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Yang F, Zhang D, Zhao LH, Mao YR, Mu J, Wang HL, Pang L, Yang SQ, Wei X, Liu CW. Prediction of clear cell renal cell carcinoma ≤ 4cm: visual assessment of ultrasound characteristics versus ultrasonographic radiomics analysis. Front Oncol 2024; 14:1298710. [PMID: 39114306 PMCID: PMC11304449 DOI: 10.3389/fonc.2024.1298710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 07/08/2024] [Indexed: 08/10/2024] Open
Abstract
Objective To investigate the diagnostic efficacy of the clinical ultrasound imaging model, ultrasonographic radiomics model, and comprehensive model based on ultrasonographic radiomics for the differentiation of small clear cell Renal Cell Carcinoma (ccRCC) and Renal Angiomyolipoma (RAML). Methods The clinical, ultrasound, and contrast-enhanced CT(CECT) imaging data of 302 small renal tumors (maximum diameter ≤ 4cm) patients in Tianjin Medical University Cancer Institute and Hospital from June 2018 to June 2022 were retrospectively analyzed, with 182 patients of ccRCC and 120 patients of RAML. The ultrasound images of the largest diameter of renal tumors were manually segmented by ITK-SNAP software, and Pyradiomics (v3.0.1) module in Python 3.8.7 was applied to extract ultrasonographic radiomics features from ROI segmented images. The patients were randomly divided into training and internal validation cohorts in the ratio of 7:3. The Random Forest algorithm of the Sklearn module was applied to construct the clinical ultrasound imaging model, ultrasonographic radiomics model, and comprehensive model. The efficacy of the prediction models was verified in an independent external validation cohort consisting of 69 patients, from 230 small renal tumor patients in two different institutions. The Delong test compared the predictive ability of three models and CECT. Calibration Curve and clinical Decision Curve Analysis were applied to evaluate the model and determine the net benefit to patients. Results 491 ultrasonographic radiomics features were extracted from 302 small renal tumor patients, and 9 ultrasonographic radiomics features were finally retained for modeling after regression and dimensionality reduction. In the internal validation cohort, the area under the curve (AUC), sensitivity, specificity, and accuracy of the clinical ultrasound imaging model, ultrasonographic radiomics model, comprehensive model, and CECT were 0.75, 76.7%, 60.0%, 70.0%; 0.80, 85.6%, 61.7%, 76.0%; 0.88, 90.6%, 76.7%, 85.0% and 0.90, 92.6%, 88.9%, 91.1%, respectively. In the external validation cohort, AUC, sensitivity, specificity, and accuracy of the three models and CECT were 0.73, 67.5%, 69.1%, 68.3%; 0.89, 86.7%, 80.0%, 83.5%; 0.90, 85.0%, 85.5%, 85.2% and 0.91, 94.6%, 88.3%, 91.3%, respectively. The DeLong test showed no significant difference between the clinical ultrasound imaging model and the ultrasonographic radiomics model (Z=-1.287, P=0.198). The comprehensive model showed superior diagnostic performance than the ultrasonographic radiomics model (Z=4. 394, P<0.001) and the clinical ultrasound imaging model (Z=4. 732, P<0.001). Moreover, there was no significant difference in AUC between the comprehensive model and CECT (Z=-0.252, P=0.801). Both in the internal and external validation cohort, the Calibration Curve and Decision Curve Analysis showed a better performance of the comprehensive model. Conclusion It is feasible to construct an ultrasonographic radiomics model for distinguishing small ccRCC and RAML based on ultrasound images, and the diagnostic performance of the comprehensive model is superior to the clinical ultrasound imaging model and ultrasonographic radiomics model, similar to that of CECT.
