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Zhen SY, Wei Y, Song R, Liu XH, Li PR, Kong XY, Wei HY, Fan WH, Liang CH. Prediction of lymphovascular invasion of gastric cancer based on contrast-enhanced computed tomography radiomics. Front Oncol 2024; 14:1389278. [PMID: 39301548 PMCID: PMC11410566 DOI: 10.3389/fonc.2024.1389278] [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: 02/21/2024] [Accepted: 08/12/2024] [Indexed: 09/22/2024] Open
Abstract
Background Lymphovascular invasion (LVI) is a significant risk factor for lymph node metastasis in gastric cancer (GC) and is closely related to the prognosis and recurrence of GC. This study aimed to establish clinical models, radiomics models and combination models for the diagnosis of GC vascular invasion. Methods This study enrolled 146 patients with GC proved by pathology and who underwent radical resection of GC. The patients were assigned to the training and validation cohorts. A total of 1,702 radiomic features were extracted from contrast-enhanced computed tomography images of GC. Logistic regression analyses were performed to establish a clinical model, a radiomics model and a combined model. The performance of the predictive models was measured by the receiver operating characteristic (ROC) curve. Results In the training cohort, the age of LVI negative (-) patients and LVI positive (+) patients were 62.41 ± 8.41 and 63.76 ± 10.08 years, respectively, and there were more male (n = 63) than female (n = 19) patients in the LVI (+) group. Diameter and differentiation were the independent risk factors for determining LVI (-) and (+). A combined model was found to be relatively highly discriminative based on the area under the ROC curve for both the training (0.853, 95% CI: 0.784-0.920, sensitivity: 0.650 and specificity: 0.907) and the validation cohorts (0.742, 95% CI: 0.559-0.925, sensitivity: 0.736 and specificity: 0.700). Conclusions The combined model had the highest diagnostic effectiveness, and the nomogram established by this model had good performance. It can provide a reliable prediction method for individual treatment of LVI in GC before surgery.
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Affiliation(s)
- Si-Yu Zhen
- Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China
- Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management, Xinxiang, China
- Xinxiang Key Laboratory for Esophageal Cancer Imaging Diagnosis and Artificial Intelligence, Xinxiang, China
| | - Yong Wei
- Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China
| | - Ran Song
- Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China
| | - Xiao-Huan Liu
- Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China
| | - Pei-Ru Li
- Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China
| | - Xiang-Yan Kong
- Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China
| | - Han-Yu Wei
- Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China
| | - Wen-Hua Fan
- Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China
| | - Chang-Hua Liang
- Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China
- Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management, Xinxiang, China
- Xinxiang Key Laboratory for Esophageal Cancer Imaging Diagnosis and Artificial Intelligence, Xinxiang, China
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Rao Y, Ma Y, Wang J, Xiao W, Wu J, Shi L, Guo L, Fan L. Performance of radiomics in the differential diagnosis of parotid tumors: a systematic review. Front Oncol 2024; 14:1383323. [PMID: 39119093 PMCID: PMC11306159 DOI: 10.3389/fonc.2024.1383323] [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: 03/06/2024] [Accepted: 07/01/2024] [Indexed: 08/10/2024] Open
Abstract
Purpose A systematic review and meta-analysis were conducted to evaluate the diagnostic precision of radiomics in the differential diagnosis of parotid tumors, considering the increasing utilization of radiomics in tumor diagnosis. Although some researchers have attempted to apply radiomics in this context, there is ongoing debate regarding its accuracy. Methods Databases of PubMed, Cochrane, EMBASE, and Web of Science up to May 29, 2024 were systematically searched. The quality of included primary studies was assessed using the Radiomics Quality Score (RQS) checklist. The meta-analysis was performed utilizing a bivariate mixed-effects model. Results A total of 39 primary studies were incorporated. The machine learning model relying on MRI radiomics for diagnosis malignant tumors of the parotid gland, demonstrated a sensitivity of 0.80 [95% CI: 0.74, 0.86], SROC of 0.89 [95% CI: 0.27-0.99] in the validation set. The machine learning model based on MRI radiomics for diagnosis malignant tumors of the parotid gland, exhibited a sensitivity of 0.83[95% CI: 0.76, 0.88], SROC of 0.89 [95% CI: 0.17-1.00] in the validation set. The models also demonstrated high predictive accuracy for benign lesions. Conclusion There is great potential for radiomics-based models to improve the accuracy of diagnosing benign and malignant tumors of the parotid gland. To further enhance this potential, future studies should consider implementing standardized radiomics-based features, adopting more robust feature selection methods, and utilizing advanced model development tools. These measures can significantly improve the diagnostic accuracy of artificial intelligence algorithms in distinguishing between benign and malignant tumors of the parotid gland. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42023434931.
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Affiliation(s)
- Yilin Rao
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Yuxi Ma
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Jinghan Wang
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Weiwei Xiao
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Jiaqi Wu
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Liang Shi
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Ling Guo
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Liyuan Fan
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
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3
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Qiu E, Vejdani-Jahromi M, Kaliaev A, Fazelpour S, Goodman D, Ryoo I, Andreu-Arasa VC, Fujima N, Buch K, Sakai O. Fully automated 3D machine learning model for HPV status characterization in oropharyngeal squamous cell carcinomas based on CT images. Am J Otolaryngol 2024; 45:104357. [PMID: 38703612 DOI: 10.1016/j.amjoto.2024.104357] [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: 04/23/2024] [Accepted: 04/25/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND Human papillomavirus (HPV) status plays a major role in predicting oropharyngeal squamous cell carcinoma (OPSCC) survival. This study assesses the accuracy of a fully automated 3D convolutional neural network (CNN) in predicting HPV status using CT images. METHODS Pretreatment CT images from OPSCC patients were used to train a 3D DenseNet-121 model to predict HPV-p16 status. Performance was evaluated by the ROC Curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. RESULTS The network achieved a mean AUC of 0.80 ± 0.06. The best-preforming fold had a sensitivity of 0.86 and specificity of 0.92 at the Youden's index. The PPV, NPV, and F1 scores are 0.97, 0.71, and 0.82, respectively. CONCLUSIONS A fully automated CNN can characterize the HPV status of OPSCC patients with high sensitivity and specificity. Further refinement of this algorithm has the potential to provide a non-invasive tool to guide clinical management.
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Affiliation(s)
- Edwin Qiu
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States of America
| | - Maryam Vejdani-Jahromi
- Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States of America; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Artem Kaliaev
- Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States of America
| | - Sherwin Fazelpour
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States of America
| | - Deniz Goodman
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States of America
| | - Inseon Ryoo
- Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States of America; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America; Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - V Carlota Andreu-Arasa
- Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States of America; Department of Radiology, VA Boston Healthcare System, MA, United States of America
| | - Noriyuki Fujima
- Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States of America; Hokkaido University Hospital, Department of Diagnostic and Interventional Radiology, Sapporo, Japan
| | - Karen Buch
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Osamu Sakai
- Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States of America; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America; Department of Otolaryngology-Head and Neck Surgery, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States of America; Department of Radiation Oncology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, United States of America; Department of Radiology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, United States of America.
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4
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Fanizzi A, Comes MC, Bove S, Cavalera E, de Franco P, Di Rito A, Errico A, Lioce M, Pati F, Portaluri M, Saponaro C, Scognamillo G, Troiano I, Troiano M, Zito FA, Massafra R. Explainable prediction model for the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma using CNN on CT images. Sci Rep 2024; 14:14276. [PMID: 38902523 PMCID: PMC11189928 DOI: 10.1038/s41598-024-65240-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 06/18/2024] [Indexed: 06/22/2024] Open
Abstract
Several studies have emphasised how positive and negative human papillomavirus (HPV+ and HPV-, respectively) oropharyngeal squamous cell carcinoma (OPSCC) has distinct molecular profiles, tumor characteristics, and disease outcomes. Different radiomics-based prediction models have been proposed, by also using innovative techniques such as Convolutional Neural Networks (CNNs). Although some of these models reached encouraging predictive performances, there evidence explaining the role of radiomic features in achieving a specific outcome is scarce. In this paper, we propose some preliminary results related to an explainable CNN-based model to predict HPV status in OPSCC patients. We extracted the Gross Tumor Volume (GTV) of pre-treatment CT images related to 499 patients (356 HPV+ and 143 HPV-) included into the OPC-Radiomics public dataset to train an end-to-end Inception-V3 CNN architecture. We also collected a multicentric dataset consisting of 92 patients (43 HPV+ , 49 HPV-), which was employed as an independent test set. Finally, we applied Gradient-weighted Class Activation Mapping (Grad-CAM) technique to highlight the most informative areas with respect to the predicted outcome. The proposed model reached an AUC value of 73.50% on the independent test. As a result of the Grad-CAM algorithm, the most informative areas related to the correctly classified HPV+ patients were located into the intratumoral area. Conversely, the most important areas referred to the tumor edges. Finally, since the proposed model provided additional information with respect to the accuracy of the classification given by the visualization of the areas of greatest interest for predictive purposes for each case examined, it could contribute to increase confidence in using computer-based predictive models in the actual clinical practice.
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Affiliation(s)
- Annarita Fanizzi
- Laboratorio Biostatistica e Bioinformatica, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Bari, Italy
| | - Maria Colomba Comes
- Laboratorio Biostatistica e Bioinformatica, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Bari, Italy.
| | - Samantha Bove
- Laboratorio Biostatistica e Bioinformatica, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Bari, Italy.
| | - Elisa Cavalera
- Radiation Oncology Unit, Dipartimento di Oncoematologia, Ospedale Vito Fazzi, Lecce, Italy
| | - Paola de Franco
- Radiation Oncology Unit, Dipartimento di Oncoematologia, Ospedale Vito Fazzi, Lecce, Italy
| | | | - Angelo Errico
- Ospedale Monsignor Raffaele Dimiccoli, Barletta, Italy
| | - Marco Lioce
- Unità Operativa Complessa di Radioterpia, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Bari, Italy
| | | | | | - Concetta Saponaro
- Unità Operativa Complessi di Anatomia Patologia, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Bari, Italy
| | - Giovanni Scognamillo
- Unità Operativa Complessa di Radioterpia, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Bari, Italy
| | - Ippolito Troiano
- Radiation Oncology Department, Fondazione IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Italy
| | - Michele Troiano
- Radiation Oncology Department, Fondazione IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Italy
| | - Francesco Alfredo Zito
- Unità Operativa Complessi di Anatomia Patologia, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Bari, Italy
| | - Raffaella Massafra
- Laboratorio Biostatistica e Bioinformatica, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Bari, Italy
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5
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Sim Y, Kim M, Kim J, Lee SK, Han K, Sohn B. Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques. Eur Radiol 2024; 34:3102-3112. [PMID: 37848774 DOI: 10.1007/s00330-023-10338-3] [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: 08/29/2022] [Revised: 07/08/2023] [Accepted: 08/20/2023] [Indexed: 10/19/2023]
Abstract
OBJECTIVES To develop and validate a multiparametric MRI-based radiomics model with optimal oversampling and machine learning techniques for predicting human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC). METHODS This retrospective, multicenter study included consecutive patients with newly diagnosed and pathologically confirmed OPSCC between January 2017 and December 2020 (110 patients in the training set, 44 patients in the external validation set). A total of 293 radiomics features were extracted from three sequences (T2-weighted images [T2WI], contrast-enhanced T1-weighted images [CE-T1WI], and ADC). Combinations of three feature selection, five oversampling, and 12 machine learning techniques were evaluated to optimize its diagnostic performance. The area under the receiver operating characteristic curve (AUC) of the top five models was validated in the external validation set. RESULTS A total of 154 patients (59.2 ± 9.1 years; 132 men [85.7%]) were included, and oversampling was employed to account for data imbalance between HPV-positive and HPV-negative OPSCC (86.4% [133/154] vs. 13.6% [21/154]). For the ADC radiomics model, the combination of random oversampling and ridge showed the highest diagnostic performance in the external validation set (AUC, 0.791; 95% CI, 0.775-0.808). The ADC radiomics model showed a higher trend in diagnostic performance compared to the radiomics model using CE-T1WI (AUC, 0.604; 95% CI, 0.590-0.618), T2WI (AUC, 0.695; 95% CI, 0.673-0.717), and a combination of both (AUC, 0.642; 95% CI, 0.626-0.657). CONCLUSIONS The ADC radiomics model using random oversampling and ridge showed the highest diagnostic performance in predicting the HPV status of OPSCC in the external validation set. CLINICAL RELEVANCE STATEMENT Among multiple sequences, the ADC radiomics model has a potential for generalizability and applicability in clinical practice. Exploring multiple oversampling and machine learning techniques was a valuable strategy for optimizing radiomics model performance. KEY POINTS • Previous radiomics studies using multiparametric MRI were conducted at single centers without external validation and had unresolved data imbalances. • Among the ADC, CE-T1WI, and T2WI radiomics models and the ADC histogram models, the ADC radiomics model was the best-performing model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma. • The ADC radiomics model with the combination of random oversampling and ridge showed the highest diagnostic performance.
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Affiliation(s)
- Yongsik Sim
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Minjae Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Jinna Kim
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung-Koo Lee
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Beomseok Sohn
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
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Corti A, Cavalieri S, Calareso G, Mattavelli D, Ravanelli M, Poli T, Licitra L, Corino VDA, Mainardi L. MRI radiomics in head and neck cancer from reproducibility to combined approaches. Sci Rep 2024; 14:9451. [PMID: 38658630 PMCID: PMC11043398 DOI: 10.1038/s41598-024-60009-6] [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: 12/30/2023] [Accepted: 04/17/2024] [Indexed: 04/26/2024] Open
Abstract
The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.
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Affiliation(s)
- Anna Corti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy.
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Davide Mattavelli
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Marco Ravanelli
- Unit of Radiology, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Tito Poli
- Maxillo-Facial Surgery Division, Head and Neck Department, University Hospital of Parma, Parma, Italy
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Valentina D A Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy
- Cardiotech Lab, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy
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7
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Ammirabile A, Mastroleo F, Marvaso G, Alterio D, Franzese C, Scorsetti M, Franco P, Giannitto C, Jereczek-Fossa BA. Mapping the research landscape of HPV-positive oropharyngeal cancer: a bibliometric analysis. Crit Rev Oncol Hematol 2024; 196:104318. [PMID: 38431241 DOI: 10.1016/j.critrevonc.2024.104318] [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: 11/15/2023] [Revised: 02/25/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024] Open
Abstract
OBJECTIVE The aim of the study is to evaluate the scientific interest, the collaboration patterns and the emerging trends regarding HPV+ OPSCC diagnosis and treatment. MATERIALS AND METHODS A cross-sectional bibliometric analysis of articles reporting on HPV+ OPSCC within Scopus database was performed and all documents published up to December 31th, 2022 were eligible for analysis. Outcomes included the exploration of key characteristics (number of manuscripts published per year, growth rate, top productive countries, most highly cited papers, and the most well-represented journals), collaboration parameters (international collaboration ratio and networks, co-occurrence networks), keywords analysis (trend topics, factorial analysis). RESULTS A total of 5200 documents were found, published from March, 1987 to December, 2022. The number of publications increased annually with an average growth rate of 19.94%, reaching a peak of 680 documents published in 2021. The 10 most cited documents (range 1105-4645) were published from 2000 to 2012. The keywords factorial analysis revealed two main clusters: one on epidemiology, diagnosis, prevention and association with other HPV tumors; the other one about the therapeutic options. According to the frequency of keywords, new items are emerging in the last three years regarding the application of Artifical Intelligence (machine learning and radiomics) and the diagnostic biomarkers (circulating tumor DNA). CONCLUSIONS This bibliometric analysis highlights the importance of research efforts in prevention, diagnostics, and treatment strategies for this disease. Given the urgency of optimizing treatment and improving clinical outcomes, further clinical trials are needed to bridge unaddressed gaps in the management of HPV+ OPSCC patients.
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Affiliation(s)
- Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele 20090, Italy; Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Milan, Rozzano 20089, Italy
| | - Federico Mastroleo
- Department of Translational Medicine (DIMET), University of Eastern Piedmont and 'Maggiore della Carità' University Hospital, Novara, Italy; Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giulia Marvaso
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
| | - Daniela Alterio
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Ciro Franzese
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele 20090, Italy; Radiotherapy and Radiosurgery Department, IRCSS Humanitas Research Hospital, Milan, Rozzano, Italy
| | - Marta Scorsetti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele 20090, Italy; Radiotherapy and Radiosurgery Department, IRCSS Humanitas Research Hospital, Milan, Rozzano, Italy
| | - Pierfrancesco Franco
- Department of Translational Medicine (DIMET), University of Eastern Piedmont and 'Maggiore della Carità' University Hospital, Novara, Italy
| | - Caterina Giannitto
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele 20090, Italy; Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Milan, Rozzano 20089, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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8
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Ansari G, Mirza-Aghazadeh-Attari M, Mosier KM, Fakhry C, Yousem DM. Radiomics Features in Predicting Human Papillomavirus Status in Oropharyngeal Squamous Cell Carcinoma: A Systematic Review, Quality Appraisal, and Meta-Analysis. Diagnostics (Basel) 2024; 14:737. [PMID: 38611650 PMCID: PMC11011663 DOI: 10.3390/diagnostics14070737] [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: 12/20/2023] [Revised: 03/18/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024] Open
Abstract
We sought to determine the diagnostic accuracy of radiomics features in predicting HPV status in oropharyngeal squamous cell carcinoma (SCC) compared to routine paraclinical measures used in clinical practice. Twenty-six articles were included in the systematic review, and thirteen were used for the meta-analysis. The overall sensitivity of the included studies was 0.78, the overall specificity was 0.76, and the overall area under the ROC curve was 0.84. The diagnostic odds ratio (DOR) equaled 12 (8, 17). Subgroup analysis showed no significant difference between radiomics features extracted from CT or MR images. Overall, the studies were of low quality in regard to radiomics quality score, although most had a low risk of bias based on the QUADAS-2 tool. Radiomics features showed good overall sensitivity and specificity in determining HPV status in OPSCC, though the low quality of the included studies poses problems for generalizability.
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Affiliation(s)
- Golnoosh Ansari
- Department of Radiology, Northwestern Hospital, Northwestern School of Medicine, Chicago, IL 60611, USA;
| | - Mohammad Mirza-Aghazadeh-Attari
- Division of Interventional Radiology, Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Kristine M. Mosier
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Carole Fakhry
- Department of Otolaryngology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA;
| | - David M. Yousem
- Division of Neuroradiology, Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA;
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9
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Giannitto C, Carnicelli G, Lusi S, Ammirabile A, Casiraghi E, De Virgilio A, Esposito AA, Farina D, Ferreli F, Franzese C, Frigerio GM, Lo Casto A, Malvezzi L, Lorini L, Othman AE, Preda L, Scorsetti M, Bossi P, Mercante G, Spriano G, Balzarini L, Francone M. The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey. J Pers Med 2024; 14:341. [PMID: 38672968 PMCID: PMC11050769 DOI: 10.3390/jpm14040341] [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: 02/19/2024] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024] Open
Abstract
Artificial intelligence (AI) approaches have been introduced in various disciplines but remain rather unused in head and neck (H&N) cancers. This survey aimed to infer the current applications of and attitudes toward AI in the multidisciplinary care of H&N cancers. From November 2020 to June 2022, a web-based questionnaire examining the relationship between AI usage and professionals' demographics and attitudes was delivered to different professionals involved in H&N cancers through social media and mailing lists. A total of 139 professionals completed the questionnaire. Only 49.7% of the respondents reported having experience with AI. The most frequent AI users were radiologists (66.2%). Significant predictors of AI use were primary specialty (V = 0.455; p < 0.001), academic qualification and age. AI's potential was seen in the improvement of diagnostic accuracy (72%), surgical planning (64.7%), treatment selection (57.6%), risk assessment (50.4%) and the prediction of complications (45.3%). Among participants, 42.7% had significant concerns over AI use, with the most frequent being the 'loss of control' (27.6%) and 'diagnostic errors' (57.0%). This survey reveals limited engagement with AI in multidisciplinary H&N cancer care, highlighting the need for broader implementation and further studies to explore its acceptance and benefits.
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Affiliation(s)
- Caterina Giannitto
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giorgia Carnicelli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Stefano Lusi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Elena Casiraghi
- Department of Computer Science “Giovanni degli Antoni”, University of Milan, Via Celoria 18, 20133 Milan, Italy;
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 717 Potter Street, Berkeley, CA 94710, USA
| | - Armando De Virgilio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | | | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia ASST Spedali Civili of Brescia, 25123 Brescia, Italy;
| | - Fabio Ferreli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Ciro Franzese
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Radiotherapy and Radiosurgery IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Gian Marco Frigerio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Antonio Lo Casto
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), University Hospital of Palermo, 90127 Palermo, Italy;
| | - Luca Malvezzi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Luigi Lorini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Medical Oncology and Hematology Unit IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Ahmed E. Othman
- Department of Neuroradiology, University Medical Center Mainz, 55131 Mainz, Germany;
| | - Lorenzo Preda
- Radiology Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy;
| | - Marta Scorsetti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Radiotherapy and Radiosurgery IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Paolo Bossi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giuseppe Mercante
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giuseppe Spriano
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Luca Balzarini
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
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10
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Osapoetra LO, Dasgupta A, DiCenzo D, Fatima K, Quiaoit K, Saifuddin M, Karam I, Poon I, Husain Z, Tran WT, Sannachi L, Czarnota GJ. Quantitative US Delta Radiomics to Predict Radiation Response in Individuals with Head and Neck Squamous Cell Carcinoma. Radiol Imaging Cancer 2024; 6:e230029. [PMID: 38391311 PMCID: PMC10988345 DOI: 10.1148/rycan.230029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 11/24/2023] [Accepted: 01/17/2024] [Indexed: 02/24/2024]
Abstract
Purpose To investigate the role of quantitative US (QUS) radiomics data obtained after the 1st week of radiation therapy (RT) in predicting treatment response in individuals with head and neck squamous cell carcinoma (HNSCC). Materials and Methods This prospective study included 55 participants (21 with complete response [median age, 65 years {IQR: 47-80 years}, 20 male, one female; and 34 with incomplete response [median age, 59 years {IQR: 39-79 years}, 33 male, one female) with bulky node-positive HNSCC treated with curative-intent RT from January 2015 to October 2019. All participants received 70 Gy of radiation in 33-35 fractions over 6-7 weeks. US radiofrequency data from metastatic lymph nodes were acquired prior to and after 1 week of RT. QUS analysis resulted in five spectral maps from which mean values were extracted. We applied a gray-level co-occurrence matrix technique for textural analysis, leading to 20 QUS texture and 80 texture-derivative parameters. The response 3 months after RT was used as the end point. Model building and evaluation utilized nested leave-one-out cross-validation. Results Five delta (Δ) parameters had statistically significant differences (P < .05). The support vector machines classifier achieved a sensitivity of 71% (15 of 21), a specificity of 76% (26 of 34), a balanced accuracy of 74%, and an area under the receiver operating characteristic curve of 0.77 on the test set. For all the classifiers, the performance improved after the 1st week of treatment. Conclusion A QUS Δ-radiomics model using data obtained after the 1st week of RT from individuals with HNSCC predicted response 3 months after treatment completion with reasonable accuracy. Keywords: Computer-Aided Diagnosis (CAD), Ultrasound, Radiation Therapy/Oncology, Head/Neck, Radiomics, Quantitative US, Radiotherapy, Head and Neck Squamous Cell Carcinoma, Machine Learning Clinicaltrials.gov registration no. NCT03908684 Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
| | | | - Daniel DiCenzo
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Kashuf Fatima
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Karina Quiaoit
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Murtuza Saifuddin
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Irene Karam
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Ian Poon
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Zain Husain
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - William T. Tran
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Lakshmanan Sannachi
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Gregory J. Czarnota
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
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Liu J, Corti A, Calareso G, Spadarella G, Licitra L, Corino VDA, Mainardi L. Developing a robust two-step machine learning multiclassification pipeline to predict primary site in head and neck carcinoma from lymph nodes. Heliyon 2024; 10:e24377. [PMID: 38312621 PMCID: PMC10835257 DOI: 10.1016/j.heliyon.2024.e24377] [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: 08/01/2023] [Revised: 12/22/2023] [Accepted: 01/08/2024] [Indexed: 02/06/2024] Open
Abstract
This study aimed to develop a robust multiclassification pipeline to determine the primary tumor location in patients with head and neck carcinoma of unknown primary using radiomics and machine learning techniques. The dataset included 400 head and neck cancer patients with primary tumor in oropharynx, OPC (n = 162), nasopharynx, NPC (n = 137), oral cavity, OC (n = 63), larynx and hypopharynx, HL (n = 38). Two radiomic-based multiclassification pipelines (P1 and P2) were developed. P1 consisted in a direct identification of the primary sites, whereas P2 was based on a two-step approach: in the first step, the number of classes was reduced by merging the two minority classes which were reclassified in the second step. Diverse correlation thresholds (0.75, 0.80, 0.85), feature selection methods (sequential forwards/backwards selection, sequential floating forward selection, neighborhood component analysis and minimum redundancy maximum relevance), and classification models (neural network, decision tree, naïve Bayes, bagged trees and support vector machine) were assessed. P2 outperformed P1, with the best results obtained with the support vector machine classifier including radiomic and clinical features (accuracies of 75.3 % (HL), 75.4 % (OC), 71.3 % (OPC), 92.9 % (NPC)). These results indicate that the two-step multiclassification pipeline integrating radiomics and clinical information is a promising approach to predict the tumor site of unknown primary.
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Affiliation(s)
- Jiaying Liu
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Anna Corti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Gaia Spadarella
- Postgraduation School in Radiodiagnostics, University of Milan, Italy
- Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy
| | - Lisa Licitra
- Head and Neck Cancer Medical Oncology Department, Fondazione IRCCS Instituto Nazionale dei Tumori di Milano, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Italy
| | - Valentina D A Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Cardiotech Lab, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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12
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Jo KH, Kim J, Cho H, Kang WJ, Lee SK, Sohn B. 18F-FDG PET/CT Parameters Enhance MRI Radiomics for Predicting Human Papilloma Virus Status in Oropharyngeal Squamous Cell Carcinoma. Yonsei Med J 2023; 64:738-744. [PMID: 37992746 PMCID: PMC10681825 DOI: 10.3349/ymj.2023.0187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/26/2023] [Accepted: 08/17/2023] [Indexed: 11/24/2023] Open
Abstract
PURPOSE Predicting human papillomavirus (HPV) status is critical in oropharyngeal squamous cell carcinoma (OPSCC) radiomics. In this study, we developed a model for HPV status prediction using magnetic resonance imaging (MRI) radiomics and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) parameters in patients with OPSCC. MATERIALS AND METHODS Patients with OPSCC who underwent 18F-FDG PET/CT and contrast-enhanced MRI before treatment between January 2012 and February 2020 were enrolled. Training and test sets (3:2) were randomly selected. 18F-FDG PET/CT parameters and MRI radiomics feature were extracted. We developed three light-gradient boosting machine prediction models using the training set: Model 1, MRI radiomics features; Model 2, 18F-FDG PET/CT parameters; and Model 3, combination of MRI radiomics features and 18F-FDG PET/CT parameters. Area under the receiver operating characteristic curve (AUROC) values were used to analyze the performance of the models in predicting HPV status in the test set. RESULTS A total of 126 patients (118 male and 8 female; mean age: 60 years) were included. Of these, 103 patients (81.7%) were HPV-positive, and 23 patients (18.3%) were HPV-negative. AUROC values in the test set were 0.762 [95% confidence interval (CI), 0.564-0.959], 0.638 (95% CI, 0.404-0.871), and 0.823 (95% CI, 0.668-0.978) for Models 1, 2, and 3, respectively. The net reclassification improvement of Model 3, compared with that of Model 1, in the test set was 0.119. CONCLUSION When combined with an MRI radiomics model, 18F-FDG PET/CT exhibits incremental value in predicting HPV status in patients with OPSCC.
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Affiliation(s)
- Kwan Hyeong Jo
- Department of Nuclear Medicine, Korea University Guro Hospital, Seoul, Korea
| | - Jinna Kim
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hojin Cho
- Department of Nuclear Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Won Jun Kang
- Department of Nuclear Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Beomseok Sohn
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
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Fazelpour S, Vejdani-Jahromi M, Kaliaev A, Qiu E, Goodman D, Andreu-Arasa VC, Fujima N, Sakai O. Multiparametric machine learning algorithm for human papillomavirus status and survival prediction in oropharyngeal cancer patients. Head Neck 2023; 45:2882-2892. [PMID: 37740534 DOI: 10.1002/hed.27519] [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: 08/06/2023] [Accepted: 09/10/2023] [Indexed: 09/24/2023] Open
Abstract
BACKGROUND Human papillomavirus (HPV) status influences prognosis in oropharyngeal cancer (OPC). Identifying high-risk patients are critical to improving treatment. We aim to provide a noninvasive opportunity for managing OPC patients by training multiple machine learning pipelines to determine the best model for characterizing HPV status and survival. METHODS Multi-parametric algorithms were designed using a 492 OPC patient database. HPV status incorporated age, sex, smoking/drinking habits, cancer subsite, TNM, and AJCC 7th edition staging. Survival considered HPV model inputs plus HPV status. Patients were split 4:1 training: testing. Algorithm efficacy was assessed through accuracy and area under the receiver operator characteristic curve (AUC). RESULTS From 31 HPV status models, ensemble yielded 0.83 AUC and 78.7% accuracy. From 38 survival models, ensemble yielded 0.91 AUC and 87.7% accuracy. CONCLUSION Results reinforce artificial intelligence's potential to use tumor imaging and patient characterizations for HPV status and outcome prediction. Utilizing these algorithms can optimize clinical guidance and patient care noninvasively.
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Affiliation(s)
- Sherwin Fazelpour
- Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
| | - Maryam Vejdani-Jahromi
- Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
| | - Artem Kaliaev
- Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
| | - Edwin Qiu
- Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
| | - Deniz Goodman
- Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
| | - V Carlota Andreu-Arasa
- Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
- Department of Radiology, VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Noriyuki Fujima
- Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Osamu Sakai
- Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
- Department of Otolaryngology-Head and Neck Surgery, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
- Department of Radiation Oncology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
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Bicci E, Calamandrei L, Mungai F, Granata V, Fusco R, De Muzio F, Bonasera L, Miele V. Imaging of human papilloma virus (HPV) related oropharynx tumour: what we know to date. Infect Agent Cancer 2023; 18:58. [PMID: 37814320 PMCID: PMC10563217 DOI: 10.1186/s13027-023-00530-x] [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: 05/23/2023] [Accepted: 09/11/2023] [Indexed: 10/11/2023] Open
Abstract
The tumours of head and neck district are around 3% of all malignancies and squamous cell carcinoma is the most frequent histotype, with rapid increase during the last two decades because of the increment of the infection due to human papilloma virus (HPV). Even if the gold standard for the diagnosis is histological examination, including the detection of viral DNA and transcription products, imaging plays a fundamental role in the detection and staging of HPV + tumours, in order to assess the primary tumour, to establish the extent of disease and for follow-up. The main diagnostic tools are Computed Tomography (CT), Positron Emission Tomography-Computed Tomography (PET-CT) and Magnetic Resonance Imaging (MRI), but also Ultrasound (US) and the use of innovative techniques such as Radiomics have an important role. Aim of our review is to illustrate the main imaging features of HPV + tumours of the oropharynx, in US, CT and MRI imaging. In particular, we will outline the main limitations and strengths of the various imaging techniques, the main uses in the diagnosis, staging and follow-up of disease and the fundamental differential diagnoses of this type of tumour. Finally, we will focus on the innovative technique of texture analysis, which is increasingly gaining importance as a diagnostic tool in aid of the radiologist.
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Affiliation(s)
- Eleonora Bicci
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy.
| | - Leonardo Calamandrei
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
| | - Francesco Mungai
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, Naples, 80013, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, 20122, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, Campobasso, 86100, Italy
| | - Luigi Bonasera
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
| | - Vittorio Miele
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
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Wei L, Niraula D, Gates EDH, Fu J, Luo Y, Nyflot MJ, Bowen SR, El Naqa IM, Cui S. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. Br J Radiol 2023; 96:20230211. [PMID: 37660402 PMCID: PMC10546458 DOI: 10.1259/bjr.20230211] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/15/2023] [Accepted: 06/27/2023] [Indexed: 09/05/2023] Open
Abstract
Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making. In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Second, AI-based data fusion methods and modeling methods as well as different validation schemes are illustrated. Third, the applications and examples of multiomics research in oncology are demonstrated. Finally, the challenges regarding the heterogeneity data set, availability of omics data, and validation of the research are discussed. The transition of multiomics research to real clinics still requires consistent efforts in standardizing omics data collection and analysis, building computational infrastructure for data sharing and storing, developing advanced methods to improve data fusion and interpretability, and ultimately, conducting large-scale prospective clinical trials to fill the gap between study findings and clinical benefits.
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Affiliation(s)
- Lise Wei
- Department of Radiation Oncology, University of Michigan, Michigan, United States
| | - Dipesh Niraula
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Evan D. H. Gates
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Jie Fu
- Department of Radiation Oncology, Stanford University, Stanford, California, United States
| | - Yi Luo
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Matthew J. Nyflot
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Stephen R. Bowen
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Issam M El Naqa
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Sunan Cui
- Department of Radiation Oncology, University of Washington, Washington, United States
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16
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Song C, Chen X, Tang C, Xue P, Jiang Y, Qiao Y. Artificial intelligence for HPV status prediction based on disease-specific images in head and neck cancer: A systematic review and meta-analysis. J Med Virol 2023; 95:e29080. [PMID: 37691329 DOI: 10.1002/jmv.29080] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/14/2023] [Accepted: 08/03/2023] [Indexed: 09/12/2023]
Abstract
Accurate early detection of the human papillomavirus (HPV) status in head and neck cancer (HNC) is crucial to identify at-risk populations, stratify patients, personalized treatment options, and predict prognosis. Artificial intelligence (AI) is an emerging tool to dissect imaging features. This systematic review and meta-analysis aimed to evaluate the performance of AI to predict the HPV positivity through the HPV-associated diseased images in HNC patients. A systematic literature search was conducted in databases including Ovid-MEDLINE, Embase, and Web of Science Core Collection for studies continuously published from inception up to October 30, 2022. Search strategies included keywords such as "artificial intelligence," "head and neck cancer," "HPV," and "sensitivity & specificity." Duplicates, articles without HPV predictions, letters, scientific reports, conference abstracts, or reviews were excluded. Binary diagnostic data were then extracted to generate contingency tables and then used to calculate the pooled sensitivity (SE), specificity (SP), area under the curve (AUC), and their 95% confidence interval (CI). A random-effects model was used for meta-analysis, four subgroup analyses were further explored. Totally, 22 original studies were included in the systematic review, 15 of which were eligible to generate 33 contingency tables for meta-analysis. The pooled SE and SP for all studies were 79% (95% CI: 75-82%) and 74% (95% CI: 69-78%) respectively, with an AUC of 0.83 (95% CI: 0.79-0.86). When only selecting one contingency table with the highest accuracy from each study, our analysis revealed a pooled SE of 79% (95% CI: 75-83%), SP of 75% (95% CI: 69-79%), and an AUC of 0.84 (95% CI: 0.81-0.87). The respective heterogeneities were moderate (I2 for SE and SP were 51.70% and 51.01%) and only low (35.99% and 21.44%). This evidence-based study showed an acceptable and promising performance for AI algorithms to predict HPV status in HNC but was not comparable to the routine p16 immunohistochemistry. The exploitation and optimization of AI algorithms warrant further research. Compared with previous studies, future studies anticipate to make progress in the selection of databases, improvement of international reporting guidelines, and application of high-quality deep learning algorithms.
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Affiliation(s)
- Cheng Song
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xu Chen
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Tang
- Shenzhen Maternity & Child Healthcare Hospital, Shenzhen, China
| | - Peng Xue
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Youlin Qiao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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17
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Glogauer J, Kohanzadeh A, Feit A, Fournier JE, Zians A, Somogyi DZ. The Use of Radiomic Features to Predict Human Papillomavirus (HPV) Status in Head and Neck Tumors: A Review. Cureus 2023; 15:e44476. [PMID: 37664330 PMCID: PMC10472720 DOI: 10.7759/cureus.44476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2023] [Indexed: 09/05/2023] Open
Abstract
Head and neck cancers represent a significant source of morbidity and mortality across the world. The individual genetic makeup of each tumor can help to determine the course of treatment and can help clinicians predict prognosis. Non-invasive tools to determine the genetic status of these tumors, particularly p16 (human papillomavirus (HPV)) status could prove extremely valuable to treating clinicians and surgeons. The field of radiomics is a burgeoning area of radiology practice that aims to provide quantitative biomarkers that can be derived from radiological images and could prove useful in determining p16 status non-invasively. In this review, we summarize the current evidence for the use of radiomics to determine the HPV status of head and neck tumors. .
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Affiliation(s)
- Judah Glogauer
- Department of Pathology and Molecular Medicine, McMaster University, Waterloo, CAN
| | | | - Avery Feit
- Medical School, Albert Einstein College of Medicine, Bronx, USA
| | - Jeffrey E Fournier
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, CAN
| | - Avraham Zians
- Department of Diagnostic and Interventional Radiology, Montefiore Medical Center, Wakefield Campus, Bronx, USA
| | - Dafna Z Somogyi
- Department of Internal Medicine, Westchester Medical Center, Valhalla, USA
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18
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Corti A, De Cecco L, Cavalieri S, Lenoci D, Pistore F, Calareso G, Mattavelli D, de Graaf P, Leemans CR, Brakenhoff RH, Ravanelli M, Poli T, Licitra L, Corino V, Mainardi L. MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures. Biomark Res 2023; 11:69. [PMID: 37455307 DOI: 10.1186/s40364-023-00494-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/03/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND . At present, the prognostic prediction in advanced oral cavity squamous cell carcinoma (OCSCC) is based on the tumor-node-metastasis (TNM) staging system, and the most used imaging modality in these patients is magnetic resonance image (MRI). With the aim to improve the prediction, we developed an MRI-based radiomic signature as a prognostic marker for overall survival (OS) in OCSCC patients and compared it with published gene expression signatures for prognosis of OS in head and neck cancer patients, replicated herein on our OCSCC dataset. METHODS For each patient, 1072 radiomic features were extracted from T1 and T2-weighted MRI (T1w and T2w). Features selection was performed, and an optimal set of five of them was used to fit a Cox proportional hazard regression model for OS. The radiomic signature was developed on a multi-centric locally advanced OCSCC retrospective dataset (n = 123) and validated on a prospective cohort (n = 108). RESULTS The performance of the signature was evaluated in terms of C-index (0.68 (IQR 0.66-0.70)), hazard ratio (HR 2.64 (95% CI 1.62-4.31)), and high/low risk group stratification (log-rank p < 0.001, Kaplan-Meier curves). When tested on a multi-centric prospective cohort (n = 108), the signature had a C-index of 0.62 (IQR 0.58-0.64) and outperformed the clinical and pathologic TNM stage and six out of seven gene expression prognostic signatures. In addition, the significant difference of the radiomic signature between stages III and IVa/b in patients receiving surgery suggests a potential association of MRI features with the pathologic stage. CONCLUSIONS Overall, the present study suggests that MRI signatures, containing non-invasive and cost-effective remarkable information, could be exploited as prognostic tools.
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Affiliation(s)
- Anna Corti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Loris De Cecco
- Integrated Biology of Rare Tumors, Department of Research, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Deborah Lenoci
- Integrated Biology of Rare Tumors, Department of Research, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Federico Pistore
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Davide Mattavelli
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Pim de Graaf
- Amsterdam UMC location Vrije Universiteit, Radiology and Nuclear Medicine, de Boelelaan 1117, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - C René Leemans
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Amsterdam UMC location Vrije Universiteit, Otolaryngology-Head and Neck Surgery, de Boelelaan 1117, Amsterdam, The Netherlands
| | - Ruud H Brakenhoff
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Amsterdam UMC location Vrije Universiteit, Otolaryngology-Head and Neck Surgery, de Boelelaan 1117, Amsterdam, The Netherlands
| | - Marco Ravanelli
- Unit of Radiology, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Tito Poli
- Maxillo-Facial Surgery Division, Head and Neck Department, University Hospital of Parma, Parma, Italy
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Valentina Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Cardiotech Lab, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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19
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Bicci E, Nardi C, Calamandrei L, Barcali E, Pietragalla M, Calistri L, Desideri I, Mungai F, Bonasera L, Miele V. Magnetic resonance imaging in naso-oropharyngeal carcinoma: role of texture analysis in the assessment of response to radiochemotherapy, a preliminary study. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01653-2. [PMID: 37336860 DOI: 10.1007/s11547-023-01653-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 05/25/2023] [Indexed: 06/21/2023]
Abstract
OBJECTIVE Identifying MRI texture parameters able to distinguish inflammation, fibrosis, and residual cancer in patients with naso-oropharynx carcinoma after radiochemotherapy (RT-CHT). MATERIAL AND METHODS In this single-centre, observational, retrospective study, texture analysis was performed on ADC maps and post-gadolinium T1 images of patients with histological diagnosis of naso-oropharyngeal carcinoma treated with RT-CHT. An initial cohort of 99 patients was selected; 57 of them were later excluded. The final cohort of 42 patients was divided into 3 groups (inflammation, fibrosis, and residual cancer) according to MRI, 18F-FDG-PET/CT performed 3-4 months after RT-CHT, and biopsy. Pre-RT-CHT lesions and the corresponding anatomic area post-RT-CHT were segmented with 3D slicer software from which 107 textural features were derived. T-Student and Wilcoxon signed-rank tests were performed, and features with p-value < 0.01 were considered statistically significant. Cut-off values-obtained by ROC curves-to discriminate post-RT-CHT non-tumoural changes from residual cancer were calculated for the parameters statistically associated to the diseased status at follow-up. RESULTS Two features-Energy and Grey Level Non-Uniformity-were statistically significant on T1 images in the comparison between 'positive' (residual cancer) and 'negative' patients (inflammation and fibrosis). Energy was also found to be statistically significant in both patients with fibrosis and residual cancer. Grey Level Non-Uniformity was significant in the differentiation between residual cancer and inflammation. Five features were statistically significant on ADC maps in the differentiation between 'positive' and 'negative' patients. The reduction in values of such features between pre- and post-RT-CHT was correlated with a good response to therapy. CONCLUSIONS Texture analysis on post-gadolinium T1 images and ADC maps can differentiate residual cancer from fibrosis and inflammation in early follow-up of naso-oropharyngeal carcinoma treated with RT-CHT.
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Affiliation(s)
- Eleonora Bicci
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Cosimo Nardi
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Leonardo Calamandrei
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Eleonora Barcali
- