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Deng J, Tu S, Li L, Li G, Zhang Y. Diagnostic, predictive and prognostic molecular biomarkers in clear cell renal cell carcinoma: A retrospective study. Cancer Rep (Hoboken) 2024; 7:e2116. [PMID: 38837683 PMCID: PMC11150078 DOI: 10.1002/cnr2.2116] [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] [Received: 10/28/2023] [Revised: 02/05/2024] [Accepted: 05/14/2024] [Indexed: 06/07/2024] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is a common and aggressive subtype of kidney cancer. Many patients are diagnosed at advanced stages, making early detection crucial. Unfortunately, there are currently no noninvasive tests for ccRCC, emphasizing the need for new biomarkers. Additionally, ccRCC often develops resistance to treatments like radiotherapy and chemotherapy. Identifying biomarkers that predict treatment outcomes is vital for personalized care. The integration of artificial intelligence (AI), multi-omics analysis, and computational biology holds promise in bolstering detection precision and resilience, opening avenues for future investigations. The amalgamation of radiogenomics and biomaterial-basedimmunomodulation signifies a revolutionary breakthrough in diagnostic medicine. This review summarizes existing literature and highlights emerging biomarkers that enhance diagnostic, predictive, and prognostic capabilities for ccRCC, setting the stage for future clinical research.
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Affiliation(s)
- Jian Deng
- Department of OncologyHejiang Hospital of Traditional Chinese MedicineLuzhouPeople's Republic of China
- School of Basic Medical SciencesSouthwest Medical UniversityLuzhouPeople's Republic of China
| | - ShengYuan Tu
- School of Basic Medical SciencesSouthwest Medical UniversityLuzhouPeople's Republic of China
| | - Lin Li
- School of StomatologySouthwest Medical UniversityLuzhouPeople's Republic of China
| | - GangLi Li
- Department of OncologyHejiang Hospital of Traditional Chinese MedicineLuzhouPeople's Republic of China
| | - YinHui Zhang
- Department of PharmacyThe Affiliated Hospital of Southwest Medical UniversityLuzhouPeople's Republic of China
- Department of AnesthesiologyHospital (T.C.M) Affiliated to Southwest Medical UniversityLuzhouPeople's Republic of China
- Department of PharmacyHejiang Hospital of Traditional Chinese MedicineLuzhouPeople's Republic of China
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Greco F, D’Andrea V, Beomonte Zobel B, Mallio CA. Radiogenomics and Texture Analysis to Detect von Hippel-Lindau (VHL) Mutation in Clear Cell Renal Cell Carcinoma. Curr Issues Mol Biol 2024; 46:3236-3250. [PMID: 38666933 PMCID: PMC11049152 DOI: 10.3390/cimb46040203] [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: 02/22/2024] [Revised: 03/24/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Radiogenomics, a burgeoning field in biomedical research, explores the correlation between imaging features and genomic data, aiming to link macroscopic manifestations with molecular characteristics. In this review, we examine existing radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the predominant renal cancer, and von Hippel-Lindau (VHL) gene mutation, the most frequent genetic mutation in ccRCC. A thorough examination of the literature was conducted through searches on the PubMed, Medline, Cochrane Library, Google Scholar, and Web of Science databases. Inclusion criteria encompassed articles published in English between 2014 and 2022, resulting in 10 articles meeting the criteria out of 39 initially retrieved articles. Most of these studies applied computed tomography (CT) images obtained from open source and institutional databases. This literature review investigates the role of radiogenomics, with and without texture analysis, in predicting VHL gene mutation in ccRCC patients. Radiogenomics leverages imaging modalities such as CT and magnetic resonance imaging (MRI), to analyze macroscopic features and establish connections with molecular elements, providing insights into tumor heterogeneity and biological behavior. The investigations explored diverse mutations, with a specific focus on VHL mutation, and applied CT imaging features for radiogenomic analysis. Moreover, radiomics and machine learning techniques were employed to predict VHL gene mutations based on CT features, demonstrating promising results. Additional studies delved into the relationship between VHL mutation and body composition, revealing significant associations with adipose tissue distribution. The review concludes by highlighting the potential role of radiogenomics in guiding targeted and selective therapies.
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Affiliation(s)
- Federico Greco
- Department of Radiology, Cittadella Della Salute Azienda Sanitaria Locale di Lecce, Piazza Filippo Bottazzi 2, 73100 Lecce, Italy
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (V.D.); (B.B.Z.); (C.A.M.)
| | - Valerio D’Andrea
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (V.D.); (B.B.Z.); (C.A.M.)