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Affiliation(s)
- Fan Yang
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Dai Zhang
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Li-Hui Zhao
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Yi-Ran Mao
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Jie Mu
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Hai-Ling Wang
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Liang Pang
- Department of Urology, Tianjin Occupational Diseases Precaution and Therapeutic Hospital, Tianjin, China
| | - Shi-Qiang Yang
- Department of Urology, Tianjin First Central Hospital, Tianjin, China
| | - Xi Wei
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Chun-Wei Liu
- Department of Cardiology, Tianjin Chest Hospital, Tianjin University, Tianjin, China
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Song H, Wang X, Wu R, Liu W. The influence of manual segmentation strategies and different phases selection on machine learning-based computed tomography in renal tumors: a systematic review and meta-analysis. LA RADIOLOGIA MEDICA 2024; 129:1025-1037. [PMID: 38740709 DOI: 10.1007/s11547-024-01825-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 04/29/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Delineating the region/volume of interest (ROI/VOI) and selecting the phases are of importance in developing machine learning (ML). The results will change when choosing different methods of drawing the ROI/VOI and selecting different phases. However, there is no related standard for delineating the ROI/VOI and selecting the phases in renal tumors to develop ML based on computed tomography (CT). METHODS The PubMed and Web of Science were searched for related studies published until March 1, 2023. Inclusion criteria were studies that developed ML models in renal tumors from CT images. And the binary diagnostic accuracy data were extracted to obtain the outcomes, such as sensitivity (SE), specificity (SP), accuracy (ACC), and area under the curve (AUC). RESULTS Twenty-three papers were included in the meta-analysis with a pooled SE of 87% (95% CI 85-88%), SP of 82% (95% CI 79-85%), and AUC of 91% (95% CI 89-93%) in phases; a pooled SE of 82% (95% CI 80-84%), SP of 85% (95% CI 83-86%), and AUC of 90% (95% CI 88-93%) in phases combined with delineating strategies, respectively. In all different combinations, the contour-focused and single phase produce the highest AUC of 93% (95% CI 90-95%). In subgroup analyses (sample size, year of publication, and geographical distribution), the performance was acceptable on phases and phases combined strategies. CONCLUSIONS To explore the effect of manual segmentation strategies and different phases selection on ML-based CT, we find that the method of single phase (CMP or NP) combined with contour-focused was considered a better strategy compared to the other strategies.
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Affiliation(s)
- Honghao Song
- Department of Pediatric Surgery, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, People's Republic of China
| | - Xiaoqing Wang
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Street, Jinan, 250021, Shandong, People's Republic of China
- Post-doctoral Research Station of Clinical Medicine, Liaocheng People's Hospital, Liaocheng, 252004, Shandong, People's Republic of China
| | - Rongde Wu
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Street, Jinan, 250021, Shandong, People's Republic of China
| | - Wei Liu
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Street, Jinan, 250021, Shandong, People's Republic of China.
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Feretzakis G, Juliebø-Jones P, Tsaturyan A, Sener TE, Verykios VS, Karapiperis D, Bellos T, Katsimperis S, Angelopoulos P, Varkarakis I, Skolarikos A, Somani B, Tzelves L. Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review. Cancers (Basel) 2024; 16:810. [PMID: 38398201 PMCID: PMC10886599 DOI: 10.3390/cancers16040810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 02/02/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
This comprehensive review critically examines the transformative impact of artificial intelligence (AI) and radiomics in the diagnosis, prognosis, and management of bladder, kidney, and prostate cancers. These cutting-edge technologies are revolutionizing the landscape of cancer care, enhancing both precision and personalization in medical treatments. Our review provides an in-depth analysis of the latest advancements in AI and radiomics, with a specific focus on their roles in urological oncology. We discuss how AI and radiomics have notably improved the accuracy of diagnosis and staging in bladder cancer, especially through advanced imaging techniques like multiparametric MRI (mpMRI) and CT scans. These tools are pivotal in assessing muscle invasiveness and pathological grades, critical elements in formulating treatment plans. In the realm of kidney cancer, AI and radiomics aid in distinguishing between renal cell carcinoma (RCC) subtypes and grades. The integration of radiogenomics offers a comprehensive view of disease biology, leading to tailored therapeutic approaches. Prostate cancer diagnosis and management have also seen substantial benefits from these technologies. AI-enhanced MRI has significantly improved tumor detection and localization, thereby aiding in more effective treatment planning. The review also addresses the challenges in integrating AI and radiomics into clinical practice, such as the need for standardization, ensuring data quality, and overcoming the "black box" nature of AI. We emphasize the importance of multicentric collaborations and extensive studies to enhance the applicability and generalizability of these technologies in diverse clinical settings. In conclusion, AI and radiomics represent a major paradigm shift in oncology, offering more precise, personalized, and patient-centric approaches to cancer care. While their potential to improve diagnostic accuracy, patient outcomes, and our understanding of cancer biology is profound, challenges in clinical integration and application persist. We advocate for continued research and development in AI and radiomics, underscoring the need to address existing limitations to fully leverage their capabilities in the field of oncology.
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Affiliation(s)
- Georgios Feretzakis
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece; (G.F.); (V.S.V.)
| | - Patrick Juliebø-Jones
- Department of Urology, Haukeland University Hospital, 5021 Bergen, Norway;
- Department of Clinical, Medicine University of Bergen, 5021 Bergen, Norway
- European Association of Urology, Young Academic Urologists, Urolithiasis Group, NL-6803 Arnhem, The Netherlands; (A.T.); (T.E.S.)
| | - Arman Tsaturyan
- European Association of Urology, Young Academic Urologists, Urolithiasis Group, NL-6803 Arnhem, The Netherlands; (A.T.); (T.E.S.)
