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Lizano M, Carrillo-García A, De La Cruz-Hernández E, Castro-Muñoz LJ, Contreras-Paredes A. Promising predictive molecular biomarkers for cervical cancer (Review). Int J Mol Med 2024; 53:50. [PMID: 38606495 PMCID: PMC11090266 DOI: 10.3892/ijmm.2024.5374] [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: 10/10/2023] [Accepted: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
Cervical cancer (CC) constitutes a serious public health problem. Vaccination and screening programs have notably reduced the incidence of CC worldwide by >80%; however, the mortality rate in low‑income countries remains high. The staging of CC is a determining factor in therapeutic strategies: The clinical management of early stages of CC includes surgery and/or radiotherapy, whereas radiotherapy and/or concurrent chemotherapy are the recommended therapeutic strategies for locally advanced CC. The histopathological characteristics of tumors can effectively serve as prognostic markers of radiotherapy response; however, the efficacy rate of radiotherapy may significantly differ among cancer patients. Failure of radiotherapy is commonly associated with a higher risk of recurrence, persistence and metastasis; therefore, radioresistance remains the most important and unresolved clinical problem. This condition highlights the importance of precision medicine in searching for possible predictive biomarkers to timely identify patients at risk of treatment response failure and provide tailored therapeutic strategies according to genetic and epigenetic characteristics. The present review aimed to summarize the evidence that supports the role of several proteins, methylation markers and non‑coding RNAs as potential predictive biomarkers for CC.
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Affiliation(s)
- Marcela Lizano
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología, Universidad Nacional Autónoma de México, Mexico City 14080, Mexico
- Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Adela Carrillo-García
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología, Universidad Nacional Autónoma de México, Mexico City 14080, Mexico
| | - Erick De La Cruz-Hernández
- Laboratorio de Investigación en Enfermedades Metabólicas e Infecciosas, División Académica Multidisciplinaria de Comalcalco, Universidad Juárez Autónoma de Tabasco, Ranchería Sur Cuarta Sección, Comalcalco City, Tabasco 86650, Mexico
| | | | - Adriana Contreras-Paredes
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología, Universidad Nacional Autónoma de México, Mexico City 14080, Mexico
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Haider SP, Zeevi T, Sharaf K, Gross M, Mahajan A, Kann BH, Judson BL, Prasad ML, Burtness B, Aboian M, Canis M, Reichel CA, Baumeister P, Payabvash S. Impact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers. J Nucl Med 2024; 65:803-809. [PMID: 38514087 DOI: 10.2967/jnumed.123.266637] [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: 09/12/2023] [Revised: 02/13/2024] [Indexed: 03/23/2024] Open
Abstract
We aimed to investigate the effects of 18F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. Methods: We extracted 1,037 18F-FDG PET radiomic features quantifying the shape, intensity, and texture of 430 OPSCC primary tumors. The reproducibility of individual features across 3 intensity-normalized images (body-weight SUV, reference tissue activity ratio to lentiform nucleus of brain and cerebellum) and the raw PET data was assessed using an intraclass correlation coefficient (ICC). We investigated the effects of intensity normalization on the features' utility in predicting the human papillomavirus (HPV) status of OPSCCs in univariate logistic regression, receiver-operating-characteristic analysis, and extreme-gradient-boosting (XGBoost) machine-learning classifiers. Results: Of 1,037 features, a high (ICC ≥ 0.90), medium (0.90 > ICC ≥ 0.75), and low (ICC < 0.75) degree of reproducibility across normalization methods was attained in 356 (34.3%), 608 (58.6%), and 73 (7%) features, respectively. In univariate analysis, features from the PET normalized to the lentiform nucleus had the strongest association with HPV status, with 865 of 1,037 (83.4%) significant features after multiple testing corrections and a median area under the receiver-operating-characteristic curve (AUC) of 0.65 (interquartile range, 0.62-0.68). Similar tendencies were observed in XGBoost models, with the lentiform nucleus-normalized model achieving the numerically highest average AUC of 0.72 (SD, 0.07) in the cross validation within the training cohort. The model generalized well to the validation cohorts, attaining an AUC of 0.73 (95% CI, 0.60-0.85) in independent validation and 0.76 (95% CI, 0.58-0.95) in external validation. The AUCs of the XGBoost models were not significantly different. Conclusion: Only one third of the features demonstrated a high degree of reproducibility across intensity-normalization techniques, making uniform normalization a prerequisite for interindividual comparability of radiomic markers. The choice of normalization technique may affect the radiomic features' predictive value with respect to HPV. Our results show trends that normalization to the lentiform nucleus may improve model performance, although more evidence is needed to draw a firm conclusion.
