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Yang F, Feng Y, Sun P, Traverso A, Dekker A, Zhang B, Huang Z, Wang Z, Yan D. Preoperative prediction of high-grade osteosarcoma response to neoadjuvant therapy based on a plain CT radiomics model: A dual-center study. J Bone Oncol 2024; 47:100614. [PMID: 38975332 PMCID: PMC11225658 DOI: 10.1016/j.jbo.2024.100614] [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: 01/25/2024] [Revised: 05/05/2024] [Accepted: 06/01/2024] [Indexed: 07/09/2024] Open
Abstract
Objective To develop a model combining clinical and radiomics features from CT scans for a preoperative noninvasive evaluation of Huvos grading of neoadjuvant chemotherapy in patients with HOS. Methods 183 patients from center A and 42 from center B were categorized into training and validation sets. Features derived from radiomics were obtained from unenhanced CT scans.Following dimensionality reduction, the most optimal features were selected and utilized in creating a radiomics model through logistic regression analysis. Integrating clinical features, a composite clinical radiomics model was developed, and a nomogram was constructed. Predictive performance of the model was evaluated using ROC curves and calibration curves. Additionally, decision curve analysis was conducted to assess practical utility of nomogram in clinical settings. Results LASSO LR analysis was performed, and finally, three selected image omics features were obtained.Radiomics model yielded AUC values with a good diagnostic effect for both patient sets (AUCs: 0.69 and 0.68, respectively). Clinical models (including sex, age, pre-chemotherapy ALP and LDH levels, new lung metastases within 1 year after surgery, and incidence) performed well in terms of Huvos grade prediction, with an AUC of 0.74 for training set. The AUC for independent validation set stood at 0.70. Notably, the amalgamation of radiomics and clinical features exhibited commendable predictive prowess in training set, registering an AUC of 0.78. This robust performance was subsequently validated in the independent validation set, where the AUC remained high at 0.75. Calibration curves of nomogram showed that the predictions were in good agreement with actual observations. Conclusion Combined model can be used for Huvos grading in patients with HOS after preoperative chemotherapy, which is helpful for adjuvant treatment decisions.
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Affiliation(s)
- Fan Yang
- Department of Radiation, Beijing Jishuitan Hospital,Capital Medical University, Beijing 100035, China
| | - Ying Feng
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Pengfei Sun
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Bin Zhang
- Department of Radiation, Peking University Shougang Hospital, Beijing 100144, China
| | - Zhen Huang
- Department of Bone Oncology, Beijing Jishuitan Hospital,Capital Medical University, Beijing 100035, China
| | - Zhixiang Wang
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Dong Yan
- Department of Radiation, Beijing Jishuitan Hospital,Capital Medical University, Beijing 100035, China
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Yang Y, Wang J, Ren Q, Yu R, Yuan Z, Jiang Q, Guan S, Tang X, Duan T, Meng X. Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study. Abdom Radiol (NY) 2024; 49:2311-2324. [PMID: 38879708 DOI: 10.1007/s00261-024-04418-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 05/24/2024] [Accepted: 05/29/2024] [Indexed: 07/30/2024]
Abstract
PURPOSE To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data. MATERIALS AND METHODS In this retrospective study, the clinical and imaging data (CT and ultrasound) of patients with ccRCC confirmed by pathology from three tertiary hospitals in different regions were collected from January 2013 to January 2023. We developed three models, including a clinical model, a radiomics model, and a combined model. The performance of the model was determined based on its discriminative power and clinical utility. The evaluation indicators included area under the receiver operating characteristic curve (AUC) value, accuracy, sensitivity, specificity, negative predictive value, positive predictive value and decision curve analysis (DCA) curve. RESULTS A total of 251 patients were evaluated. Patients (n = 166) from Shandong University Qilu Hospital (Jinan) were divided into the training cohort, of which 50 patients developed metastases; patients (n = 37) from Shandong University Qilu Hospital (Qingdao) were used as internal testing, of which 15 patients developed metastases; patients (n = 48) from Changzhou Second People's Hospital were used as external testing, of which 13 patients developed metastases. In the training set, the combined model showed the highest performance (AUC, 0.924) in predicting lymph node metastasis (LNM), while the clinical and radiomics models both had AUCs of 0.845 and 0.870, respectively. In the internal testing, the combined model had the highest performance (AUC, 0.877) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.726 and 0.836, respectively. In the external testing, the combined model had the highest performance (AUC, 0.849) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.708 and 0.804, respectively. The DCA curve showed that the combined model had a significant prediction probability in predicting the risk of LNM in ccRCC patients compared with the clinical model or the radiomics model. CONCLUSION The combined model was superior to the clinical and radiomics models in predicting LNM in ccRCC patients.