- Department of Urology, Erebouni Medical Center, Yerevan 0087, Armenia
| | - Tarik Emre Sener
- European Association of Urology, Young Academic Urologists, Urolithiasis Group, NL-6803 Arnhem, The Netherlands; (A.T.); (T.E.S.)
- Department of Urology, Marmara University School of Medicine, Istanbul 34854, Turkey
| | - Vassilios S. Verykios
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece; (G.F.); (V.S.V.)
| | - Dimitrios Karapiperis
- School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece;
| | - Themistoklis Bellos
- Second Department of Urology, Sismanoglio Hospital, National and Kapodistrian University of Athens, 15126 Athens, Greece; (T.B.); (S.K.); (P.A.); (I.V.); (A.S.)
| | - Stamatios Katsimperis
- Second Department of Urology, Sismanoglio Hospital, National and Kapodistrian University of Athens, 15126 Athens, Greece; (T.B.); (S.K.); (P.A.); (I.V.); (A.S.)
| | - Panagiotis Angelopoulos
- Second Department of Urology, Sismanoglio Hospital, National and Kapodistrian University of Athens, 15126 Athens, Greece; (T.B.); (S.K.); (P.A.); (I.V.); (A.S.)
| | - Ioannis Varkarakis
- Second Department of Urology, Sismanoglio Hospital, National and Kapodistrian University of Athens, 15126 Athens, Greece; (T.B.); (S.K.); (P.A.); (I.V.); (A.S.)
| | - Andreas Skolarikos
- Second Department of Urology, Sismanoglio Hospital, National and Kapodistrian University of Athens, 15126 Athens, Greece; (T.B.); (S.K.); (P.A.); (I.V.); (A.S.)
| | - Bhaskar Somani
- Department of Urology, University of Southampton, Southampton SO17 1BJ, UK;
| | - Lazaros Tzelves
- European Association of Urology, Young Academic Urologists, Urolithiasis Group, NL-6803 Arnhem, The Netherlands; (A.T.); (T.E.S.)
- Second Department of Urology, Sismanoglio Hospital, National and Kapodistrian University of Athens, 15126 Athens, Greece; (T.B.); (S.K.); (P.A.); (I.V.); (A.S.)
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Yao Q, Zhang X, Wang Y, Wang C, Wei C, Chen J, Chen D. Comprehensive analysis of a tryptophan metabolism-related model in the prognostic prediction and immune status for clear cell renal carcinoma. Eur J Med Res 2024; 29:22. [PMID: 38183155 PMCID: PMC10768089 DOI: 10.1186/s40001-023-01619-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 12/24/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND Clear cell renal cell carcinoma (ccRCC) is characterized as one of the most common types of urological cancer with high degrees of malignancy and mortality. Due to the limited effectiveness of existing traditional therapeutic methods and poor prognosis, the treatment and therapy of advanced ccRCC patients remain challenging. Tryptophan metabolism has been widely investigated because it significantly participates in the malignant traits of multiple cancers. The functions and prognostic values of tryptophan metabolism-related genes (TMR) in ccRCC remain virtually obscure. METHODS We employed the expression levels of 40 TMR genes to identify the subtypes of ccRCC and explored the clinical characteristics, prognosis, immune features, and immunotherapy response in the subtypes. Then, a model was constructed for the prediction of prognosis based on the differentially expressed genes (DEGs) in the subtypes from the TCGA database and verified using the ICGC database. The prediction performance of this model was confirmed by the receiver operating characteristic (ROC) curves. The relationship of Risk Score with the infiltration of distinct tumor microenvironment cells, the expression profiles of immune checkpoint genes, and the treatment benefits of immunotherapy and chemotherapy drugs were also investigated. RESULTS The two subtypes revealed dramatic differences in terms of clinical characteristics, prognosis, immune features, and immunotherapy response. The constructed 6-gene-based model showed that the high Risk Score was significantly connected to poor overall survival (OS) and advanced tumor stages. Furthermore, increased expression of CYP1B1, KMO, and TDO2 was observed in ccRCC tissues at the translation levels, and an unfavorable prognosis for these patients was also found. CONCLUSION We identified 2 molecular subtypes of ccRCC based on the expression of TMR genes and constructed a prognosis-related model that may be used as a powerful tool to guide the prediction of ccRCC prognosis and personalized therapy. In addition, CYP1B1, KMO, and TDO2 can be regarded as the risk prognostic genes for ccRCC.
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Affiliation(s)
- Qinfan Yao
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Xiuyuan Zhang
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Yucheng Wang
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Cuili Wang
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Chunchun Wei
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Jianghua Chen
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
- Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province, Hangzhou, China.
- Institute of Nephropathy, Zhejiang University, Hangzhou, China.
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China.
| | - Dajin Chen
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
- Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province, Hangzhou, China.