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Affiliation(s)
- Stefan P Haider
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany;
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
| | - Tal Zeevi
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
| | - Kariem Sharaf
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany
| | - Moritz Gross
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
- Charité Center for Diagnostic and Interventional Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Amit Mahajan
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
| | - Benjamin H Kann
- Department of Radiation Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Benjamin L Judson
- Division of Otolaryngology, Yale School of Medicine, New Haven, Connecticut
| | - Manju L Prasad
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut; and
| | - Barbara Burtness
- Section of Medical Oncology, Yale School of Medicine, New Haven, Connecticut
| | - Mariam Aboian
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
| | - Martin Canis
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany
| | - Christoph A Reichel
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany
| | - Philipp Baumeister
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany
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3
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Niyoteka S, Seban RD, Rouhi R, Scarsbrook A, Genestie C, Classe M, Carré A, Sun R, La Greca Saint-Esteven A, Chargari C, McKenna J, McDermott G, Malinen E, Tanadini-Lang S, Guckenberger M, Guren MG, Lemanski C, Deutsch E, Robert C. A common [18F]-FDG PET radiomic signature to predict survival in patients with HPV-induced cancers. Eur J Nucl Med Mol Imaging 2023; 50:4010-4023. [PMID: 37632562 DOI: 10.1007/s00259-023-06320-2] [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: 12/22/2022] [Accepted: 06/24/2023] [Indexed: 08/28/2023]
Abstract
Locally advanced cervical cancer (LACC) and anal and oropharyngeal squamous cell carcinoma (ASCC and OPSCC) are mostly caused by oncogenic human papillomaviruses (HPV). In this paper, we developed machine learning (ML) models based on clinical, biological, and radiomic features extracted from pre-treatment fluorine-18-fluorodeoxyglucose positron emission tomography ([18F]-FDG PET) images to predict the survival of patients with HPV-induced cancers. For this purpose, cohorts from five institutions were used: two cohorts of patients treated for LACC including 104 patients from Gustave Roussy Campus Cancer (Center 1) and 90 patients from Leeds Teaching Hospitals NHS Trust (Center 2), two datasets of patients treated for ASCC composed of 66 patients from Institut du Cancer de Montpellier (Center 3) and 67 patients from Oslo University Hospital (Center 4), and one dataset of 45 OPSCC patients from the University Hospital of Zurich (Center 5). Radiomic features were extracted from baseline [18F]-FDG PET images. The ComBat technique was applied to mitigate intra-scanner variability. A modified consensus nested cross-validation for feature selection and hyperparameter tuning was applied on four ML models to predict progression-free survival (PFS) and overall survival (OS) using harmonized imaging features and/or clinical and biological variables as inputs. Each model was trained and optimized on Center 1 and Center 3 cohorts and tested on Center 2, Center 4, and Center 5 cohorts. The radiomic-based CoxNet model achieved C-index values of 0.75 and 0.78 for PFS and 0.76, 0.74, and 0.75 for OS on the test sets. Radiomic feature-based models had superior performance compared to the bioclinical ones, and combining radiomic and bioclinical variables did not improve the performances. Metabolic tumor volume (MTV)-based models obtained lower C-index values for a majority of the tested configurations but quite equivalent performance in terms of time-dependent AUCs (td-AUC). The results demonstrate the possibility of identifying common PET-based image signatures for predicting the response of patients with induced HPV pathology, validated on multi-center multiconstructor data.
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Affiliation(s)
- Stephane Niyoteka
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France.