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Affiliation(s)
- YouChang Yang
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China
| | - JiaJia Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - QingGuo Ren
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China
| | - Rong Yu
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - ZiYi Yuan
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - QingJun Jiang
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China
| | - Shuai Guan
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China
| | - XiaoQiang Tang
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - TongTong Duan
- Department of Ultrasound, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - XiangShui Meng
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China.
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Mendes Serrão E, Klug M, Moloney BM, Jhaveri A, Lo Gullo R, Pinker K, Luker G, Haider MA, Shinagare AB, Liu X. Current Status of Cancer Genomics and Imaging Phenotypes: What Radiologists Need to Know. Radiol Imaging Cancer 2023; 5:e220153. [PMID: 37921555 DOI: 10.1148/rycan.220153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Ongoing discoveries in cancer genomics and epigenomics have revolutionized clinical oncology and precision health care. This knowledge provides unprecedented insights into tumor biology and heterogeneity within a single tumor, among primary and metastatic lesions, and among patients with the same histologic type of cancer. Large-scale genomic sequencing studies also sparked the development of new tumor classifications, biomarkers, and targeted therapies. Because of the central role of imaging in cancer diagnosis and therapy, radiologists need to be familiar with the basic concepts of genomics, which are now becoming the new norm in oncologic clinical practice. By incorporating these concepts into clinical practice, radiologists can make their imaging interpretations more meaningful and specific, facilitate multidisciplinary clinical dialogue and interventions, and provide better patient-centric care. This review article highlights basic concepts of genomics and epigenomics, reviews the most common genetic alterations in cancer, and discusses the implications of these concepts on imaging by organ system in a case-based manner. This information will help stimulate new innovations in imaging research, accelerate the development and validation of new imaging biomarkers, and motivate efforts to bring new molecular and functional imaging methods to clinical radiology. Keywords: Oncology, Cancer Genomics, Epignomics, Radiogenomics, Imaging Markers Supplemental material is available for this article. © RSNA, 2023.
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Affiliation(s)
- Eva Mendes Serrão
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Maximiliano Klug
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Brian M Moloney
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Aaditeya Jhaveri
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Roberto Lo Gullo
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Katja Pinker
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Gary Luker
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Masoom A Haider
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Atul B Shinagare
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Xiaoyang Liu
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
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Badoiu SC, Greabu M, Miricescu D, Stanescu-Spinu II, Ilinca R, Balan DG, Balcangiu-Stroescu AE, Mihai DA, Vacaroiu IA, Stefani C, Jinga V. PI3K/AKT/mTOR Dysregulation and Reprogramming Metabolic Pathways in Renal Cancer: Crosstalk with the VHL/HIF Axis. Int J Mol Sci 2023; 24:8391. [PMID: 37176098 PMCID: PMC10179314 DOI: 10.3390/ijms24098391] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/26/2023] [Accepted: 05/04/2023] [Indexed: 05/15/2023] Open
Abstract
Renal cell carcinoma (RCC) represents 85-95% of kidney cancers and is the most frequent type of renal cancer in adult patients. It accounts for 3% of all cancer cases and is in 7th place among the most frequent histological types of cancer. Clear cell renal cell carcinoma (ccRCC), accounts for 75% of RCCs and has the most kidney cancer-related deaths. One-third of the patients with ccRCC develop metastases. Renal cancer presents cellular alterations in sugars, lipids, amino acids, and nucleic acid metabolism. RCC is characterized by several metabolic dysregulations including oxygen sensing (VHL/HIF pathway), glucose transporters (GLUT 1 and GLUT 4) energy sensing, and energy nutrient sensing cascade. Metabolic reprogramming represents an important characteristic of the cancer cells to survive in nutrient and oxygen-deprived environments, to proliferate and metastasize in different body sites. The phosphoinositide 3-kinase-AKT-mammalian target of the rapamycin (PI3K/AKT/mTOR) signaling pathway is usually dysregulated in various cancer types including renal cancer. This molecular pathway is frequently correlated with tumor growth and survival. The main aim of this review is to present renal cancer types, dysregulation of PI3K/AKT/mTOR signaling pathway members, crosstalk with VHL/HIF axis, and carbohydrates, lipids, and amino acid alterations.
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Affiliation(s)
- Silviu Constantin Badoiu
- Department of Anatomy and Embryology, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, 050474 Bucharest, Romania;
| | - Maria Greabu
- Department of Biochemistry, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, Sector 5, 050474 Bucharest, Romania;
| | - Daniela Miricescu
- Department of Biochemistry, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, Sector 5, 050474 Bucharest, Romania;
| | - Iulia-Ioana Stanescu-Spinu
- Department of Biochemistry, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, Sector 5, 050474 Bucharest, Romania;
| | - Radu Ilinca
- Department of Medical Informatics and Biostatistics, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, 050474 Bucharest, Romania;
| | - Daniela Gabriela Balan
- Department of Physiology, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, 050474 Bucharest, Romania; (D.G.B.); (A.-E.B.-S.)