- Institute of Nephropathy, Zhejiang University, Hangzhou, China.
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China.
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Schawkat K, Krajewski KM. Insights into Renal Cell Carcinoma with Novel Imaging Approaches. Hematol Oncol Clin North Am 2023; 37:863-875. [PMID: 37302934 DOI: 10.1016/j.hoc.2023.05.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
This article presents a comprehensive overview of new imaging approaches and techniques for improving the assessment of renal masses and renal cell carcinoma. The Bosniak classification, version 2019, as well as the clear cell likelihood score, version 2.0, will be discussed as new imaging algorithms using established techniques. Additionally, newer modalities, such as contrast-enhanced ultrasound, dual energy computed tomography, and molecular imaging, will be discussed in conjunction with emerging radiomics and artificial intelligence techniques. Current diagnostic algorithms combined with newer approaches may be an effective way to overcome existing limitations in renal mass and RCC characterization.
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Affiliation(s)
- Khoschy Schawkat
- Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Harvard Medical School
| | - Katherine M Krajewski
- Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Harvard Medical School; Dana-Farber Cancer Institute, 440 Brookline Avenue, Building MA Floor L1 Room 04AC, Boston, MA 02215, USA.
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Deniffel D, McAlpine K, Harder FN, Jain R, Lawson KA, Healy GM, Hui S, Zhang X, Salinas-Miranda E, van der Kwast T, Finelli A, Haider MA. Predicting the recurrence risk of renal cell carcinoma after nephrectomy: potential role of CT-radiomics for adjuvant treatment decisions. Eur Radiol 2023; 33:5840-5850. [PMID: 37074425 DOI: 10.1007/s00330-023-09551-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 01/09/2023] [Accepted: 02/12/2023] [Indexed: 04/20/2023]
Abstract
OBJECTIVES Previous trial results suggest that only a small number of patients with non-metastatic renal cell carcinoma (RCC) benefit from adjuvant therapy. We assessed whether the addition of CT-based radiomics to established clinico-pathological biomarkers improves recurrence risk prediction for adjuvant treatment decisions. METHODS This retrospective study included 453 patients with non-metastatic RCC undergoing nephrectomy. Cox models were trained to predict disease-free survival (DFS) using post-operative biomarkers (age, stage, tumor size and grade) with and without radiomics selected on pre-operative CT. Models were assessed using C-statistic, calibration, and decision curve analyses (repeated tenfold cross-validation). RESULTS At multivariable analysis, one of four selected radiomic features (wavelet-HHL_glcm_ClusterShade) was prognostic for DFS with an adjusted hazard ratio (HR) of 0.44 (p = 0.02), along with American Joint Committee on Cancer (AJCC) stage group (III versus I, HR 2.90; p = 0.002), grade 4 (versus grade 1, HR 8.90; p = 0.001), age (per 10 years HR 1.29; p = 0.03), and tumor size (per cm HR 1.13; p = 0.003). The discriminatory ability of the combined clinical-radiomic model (C = 0.80) was superior to that of the clinical model (C = 0.78; p < 0.001). Decision curve analysis revealed a net benefit of the combined model when used for adjuvant treatment decisions. At an exemplary threshold probability of ≥ 25% for disease recurrence within 5 years, using the combined versus the clinical model was equivalent to treating 9 additional patients (per 1000 assessed) who would recur without treatment (i.e., true-positive predictions) with no increase in false-positive predictions. CONCLUSION Adding CT-based radiomic features to established prognostic biomarkers improved post-operative recurrence risk assessment in our internal validation study and may help guide decisions regarding adjuvant therapy. KEY POINTS In patients with non-metastatic renal cell carcinoma undergoing nephrectomy, CT-based radiomics combined with established clinical and pathological biomarkers improved recurrence risk assessment. Compared to a clinical base model, the combined risk model enabled superior clinical utility if used to guide decisions on adjuvant treatment.
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Affiliation(s)
- Dominik Deniffel
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, Toronto, ON, M5G 1X5, Canada
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada
| | - Kristen McAlpine
- Division of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Felix N Harder
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, Toronto, ON, M5G 1X5, Canada
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada
| | - Rahi Jain
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Keith A Lawson
- Division of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Gerard M Healy
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, Toronto, ON, M5G 1X5, Canada
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada
- Department of Radiology, St Vincent's University Hospital, Dublin, Ireland
| | - Shirley Hui
- Division of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Xiaoyu Zhang
- Division of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Emmanuel Salinas-Miranda
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, Toronto, ON, M5G 1X5, Canada
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada
| | - Theodorus van der Kwast
- Department of Pathology, Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
| | - Antonio Finelli
- Division of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Masoom A Haider
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, Toronto, ON, M5G 1X5, Canada.
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada.
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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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