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France.
| | - Romain-David Seban
- Department of Nuclear Medicine, Institut Curie, Saint Cloud, France
- Department of Nuclear Medicine, Gustave Roussy, 94805, Villejuif, France
| | - Rahimeh Rouhi
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France
| | - Andrew Scarsbrook
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| | | | - Marion Classe
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Pathology Department, Gustave Roussy, F-94805, Villejuif, France
| | - Alexandre Carré
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France
| | - Roger Sun
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France
| | | | - Cyrus Chargari
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France
| | - Jack McKenna
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Garry McDermott
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Eirik Malinen
- Department of Medical Physics, Oslo University Hospital, Oslo, Norway
| | | | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital of Zurich, Zurich, Switzerland
| | - Marianne G Guren
- Department of Oncology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Claire Lemanski
- Department of Radiation Oncology, Institut Régional du Cancer de Montpellier, Montpellier, France
| | - Eric Deutsch
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France
| | - Charlotte Robert
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France
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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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5
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Gao L, Yusufaly TI, Williamson CW, Mell LK. Optimized Atlas-Based Auto-Segmentation of Bony Structures from Whole-Body Computed Tomography. Pract Radiat Oncol 2023; 13:e442-e450. [PMID: 37030539 DOI: 10.1016/j.prro.2023.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 04/09/2023]
Abstract
PURPOSE To develop and test a method for fully automated segmentation of bony structures from whole-body computed tomography (CT) and evaluate its performance compared with manual segmentation. METHODS AND MATERIALS We developed a workflow for automatic whole-body bone segmentation using atlas-based segmentation (ABS) method with a postprocessing module (ABSPP) in MIM MAESTRO software. Fifty-two CT scans comprised the training set to build the atlas library, and 29 CT scans comprised the test set. To validate the workflow, we compared Dice similarity coefficient (DSC), mean distance to agreement, and relative volume errors between ABSPP and ABS with no postprocessing (ABSNPP) with manual segmentation as the reference (gold standard). RESULTS The ABSPP method resulted in significantly improved segmentation accuracy (DSC range, 0.85-0.98) compared with the ABSNPP method (DSC range, 0.55-0.87; P < .001). Mean distance to agreement results also indicated high agreement between ABSPP and manual reference delineations (range, 0.11-1.56 mm), which was significantly improved compared with ABSNPP (range, 1.00-2.34 mm) for the majority of tested bony structures. Relative volume errors were also significantly lower for ABSPP compared with ABSNPP for most bony structures. CONCLUSIONS We developed a fully automated MIM workflow for bony structure segmentation from whole-body CT, which exhibited high accuracy compared with manual delineation. The integrated postprocessing module significantly improved workflow performance.
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Affiliation(s)
- Lei Gao
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California
| | - Tahir I Yusufaly
- Russell H. Morgan Department of Radiology and Radiologic Sciences, Johns Hopkins University, School of Medicine, Baltimore, Maryland
| | - Casey W Williamson
- Department of Radiation Medicine, Oregon Health Sciences University, Portland, Oregon
| | - Loren K Mell
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California.
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Liu S, Zhou Y, Wang C, Shen J, Zheng Y. Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images. BMC Med Imaging 2023; 23:101. [PMID: 37528338 PMCID: PMC10392004 DOI: 10.1186/s12880-023-01059-6] [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: 01/18/2023] [Accepted: 07/19/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Lymph node metastasis is an important factor affecting the treatment and prognosis of patients with cervical cancer. However, the comparison of different algorithms and features to predict lymph node metastasis is not well understood. This study aimed to construct a non-invasive model for predicting lymph node metastasis in patients with cervical cancer based on clinical features combined with the radiomic features of magnetic resonance imaging (MRI) images. METHODS A total of 180 cervical cancer patients were divided into the training set (n = 126) and testing set (n = 54). In this cross-sectional study, radiomic features of MRI images and clinical features of patients were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to filter the features. Seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Logistic Regression, Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) are used to build the models. Receiver operating characteristics (ROC) curve and area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the performance of the models. RESULTS Of these 180 patients, 49 (27.22%) patients had lymph node metastases. Five of the 122 radiomic features and 3 clinical features were used to build predictive models. Compared with other models, the MNB model was the most robust, with its AUC, specificity, and accuracy on the testing set of 0.745 (95%CI: 0.740-0.750), 0.900 (95%CI: 0.807-0.993), and 0.778 (95%CI: 0.667-0.889), respectively. Furthermore, the AUCs of the MNB models with clinical features only, radiomic features only, and combined features were 0.698 (95%CI: 0.692-0.704), 0.632 (95%CI: 0.627-0.637), and 0.745 (95%CI: 0.740-0.750), respectively. CONCLUSION The MNB model, which combines the radiomic features of MRI images with the clinical features of the patient, can be used as a non-invasive tool for the preoperative assessment of lymph node metastasis.