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Roma, Italy
| | - Bruno Beomonte Zobel
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (V.D.); (B.B.Z.); (C.A.M.)
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Roma, Italy
| | - Carlo Augusto Mallio
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (V.D.); (B.B.Z.); (C.A.M.)
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Roma, Italy
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Greco F, Panunzio A, Tafuri A, Bernetti C, Pagliarulo V, Zobel BB, Scardapane A, Mallio CA. CT-Based Radiogenomics of P4HA3 Expression in Clear Cell Renal Cell Carcinoma. Acad Radiol 2024; 31:902-908. [PMID: 37537130 DOI: 10.1016/j.acra.2023.07.003] [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] [Received: 05/30/2023] [Revised: 06/29/2023] [Accepted: 07/05/2023] [Indexed: 08/05/2023]
Abstract
RATIONALE AND OBJECTIVES The sequencing of the renal cell carcinoma (RCC) genome identified several mutations with prognostic significance. Genomic analysis, collected in The Cancer Genome Atlas Research Network, revealed several clear cell renal cell carcinoma (ccRCC) gene mutations and gene expressions. Radiogenomics is a new branch of diagnostic imaging based on the association between imaging phenotypes and genomics of diseases. P4HA3 expression has recently been shown to correlate with increased aggressiveness of ccRCC, with poor prognosis, proliferation, migration, invasion, and metastases, suggesting P4HA3 as a prognostic marker and therapeutic target in ccRCC. The aim of this study is to investigate the computed tomography (CT) imaging phenotype of P4HA3 expression in ccRCC patients. MATERIALS AND METHODS In this retrospective study we enrolled 196 ccRCC patients divided into two groups: ccRCC patients with P4HA3 expression (n = 13) and ccRCC patients without P4HA3 expression (n = 183). Several imaging features were evaluated on preoperative CT scan. The statistical significance threshold was set at P < .05. RESULTS A statistically significant association was found with larger primary tumor size (P = .033), tumor infiltration (P = .023), ill-defined tumor margins (P = .025), and advanced tumor stage American Joint Committee of Cancer (P = .014). CONCLUSION This study demonstrates CT imaging features associated with P4HA3 expression in ccRCC. These results could contribute to better understand P4HA3 expression with a noninvasive approach and could be applied to the development of targeted therapies.
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Affiliation(s)
- Federico Greco
- Department of Radiology, Cittadella della Salute Azienda Sanitaria Locale di Lecce, Piazza Filippo Bottazzi, 2, 73100 Lecce, Italy (F.G.).
| | - Andrea Panunzio
- Department of Urology, "Vito Fazzi" Hospital, Lecce, Italy (A.P., A.T., V.P.)
| | - Alessandro Tafuri
- Department of Urology, "Vito Fazzi" Hospital, Lecce, Italy (A.P., A.T., V.P.)
| | - Caterina Bernetti
- Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Roma, Italy (C.B., B.B.Z., C.A.M.); Fondazione Policlinico Universitario Campus Bio-Medico, Roma, Italy (C.B., B.B.Z., C.A.M.)
| | - Vincenzo Pagliarulo
- Department of Urology, "Vito Fazzi" Hospital, Lecce, Italy (A.P., A.T., V.P.)
| | - Bruno Beomonte Zobel
- Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Roma, Italy (C.B., B.B.Z., C.A.M.); Fondazione Policlinico Universitario Campus Bio-Medico, Roma, Italy (C.B., B.B.Z., C.A.M.)
| | - Arnaldo Scardapane
- Dipartimento Interdisciplinare di Medicina, Sezione di Diagnostica per immagini, Università degli Studi di Bari "Aldo Moro", Bari, Italy (A.S.)
| | - Carlo Augusto Mallio
- Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Roma, Italy (C.B., B.B.Z., C.A.M.); Fondazione Policlinico Universitario Campus Bio-Medico, Roma, Italy (C.B., B.B.Z., C.A.M.)
