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Halle MK, Hodneland E, Wagner-Larsen KS, Lura NG, Fasmer KE, Berg HF, Stokowy T, Srivastava A, Forsse D, Hoivik EA, Woie K, Bertelsen BI, Krakstad C, Haldorsen IS. Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer. Sci Rep 2024; 14:11339. [PMID: 38760387 PMCID: PMC11101482 DOI: 10.1038/s41598-024-61271-4] [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: 02/23/2024] [Accepted: 05/03/2024] [Indexed: 05/19/2024] Open
Abstract
Cervical cancer (CC) is a major global health problem with 570,000 new cases and 266,000 deaths annually. Prognosis is poor for advanced stage disease, and few effective treatments exist. Preoperative diagnostic imaging is common in high-income countries and MRI measured tumor size routinely guides treatment allocation of cervical cancer patients. Recently, the role of MRI radiomics has been recognized. However, its potential to independently predict survival and treatment response requires further clarification. This retrospective cohort study demonstrates how non-invasive, preoperative, MRI radiomic profiling may improve prognostication and tailoring of treatments and follow-ups for cervical cancer patients. By unsupervised clustering based on 293 radiomic features from 132 patients, we identify three distinct clusters comprising patients with significantly different risk profiles, also when adjusting for FIGO stage and age. By linking their radiomic profiles to genomic alterations, we identify putative treatment targets for the different patient clusters (e.g., immunotherapy, CDK4/6 and YAP-TEAD inhibitors and p53 pathway targeting treatments).
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Affiliation(s)
- Mari Kyllesø Halle
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Erlend Hodneland
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Mathematics, University of Bergen, Bergen, Norway
| | - Kari S Wagner-Larsen
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Njål G Lura
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kristine E Fasmer
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Hege F Berg
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Tomasz Stokowy
- Genomics Core Facility, Department of Clinical Science, University of Bergen, Bergen, Norway
- Section of Bioinformatics, Clinical Laboratory, Haukeland University Hospital, Bergen, Norway
| | - Aashish Srivastava
- Genomics Core Facility, Department of Clinical Science, University of Bergen, Bergen, Norway
- Section of Bioinformatics, Clinical Laboratory, Haukeland University Hospital, Bergen, Norway
| | - David Forsse
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Erling A Hoivik
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Kathrine Woie
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Bjørn I Bertelsen
- Department of Pathology, Haukeland University Hospital, Bergen, Norway
| | - Camilla Krakstad
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
| | - Ingfrid S Haldorsen
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.
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Liu H, Cui Y, Chang C, Zhou Z, Zhang Y, Ma C, Yin Y, Wang R. Development and validation of a 18F-FDG PET/CT radiomics nomogram for predicting progression free survival in locally advanced cervical cancer: a retrospective multicenter study. BMC Cancer 2024; 24:150. [PMID: 38291351 PMCID: PMC10826285 DOI: 10.1186/s12885-024-11917-3] [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: 10/24/2023] [Accepted: 01/24/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND The existing staging system cannot meet the needs of accurate survival prediction. Accurate survival prediction for locally advanced cervical cancer (LACC) patients who have undergone concurrent radiochemotherapy (CCRT) can improve their treatment management. Thus, this present study aimed to develop and validate radiomics models based on pretreatment 18Fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT) images to accurately predict the prognosis in patients. METHODS The data from 190 consecutive patients with LACC who underwent pretreatment 18F-FDG PET-CT and CCRT at two cancer hospitals were retrospectively analyzed; 176 patients from the same hospital were randomly divided into training (n = 117) and internal validation (n = 50) cohorts. Clinical features were selected from the training cohort using univariate and multivariate Cox proportional hazards models; radiomic features were extracted from PET and CT images and filtered using least absolute shrinkage and selection operator and Cox proportional hazard regression. Three prediction models and a nomogram were then constructed using the previously selected clinical, CT and PET radiomics features. The external validation cohort that was used to validate the models included 23 patients with LACC from another cancer hospital. The predictive performance of the constructed models was evaluated using receiver operator characteristic curves, Kaplan Meier curves, and a nomogram. RESULTS In total, one clinical, one PET radiomics, and three CT radiomics features were significantly associated with progression-free survival in the training cohort. Across all three cohorts, the combined model displayed better efficacy and clinical utility than any of these parameters alone in predicting 3-year progression-free survival (area under curve: 0.661, 0.718, and 0.775; C-index: 0.698, 0.724, and 0.705, respectively) and 5-year progression-free survival (area under curve: 0.661, 0.711, and 0.767; C-index, 0.698, 0.722, and 0.676, respectively). On subsequent construction of a nomogram, the calibration curve demonstrated good agreement between actually observed and nomogram-predicted values. CONCLUSIONS In this study, a clinico-radiomics prediction model was developed and successfully validated using an independent external validation cohort. The nomogram incorporating radiomics and clinical features could be a useful clinical tool for the early and accurate assessment of long-term prognosis in patients with LACC patients who undergo concurrent chemoradiotherapy.