| | - Andra-Elena Balcangiu-Stroescu
- Department of Physiology, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, 050474 Bucharest, Romania; (D.G.B.); (A.-E.B.-S.)
| | - Doina-Andrada Mihai
- Department of Diabetes, Nutrition and Metabolic Diseases, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, 050474 Bucharest, Romania;
| | - Ileana Adela Vacaroiu
- Department of Nephrology, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, 020021 Bucharest, Romania;
| | - Constantin Stefani
- Department of Family Medicine and Clinical Base, Dr. Carol Davila Central Military Emergency University Hospital, 134 Calea Plevnei, 010825 Bucharest, Romania;
| | - Viorel Jinga
- Department of Urology, “Prof. Dr. Theodor Burghele” Hospital, 050653 Bucharest, Romania
- “Prof. Dr. Theodor Burghele” Clinical Hospital, University of Medicine and Pharmacy Carol Davila, 050474 Bucharest, Romania
- Medical Sciences Section, Academy of Romanian Scientists, 050085 Bucharest, Romania
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8
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Varghese B, Cen S, Zahoor H, Siddiqui I, Aron M, Sali A, Rhie S, Lei X, Rivas M, Liu D, Hwang D, Quinn D, Desai M, Vaishampayan U, Gill I, Duddalwar V. Feasibility of using CT radiomic signatures for predicting CD8-T cell infiltration and PD-L1 expression in renal cell carcinoma. Eur J Radiol Open 2022; 9:100440. [PMID: 36090617 PMCID: PMC9460152 DOI: 10.1016/j.ejro.2022.100440] [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: 05/11/2022] [Revised: 08/25/2022] [Accepted: 08/30/2022] [Indexed: 01/26/2023] Open
Abstract
Objectives To identify computed tomography (CT)-based radiomic signatures of cluster of differentiation 8 (CD8)-T cell infiltration and programmed cell death ligand 1 (PD-L1) expression levels in patients with clear-cell renal cell carcinoma (ccRCC). Methods Seventy-eight patients with pathologically confirmed localized ccRCC, preoperative multiphase CT and tumor resection specimens were enrolled in this retrospective study. Regions of interest (ROI) of the ccRCC volume were manually segmented from the CT images and processed using a radiomics panel comprising of 1708 metrics. The extracted metrics were used as inputs to three machine learning classifiers: Random Forest, AdaBoost, and ElasticNet to create radiomic signatures for CD8-T cell infiltration and PD-L1 expression, respectively. Results Using a cut-off of 80 lymphocytes per high power field, 59 % were classified to CD8 highly infiltrated tumors and 41 % were CD8 non highly infiltrated tumors, respectively. An ElasticNet classifier discriminated between these two groups of CD8-T cells with an AUC of 0.68 (95 % CI, 0.55-0.80). In addition, based on tumor proportion score with a cut-off of > 1 % tumor cells expressing PD-L1, 76 % were PD-L1 positive and 24 % were PD-L1 negative. An Adaboost classifier discriminated between PD-L1 positive and PD-L1 negative tumors with an AUC of 0.8 95 % CI: (0.66, 0.95). 3D radiomics metrics of graylevel co-occurrence matrix (GLCM) and graylevel run-length matrix (GLRLM) metrics drove the performance for CD8-Tcell and PD-L1 classification, respectively. Conclusions CT-radiomic signatures can differentiate tumors with high CD8-T cell infiltration with moderate accuracy and positive PD-L1 expression with good accuracy in ccRCC.
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Affiliation(s)
- Bino Varghese
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA,Correspondence to: Keck Medical Center of USC, University of Southern California, Norris Topping Tower 4417, Los Angeles, CA 90033, USA.
| | - Steven Cen
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Haris Zahoor
- Keck School of Medicine, Department of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Imran Siddiqui
- Keck School of Medicine, Department of Pathology, University of Southern California, Los Angeles, CA, USA
| | - Manju Aron
- Keck School of Medicine, Department of Pathology, University of Southern California, Los Angeles, CA, USA
| | - Akash Sali
- Homi Bhabha Cancer Hospital, Department of Pathology, Sangrur, Punjab, India
| | - Suhn Rhie
- Keck School of Medicine, Department of Molecular Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xiaomeng Lei
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Marielena Rivas
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Derek Liu
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Darryl Hwang
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - David Quinn
- Keck School of Medicine, Department of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Mihir Desai
- Keck School of Medicine, Department of Urology, University of Southern California, Los Angeles, CA, USA
| | - Ulka Vaishampayan
- Rogel Cancer Center, Urologic Oncology Clinic, University of Michigan, Ann Arbor, MI, USA
| | - Inderbir Gill
- Keck School of Medicine, Department of Urology, University of Southern California, Los Angeles, CA, USA
| | - Vinay Duddalwar
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
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