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Semenescu LE, Tataranu LG, Dricu A, Ciubotaru GV, Radoi MP, Rodriguez SMB, Kamel A. A Neurosurgical Perspective on Brain Metastases from Renal Cell Carcinoma: Multi-Institutional, Retrospective Analysis. Biomedicines 2023; 11:2485. [PMID: 37760926 PMCID: PMC10526360 DOI: 10.3390/biomedicines11092485] [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: 07/31/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND While acknowledging the generally poor prognostic features of brain metastases from renal cell carcinoma (BM RCC), it is important to be aware of the fact that neurosurgery still plays a vital role in managing this disease, even though we have entered an era of targeted therapies. Notwithstanding their initial high effectiveness, these agents often fail, as tumors develop resistance or relapse. METHODS The authors of this study aimed to evaluate patients presenting with BM RCC and their outcomes after being treated in the Neurosurgical Department of Clinical Emergency Hospital "Bagdasar-Arseni", and the Neurosurgical Department of the National Institute of Neurology and Neurovascular Diseases, Bucharest, Romania. The study is based on a thorough appraisal of the patient's demographic and clinicopathological data and is focused on the strategic role of neurosurgery in BM RCC. RESULTS A total of 24 patients were identified with BM RCC, of whom 91.6% had clear-cell RCC (ccRCC) and 37.5% had a prior nephrectomy. Only 29.1% of patients harbored extracranial metastases, while 83.3% had a single BM RCC. A total of 29.1% of patients were given systemic therapy. Neurosurgical resection of the BM was performed in 23 out of 24 patients. Survival rates were prolonged in patients who underwent nephrectomy, in patients who received systemic therapy, and in patients with a single BM RCC. Furthermore, higher levels of hemoglobin were associated in our study with a higher number of BMs. CONCLUSION Neurosurgery is still a cornerstone in the treatment of symptomatic BM RCC. Among the numerous advantages of neurosurgical intervention, the most important is represented by the quick reversal of neurological manifestations, which in most cases can be life-saving.
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Affiliation(s)
- Liliana Eleonora Semenescu
- Department of Biochemistry, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, Str. Petru Rares nr. 2–4, 710204 Craiova, Romania; (L.E.S.); (A.D.)
| | - Ligia Gabriela Tataranu
- Neurosurgical Department, Clinical Emergency Hospital “Bagdasar-Arseni”, Soseaua Berceni 12, 041915 Bucharest, Romania; (G.V.C.); (S.M.B.R.); (A.K.)
- Department of Neurosurgery, Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 020022 Bucharest, Romania
| | - Anica Dricu
- Department of Biochemistry, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, Str. Petru Rares nr. 2–4, 710204 Craiova, Romania; (L.E.S.); (A.D.)
| | - Gheorghe Vasile Ciubotaru
- Neurosurgical Department, Clinical Emergency Hospital “Bagdasar-Arseni”, Soseaua Berceni 12, 041915 Bucharest, Romania; (G.V.C.); (S.M.B.R.); (A.K.)
| | - Mugurel Petrinel Radoi
- Neurosurgical Department, National Institute of Neurology and Neurovascular Diseases, Soseaua Berceni 10, 041914 Bucharest, Romania;
| | - Silvia Mara Baez Rodriguez
- Neurosurgical Department, Clinical Emergency Hospital “Bagdasar-Arseni”, Soseaua Berceni 12, 041915 Bucharest, Romania; (G.V.C.); (S.M.B.R.); (A.K.)
| | - Amira Kamel
- Neurosurgical Department, Clinical Emergency Hospital “Bagdasar-Arseni”, Soseaua Berceni 12, 041915 Bucharest, Romania; (G.V.C.); (S.M.B.R.); (A.K.)
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Chartier S, Arif-Tiwari H. MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach. Cancers (Basel) 2023; 15:2799. [PMID: 37345136 DOI: 10.3390/cancers15102799] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/12/2023] [Accepted: 05/12/2023] [Indexed: 06/23/2023] Open
Abstract
Between 1983 and 2002, the incidence of solid renal tumors increased from 7.1 to 10.8 cases per 100,000. This is in large part due to the increase in the volume of ultrasound and cross-sectional imaging, although a majority of solid renal tumors are still found incidentally. Ultrasound and computed tomography (CT) have been the mainstay of renal mass screening and diagnosis but recent advances in magnetic resonance (MR) technology have made this the optimal choice when diagnosing and staging renal tumors. Our purpose in writing this review is to survey the modern MR imaging approach to benign and malignant solid renal tumors, consolidate the various imaging findings into an easy-to-read reference, and provide an imaging-based, algorithmic approach to renal mass characterization for clinicians. MR is at the forefront of renal mass characterization, surpassing ultrasound and CT in its ability to describe multiple tissue parameters and predict tumor biology. Cutting-edge MR protocols and the integration of diagnostic algorithms can improve patient outcomes, allowing the imager to narrow the differential and better guide oncologic and surgical management.