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Affiliation(s)
- Shuyu Liu
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Bengbu Medical College, No.287 Changhuai Road, Longzihu District, Bengbu, Anhui, 233004, China
| | - Yu Zhou
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Bengbu Medical College, No.287 Changhuai Road, Longzihu District, Bengbu, Anhui, 233004, China
| | - Caizhi Wang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Bengbu Medical College, No.287 Changhuai Road, Longzihu District, Bengbu, Anhui, 233004, China
| | - Junjie Shen
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, 233004, China
| | - Yi Zheng
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Bengbu Medical College, No.287 Changhuai Road, Longzihu District, Bengbu, Anhui, 233004, China.
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Wang J, Mao Y, Gao X, Zhang Y. Recurrence risk stratification for locally advanced cervical cancer using multi-modality transformer network. Front Oncol 2023; 13:1100087. [PMID: 36874136 PMCID: PMC9978213 DOI: 10.3389/fonc.2023.1100087] [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: 11/16/2022] [Accepted: 02/01/2023] [Indexed: 02/18/2023] Open
Abstract
Objectives Recurrence risk evaluation is clinically significant for patients with locally advanced cervical cancer (LACC). We investigated the ability of transformer network in recurrence risk stratification of LACC based on computed tomography (CT) and magnetic resonance (MR) images. Methods A total of 104 patients with pathologically diagnosed LACC between July 2017 and December 2021 were enrolled in this study. All patients underwent CT and MR scanning, and their recurrence status was identified by the biopsy. We randomly divided patients into training cohort (48 cases, non-recurrence: recurrence = 37: 11), validation cohort (21 cases, non-recurrence: recurrence = 16: 5), and testing cohort (35 cases, non-recurrence: recurrence = 27: 8), upon which we extracted 1989, 882 and 315 patches for model's development, validation and evaluation, respectively. The transformer network consisted of three modality fusion modules to extract multi-modality and multi-scale information, and a fully-connected module to perform recurrence risk prediction. The model's prediction performance was assessed by six metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, f1-score, sensitivity, specificity and precision. Univariate analysis with F-test and T-test were conducted for statistical analysis. Results The proposed transformer network is superior to conventional radiomics methods and other deep learning networks in both training, validation and testing cohorts. Particularly, in testing cohort, the transformer network achieved the highest AUC of 0.819 ± 0.038, while four conventional radiomics methods and two deep learning networks got the AUCs of 0.680 ± 0.050, 0.720 ± 0.068, 0.777 ± 0.048, 0.691 ± 0.103, 0.743 ± 0.022 and 0.733 ± 0.027, respectively. Conclusions The multi-modality transformer network showed promising performance in recurrence risk stratification of LACC and may be used as an effective tool to help clinicians make clinical decisions.
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Affiliation(s)
- Jian Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Yixiao Mao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Xinna Gao
- Department of Radiation Oncology, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Yu Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
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8
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Ren K, Shen L, Qiu J, Sun K, Chen T, Xuan L, Yang M, She HY, Shen L, Zhu H, Deng L, Jing D, Shi L. Treatment planning computed tomography radiomics for predicting treatment outcomes and haematological toxicities in locally advanced cervical cancer treated with radiotherapy: A retrospective cohort study. BJOG 2022; 130:222-230. [PMID: 36056595 DOI: 10.1111/1471-0528.17285] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 08/08/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE We evaluated whether radiomic features extracted from planning computed tomography (CT) scans predict clinical end points in patients with locally advanced cervical cancer (LACC) undergoing intensity-modulated radiation therapy and brachytherapy. DESIGN A retrospective cohort study. SETTING Xiangya Hospital of Central South University, Changsha, Hunan, China. POPULATION Two hundred and fifty-seven LACC patients who were treated with intensity-modulated radiotherapy from 2014 to 2017. METHODS Patients were allocated into the training/validation sets (3:1 ratio) using proportional random sampling, resulting in the same proportion of groups in the two sets. We extracted 254 radiomic features from each of the gross target volume, pelvis and sacral vertebrae. The sequentially backward elimination support vector machine algorithm was used for feature selection and end point prediction. MAIN OUTCOMES AND MEASURES Clinical end points include tumour complete response (CR), 5-year overall survival (OS), anaemia, and leucopenia. RESULTS A combination of ten clinicopathological parameters and 34 radiomic features performed best for predicting CR (validation balanced accuracy: 80.8%). The validation balanced accuracy of 54 radiomic features was 85.8% for OS, and their scores can stratify patients into the low-risk and high-risk groups (5-year OS: 95.5% versus 36.4%, p < 0.001). The clinical and radiomic models were also predictive of anaemia and leucopenia (validation balanced accuracies: 71.0% and 69.9%). CONCLUSION This study demonstrated that combining clinicopathological parameters with CT-based radiomics may have value for predicting clinical end points in LACC. If validated, this model may guide therapeutic strategy to optimise the effectiveness and minimise toxicity or treatment for LACC.