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Affiliation(s)
- Huiling Liu
- Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China
| | - Yongbin Cui
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
| | - Cheng Chang
- Department of Nuclear Medicine, Third Affiliated Hospital of Xinjiang Medical University, State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, China
| | - Zichun Zhou
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, China
| | - Yalin Zhang
- Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China
- Xinjiang Key Laboratory of Oncology, Urumqi, China
- Key Laboratory of Cancer Immunotherapy and Radiotherapy, Chinese Academy of Medical Sciences, Urumqi, China
| | - Changsheng Ma
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China.
| | - Ruozheng Wang
- Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China.
- Xinjiang Key Laboratory of Oncology, Urumqi, China.
- Key Laboratory of Cancer Immunotherapy and Radiotherapy, Chinese Academy of Medical Sciences, Urumqi, China.
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Bizzarri N, Russo L, Dolciami M, Zormpas-Petridis K, Boldrini L, Querleu D, Ferrandina G, Pedone Anchora L, Gui B, Sala E, Scambia G. Radiomics systematic review in cervical cancer: gynecological oncologists' perspective. Int J Gynecol Cancer 2023; 33:1522-1541. [PMID: 37714669 DOI: 10.1136/ijgc-2023-004589] [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: 09/17/2023] Open
Abstract
OBJECTIVE Radiomics is the process of extracting quantitative features from radiological images, and represents a relatively new field in gynecological cancers. Cervical cancer has been the most studied gynecological tumor for what concerns radiomics analysis. The aim of this study was to report on the clinical applications of radiomics combined and/or compared with clinical-pathological variables in patients with cervical cancer. METHODS A systematic review of the literature from inception to February 2023 was performed, including studies on cervical cancer analysing a predictive/prognostic radiomics model, which was combined and/or compared with a radiological or a clinical-pathological model. RESULTS A total of 57 of 334 (17.1%) screened studies met inclusion criteria. The majority of studies used magnetic resonance imaging (MRI), but positron emission tomography (PET)/computed tomography (CT) scan, CT scan, and ultrasound scan also underwent radiomics analysis. In apparent early-stage disease, the majority of studies (16/27, 59.3%) analysed the role of radiomics signature in predicting lymph node metastasis; six (22.2%) investigated the prediction of radiomics to detect lymphovascular space involvement, one (3.7%) investigated depth of stromal infiltration, and one investigated (3.7%) parametrial infiltration. Survival prediction was evaluated both in early-stage and locally advanced settings. No study focused on the application of radiomics in metastatic or recurrent disease. CONCLUSION Radiomics signatures were predictive of pathological and oncological outcomes, particularly if combined with clinical variables. These may be integrated in a model using different clinical-pathological and translational characteristics, with the aim to tailor and personalize the treatment of each patient with cervical cancer.
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Affiliation(s)
- Nicolò Bizzarri
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Luca Russo
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Miriam Dolciami
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Konstantinos Zormpas-Petridis
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Luca Boldrini
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Denis Querleu
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Gabriella Ferrandina
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luigi Pedone Anchora
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Benedetta Gui
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Evis Sala
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giovanni Scambia
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
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Wagner‐Larsen KS, Hodneland E, Fasmer KE, Lura N, Woie K, Bertelsen BI, Salvesen Ø, Halle MK, Smit N, Krakstad C, Haldorsen IS. MRI-based radiomic signatures for pretreatment prognostication in cervical cancer. Cancer Med 2023; 12:20251-20265. [PMID: 37840437 PMCID: PMC10652318 DOI: 10.1002/cam4.6526] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/16/2023] [Accepted: 08/31/2023] [Indexed: 10/17/2023] Open
Abstract
BACKGROUND Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC). PURPOSE To investigate whether pretreatment MRI-based radiomic signatures predict disease-specific survival (DSS) in CC. STUDY TYPE Retrospective. POPULATION CC patients (n = 133) allocated into training(T) (nT = 89)/validation(V) (nV = 44) cohorts. FIELD STRENGTH/SEQUENCE T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) at 1.5T or 3.0T. ASSESSMENT Radiomic features from segmented tumors were extracted from T2WI and DWI (high b-value DWI and apparent diffusion coefficient (ADC) maps). STATISTICAL TESTS Radiomic signatures for prediction of DSS from T2WI (T2rad ) and T2WI with DWI (T2 + DWIrad ) were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression. Area under time-dependent receiver operating characteristics curves (AUC) were used to evaluate and compare the prognostic performance of the radiomic signatures, MRI-derived maximum tumor size ≤/> 4 cm (MAXsize ), and 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I-II/III-IV). Survival was analyzed using Cox model estimating hazard ratios (HR) and Kaplan-Meier method with log-rank tests. RESULTS The radiomic signatures T2rad and T2 + DWIrad yielded AUCT /AUCV of 0.80/0.62 and 0.81/0.75, respectively, for predicting 5-year DSS. Both signatures yielded better or equal prognostic performance to that of MAXsize (AUCT /AUCV : 0.69/0.65) and FIGO (AUCT /AUCV : 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HRT /HRV for T2rad : 4.0/2.5 and T2 + DWIrad : 4.8/2.1). Adding T2rad and T2 + DWIrad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort(T) (AUCT 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWIrad tended to the same in cohort(V) (AUCV 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWIrad was significantly associated with reduced DSS in both cohorts. DATA CONCLUSION Radiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.
