehensive review. A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 March 2020 to identify English literature relevant to radiomics applications in renal tumor assessment. In total, 42 articles were included in the analysis and divided into four main categories: renal mass differentiation, nuclear grade prediction, gene expression-based molecular signatures, and patient outcome prediction. The main area of research involves accurately differentiating benign and malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat and oncocytoma. Nuclear grade prediction may enhance proper patient selection for risk-stratified treatment. Radiomics-predicted gene mutations may serve as surrogate biomarkers for high-risk disease, while predicting patients’ responses to targeted therapies and their outcomes will help develop personalized treatment algorithms. Studies generally reported the superiority of radiomics over expert radiological interpretation. Radiomics provides an alternative to subjective image interpretation for improving renal tumor diagnostic accuracy. Further incorporation of clinical and imaging data into radiomics algorithms will augment tumor prediction accuracy and enhance individualized medicine.
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A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma. Eur J Radiol 2020; 129:109079. [PMID: 32526669 DOI: 10.1016/j.ejrad.2020.109079] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 05/14/2020] [Accepted: 05/15/2020] [Indexed: 12/19/2022]
Abstract
PURPOSE To investigate the effects of different methodologies on the performance of deep learning (DL) model for differentiating high- from low-grade clear cell renal cell carcinoma (ccRCC). METHOD Patients with pathologically proven ccRCC diagnosed between October 2009 and March 2019 were assigned to training or internal test dataset, and external test dataset was acquired from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. The effects of different methodologies on the performance of DL-model, including image cropping (IC), setting the attention level, selecting model complexity (MC), and applying transfer learning (TL), were compared using repeated measures analysis of variance (ANOVA) and receiver operating characteristic (ROC) curve analysis. The performance of DL-model was evaluated through accuracy and ROC analyses with internal and external tests. RESULTS In this retrospective study, patients (n = 390) from one hospital were randomly assigned to training (n = 370) or internal test dataset (n = 20), and the other 20 patients from TCGA-KIRC database were assigned to external test dataset. IC, the attention level, MC, and TL had major effects on the performance of the DL-model. The DL-model based on the cropping of an image less than three times the tumor diameter, without attention, a simple model and the application of TL achieved the best performance in internal (ACC = 73.7 ± 11.6%, AUC = 0.82 ± 0.11) and external (ACC = 77.9 ± 6.2%, AUC = 0.81 ± 0.04) tests. CONCLUSIONS CT-based DL model can be conveniently applied for grading ccRCC with simple IC in routine clinical practice.
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Suarez-Ibarrola R, Hein S, Reis G, Gratzke C, Miernik A. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J Urol 2019; 38:2329-2347. [PMID: 31691082 DOI: 10.1007/s00345-019-03000-5] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 10/25/2019] [Indexed: 01/15/2023] Open
Abstract
PURPOSE The purpose of the study was to provide a comprehensive review of recent machine learning (ML) and deep learning (DL) applications in urological practice. Numerous studies have reported their use in the medical care of various urological disorders; however, no critical analysis has been made to date. METHODS A detailed search of original articles was performed using the PubMed MEDLINE database to identify recent English literature relevant to ML and DL applications in the fields of urolithiasis, renal cell carcinoma (RCC), bladder cancer (BCa), and prostate cancer (PCa). RESULTS In total, 43 articles were included addressing these four subfields. The most common ML and DL application in urolithiasis is in the prediction of endourologic surgical outcomes. The main area of research involving ML and DL in RCC concerns the differentiation between benign and malignant small renal masses, Fuhrman nuclear grade prediction, and gene expression-based molecular signatures. BCa studies employ radiomics and texture feature analysis for the distinction between low- and high-grade tumors, address accurate image-based cytology, and use algorithms to predict treatment response, tumor recurrence, and patient survival. PCa studies aim at developing algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction. Studies consistently found the superiority of these methods over traditional statistical methods. CONCLUSIONS The continuous incorporation of clinical data, further ML and DL algorithm retraining, and generalizability of models will augment the prediction accuracy and enhance individualized medicine.
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Affiliation(s)
- Rodrigo Suarez-Ibarrola
- Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany.
| | - Simon Hein
- Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Gerd Reis
- Department Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
| | - Christian Gratzke
- Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Arkadiusz Miernik
- Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany
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