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Affiliation(s)
- Kang Ren
- Department of Oncology, National Clinical Research Centre for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Lin Shen
- Department of Oncology, National Clinical Research Centre for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jianfeng Qiu
- Medical Science and Technology Innovation Centre, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Kui Sun
- Medical Science and Technology Innovation Centre, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Tingyin Chen
- Department of Network and Information Centre, Xiangya Hospital, Central South University, Changsha, China
| | - Long Xuan
- XiangYa School of Life Medicine, Central South University, Changsha, China
| | - Minwu Yang
- Xiangya School of Stomatology, Central South University, Changsha, China
| | - Hao-Yuan She
- School of Life Science, Central South University, Changsha, China
| | - Liangfang Shen
- Department of Oncology, National Clinical Research Centre for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hong Zhu
- Department of Oncology, National Clinical Research Centre for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Lan Deng
- Hunan Polytechnic of Environment and Biology, Hengyang, China
| | - Di Jing
- Department of Oncology, National Clinical Research Centre for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Liting Shi
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China
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Peng Z, Wang Y, Fan R, Gao K, Xie S, Wang F, Zhang J, Zhang H, He Y, Xie Z, Jiang W. Treatment of Recurrent Nasopharyngeal Carcinoma: A Sequential Challenge. Cancers (Basel) 2022; 14:cancers14174111. [PMID: 36077648 PMCID: PMC9454547 DOI: 10.3390/cancers14174111] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/19/2022] [Accepted: 08/19/2022] [Indexed: 11/23/2022] Open
Abstract
Simple Summary Recurrent nasopharyngeal carcinoma is one of the major causes of death among NPC patients. However, there are no international guidelines for the treatment of patients with recurrent NPC now. In this article, we summarize past publications on clinical research and mechanistic studies related to recurrent NPC, combined with the experience and lessons learned by our institutional multidisciplinary team in the treatment of recurrent NPC. We propose an objective protocol for the treatment of recurrent NPC. Abstract Recurrent nasopharyngeal carcinoma (NPC), which occurs in 10–20% of patients with primary NPC after the initial treatment modality of intensity-modulated radiation therapy (IMRT), is one of the major causes of death among NPC patients. Patients with recurrent disease without distant metastases still have a chance to be saved, but re-treatment often carries more serious toxicities or higher risks. For this group of patients, both otolaryngologists and oncologists are committed to developing more appropriate treatment regimens that can prolong patient survival and improve survival therapy. Currently, there are no international guidelines for the treatment of patients with recurrent NPC. In this article, we summarize past publications on clinical research and mechanistic studies related to recurrent NPC, combined with the experience and lessons learned by our institutional multidisciplinary team in the treatment of recurrent NPC. We propose an objective protocol for the treatment of recurrent NPC.
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Affiliation(s)
- Zhouying Peng
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yumin Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ruohao Fan
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Kelei Gao
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Shumin Xie
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Fengjun Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Junyi Zhang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Hua Zhang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yuxiang He
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhihai Xie
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
- Correspondence:
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10
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Yusufaly T, Mell L. Reply to 'Adding non-tumor radiomic features to the prognostic model is bothersome but useful'. J Nucl Med 2022; 63:495. [PMID: 35027373 DOI: 10.2967/jnumed.121.263730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 12/20/2021] [Accepted: 01/06/2022] [Indexed: 11/16/2022] Open
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11
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Zhang L, Zhang B. Adding non-tumor radiomic features to the prognostic model is bothersome but useful. J Nucl Med 2021; 63:494-495. [PMID: 34916244 DOI: 10.2967/jnumed.121.263479] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 11/03/2021] [Accepted: 11/16/2021] [Indexed: 11/16/2022] Open
Affiliation(s)
- Lu Zhang
- the First Affiliated Hospital of Jinan University, China
| | - Bin Zhang
- the First Affiliated Hospital of Jinan University, China
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