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Affiliation(s)
- Stephane Chartier
- Department of Medical Imaging, College of Medicine, The University of Arizona, Tucson, AZ 85724, USA
| | - Hina Arif-Tiwari
- Department of Medical Imaging, College of Medicine, The University of Arizona, Tucson, AZ 85724, USA
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Nolazco JI, Soerensen SJC, Chung BI. Biomarkers for the Detection and Surveillance of Renal Cancer. Urol Clin North Am 2023; 50:191-204. [PMID: 36948666 DOI: 10.1016/j.ucl.2023.01.009] [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] [Indexed: 02/22/2023]
Abstract
Renal cell carcinoma (RCC) is a heterogeneous disease characterized by a broad spectrum of disorders in terms of genetics, molecular and clinical characteristics. There is an urgent need for noninvasive tools to stratify and select patients for treatment accurately. In this review, we analyze serum, urinary, and imaging biomarkers that have the potential to detect malignant tumors in patients with RCC. We discuss the characteristics of these numerous biomarkers and their ability to be used routinely in clinical practice. The development of biomarkers continues to evolve with promising prospects.
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Affiliation(s)
- José Ignacio Nolazco
- Division of Urological Surgery, Brigham and Women's Hospital, Harvard Medical School, 45 Francis Street, Boston, MA 02115, USA; Servicio de Urología, Hospital Universitario Austral, Universidad Austral, Av Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina.
| | - Simon John Christoph Soerensen
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA; Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, USA
| | - Benjamin I Chung
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
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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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Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology. Semin Cancer Biol 2023; 91:1-15. [PMID: 36801447 DOI: 10.1016/j.semcancer.2023.02.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/30/2023] [Accepted: 02/15/2023] [Indexed: 02/21/2023]
Abstract
Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.
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Anari PY, Lay N, Gopal N, Chaurasia A, Samimi S, Harmon S, Firouzabadi FD, Merino MJ, Wakim P, Turkbey E, Jones EC, Ball MW, Turkbey B, Linehan WM, Malayeri AA. An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome. Abdom Radiol (NY) 2022; 47:3554-3562. [PMID: 35869307 PMCID: PMC10645140 DOI: 10.1007/s00261-022-03610-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/28/2022] [Accepted: 07/03/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE Upfront knowledge of tumor growth rates of clear cell renal cell carcinoma in von Hippel-Lindau syndrome (VHL) patients can allow for a more personalized approach to either surveillance imaging frequency or surgical planning. In this study, we implement a machine learning algorithm utilizing radiomic features of renal tumors identified on baseline magnetic resonance imaging (MRI) in VHL patients to predict the volumetric growth rate category of these tumors. MATERIALS AND METHODS A total of 73 VHL patients with 173 pathologically confirmed Clear Cell Renal Cell Carcinoma (ccRCCs) underwent MRI at least at two different time points between 2015 and 2021. Each tumor was manually segmented in excretory phase contrast T1 weighed MRI and co-registered on pre-contrast, corticomedullary and nephrographic phases. Radiomic features and volumetric data from each tumor were extracted using the PyRadiomics library in Python (4544 total features). Tumor doubling time (DT) was calculated and patients were divided into two groups: DT < = 1 year and DT > 1 year. Random forest classifier (RFC) was used to predict the DT category. To measure prediction performance, the cohort was randomly divided into 100 training and test sets (80% and 20%). Model performance was evaluated using area under curve of receiver operating characteristic curve (AUC-ROC), as well as accuracy, F1, precision and recall, reported as percentages with 95% confidence intervals (CIs). RESULTS The average age of patients was 47.2 ± 10.3 years. Mean interval between MRIs for each patient was 1.3 years. Tumors included in this study were categorized into 155 Grade 2; 16 Grade 3; and 2 Grade 4. Mean accuracy of RFC model was 79.0% [67.4-90.6] and mean AUC-ROC of 0.795 [0.608-0.988]. The accuracy for predicting DT classes was not different among the MRI sequences (P-value = 0.56). CONCLUSION Here we demonstrate the utility of machine learning in accurately predicting the renal tumor growth rate category of VHL patients based on radiomic features extracted from different T1-weighted pre- and post-contrast MRI sequences.
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Affiliation(s)
- Pouria Yazdian Anari
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Nathan Lay
- Artificial Intelligence Resource, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Nikhil Gopal
- Urologic Oncology Branch, Clinical Center, National Cancer Institute (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Aditi Chaurasia
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Safa Samimi
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Stephanie Harmon
- Artificial Intelligence Resource, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | | | - Maria J Merino
- Pathology Department, Clinical Center, National Cancer Institutes (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Paul Wakim
- Biostatistics and Clinical Epidemiology Service, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Evrim Turkbey
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Mark W Ball
- Urologic Oncology Branch, Clinical Center, National Cancer Institute (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Artificial Intelligence Resource, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - W Marston Linehan
- Urologic Oncology Branch, Clinical Center, National Cancer Institute (NCI), National Institutes of Health, Bldg. 10, Room 2 W-5940 and Room 1-5940, 10 Center Drive, Bethesda, MD, 20892, USA.
| | - Ashkan A Malayeri
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892-1109, USA.
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Deng PZ, Zhao BG, Huang XH, Xu TF, Chen ZJ, Wei QF, Liu XY, Guo YQ, Yuan SG, Liao WJ. Preoperative contrast-enhanced computed tomography-based radiomics model for overall survival prediction in hepatocellular carcinoma. World J Gastroenterol 2022; 28:4376-4389. [PMID: 36159012 PMCID: PMC9453776 DOI: 10.3748/wjg.v28.i31.4376] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/14/2022] [Accepted: 07/22/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with a rising incidence worldwide. The prognosis of HCC patients after radical resection remains poor. Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer, which can assist with cancer diagnosis, therapeutic decision-making and prognosis improvement.
AIM To develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival (OS) of HCC patients after radical hepatectomy.
METHODS A total of 150 HCC patients were randomly divided into a training cohort (n = 107) and a validation cohort (n = 43). Radiomics features were extracted from the entire tumour lesion. The least absolute shrinkage and selection operator algorithm was applied for the selection of radiomics features and the construction of the radiomics signature. Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors and develop the predictive nomogram, incorporating clinicopathological characteristics and the radiomics signature. The accuracy of the nomogram was assessed with the concordance index, receiver operating characteristic (ROC) curve and calibration curve. The clinical utility was evaluated by decision curve analysis (DCA). Kaplan–Meier methodology was used to compare the survival between the low- and high-risk subgroups.
RESULTS In total, seven radiomics features were selected to construct the radiomics signature. According to the results of univariate and multivariate Cox regression analyses, alpha-fetoprotein (AFP), neutrophil-to-lymphocyte ratio (NLR) and radiomics signature were included to build the nomogram. The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774, respectively. ROC curve analysis for predicting 1-, 3-, and 5-year OS confirmed satisfactory accuracy [training cohort, area under the curve (AUC) = 0.850, 0.791 and 0.823, respectively; validation cohort, AUC = 0.905, 0.884 and 0.911, respectively]. The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival. DCA curves suggested that the nomogram had more benefit than traditional staging system models. Kaplan–Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival (all P < 0.0001).
CONCLUSION The nomogram containing the radiomics signature, NLR and AFP is a reliable tool for predicting the OS of HCC patients.
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Affiliation(s)
- Peng-Zhan Deng
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Bi-Geng Zhao
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Xian-Hui Huang
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Ting-Feng Xu
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Zi-Jun Chen
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Qiu-Feng Wei
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Xiao-Yi Liu
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Yu-Qi Guo
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Sheng-Guang Yuan
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Wei-Jia Liao
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
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Updating Clear Cell Renal Cell Carcinoma (a Tribute to Prof. Ondrej Hes). Cancers (Basel) 2022; 14:cancers14163990. [PMID: 36010980 PMCID: PMC9406461 DOI: 10.3390/cancers14163990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 08/17/2022] [Indexed: 11/17/2022] Open
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Ming W, Zhu Y, Bai Y, Gu W, Li F, Hu Z, Xia T, Dai Z, Yu X, Li H, Gu Y, Yuan S, Zhang R, Li H, Zhu W, Ding J, Sun X, Liu Y, Liu H, Liu X. Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer. Front Oncol 2022; 12:943326. [PMID: 35965527 PMCID: PMC9366134 DOI: 10.3389/fonc.2022.943326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/29/2022] [Indexed: 11/17/2022] Open
Abstract
Background To investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively. Methods Two radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis. Results Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001). Conclusions Our results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.
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Affiliation(s)
- Wenlong Ming
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yanhui Zhu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yunfei Bai
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Wanjun Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine, School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China
| | - Fuyu Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Zixi Hu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Tiansong Xia
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zuolei Dai
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiafei Yu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Huamei Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yu Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Shaoxun Yuan
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Rongxin Zhang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Haitao Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Wenyong Zhu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Jianing Ding
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yun Liu
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Yun Liu, ; Hongde Liu, ; Xiaoan Liu,
| | - Hongde Liu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- *Correspondence: Yun Liu, ; Hongde Liu, ; Xiaoan Liu,
| | - Xiaoan Liu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Yun Liu, ; Hongde Liu, ; Xiaoan Liu,
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