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Affiliation(s)
- Kari S. Wagner‐Larsen
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of RadiologyHaukeland University HospitalBergenNorway
- Section for Radiology, Department of Clinical MedicineUniversity of BergenBergenNorway
| | - Erlend Hodneland
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of RadiologyHaukeland University HospitalBergenNorway
- Department of MathematicsUniversity of BergenBergenNorway
| | - Kristine E. Fasmer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of RadiologyHaukeland University HospitalBergenNorway
- Section for Radiology, Department of Clinical MedicineUniversity of BergenBergenNorway
| | - Njål Lura
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of RadiologyHaukeland University HospitalBergenNorway
- Section for Radiology, Department of Clinical MedicineUniversity of BergenBergenNorway
| | - Kathrine Woie
- Department of Obstetrics and GynecologyHaukeland University HospitalBergenNorway
| | | | - Øyvind Salvesen
- Clinical Research Unit, Department of Clinical and Molecular MedicineNorwegian University of Science and TechnologyTrondheimNorway
| | - Mari K. Halle
- Department of Obstetrics and GynecologyHaukeland University HospitalBergenNorway
- Centre for Cancer Biomarkers (CCBIO), Department of Clinical ScienceUniversity of BergenBergenNorway
| | - Noeska Smit
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of RadiologyHaukeland University HospitalBergenNorway
- Department of InformaticsUniversity of BergenBergenNorway
| | - Camilla Krakstad
- Department of Obstetrics and GynecologyHaukeland University HospitalBergenNorway
- Centre for Cancer Biomarkers (CCBIO), Department of Clinical ScienceUniversity of BergenBergenNorway
| | - Ingfrid S. Haldorsen
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of RadiologyHaukeland University HospitalBergenNorway
- Section for Radiology, Department of Clinical MedicineUniversity of BergenBergenNorway
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Avesani G, Perazzolo A, Amerighi A, Celli V, Panico C, Sala E, Gui B. The Utility of Contrast-Enhanced Magnetic Resonance Imaging in Uterine Cervical Cancer: A Systematic Review. Life (Basel) 2023; 13:1368. [PMID: 37374150 DOI: 10.3390/life13061368] [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: 05/04/2023] [Revised: 06/03/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
Correct staging of cervical cancer is essential to establish the best therapeutic procedure and prognosis for the patient. MRI is the best imaging modality for local staging and follow-up. According to the latest ESUR guidelines, T2WI and DWI-MR sequences are fundamental in these settings, and CE-MRI remains optional. This systematic review, according to the PRISMA 2020 checklist, aims to give an overview of the literature regarding the use of contrast in MRI in cervical cancer and provide more specific indications of when it may be helpful. Systematic searches on PubMed and Web Of Science (WOS) were performed, and 97 papers were included; 1 paper was added considering the references of included articles. From our literature review, it emerged that many papers about the use of contrast in cervical cancer are dated, especially about staging and detection of tumor recurrence. We did not find strong evidence suggesting that CE-MRI is helpful in any clinical setting for cervical cancer staging and detection of tumor recurrence. There is growing evidence that perfusion parameters and perfusion-derived radiomics models might have a role as prognostic and predictive biomarkers, but the lack of standardization and validation limits their use in a research setting.
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Affiliation(s)
- Giacomo Avesani
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
| | - Alessio Perazzolo
- Department of Radiological and Hematological Sciences, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Andrea Amerighi
- Department of Radiological and Hematological Sciences, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Veronica Celli
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
| | - Camilla Panico
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
| | - Evis Sala
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
| | - Benedetta Gui
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
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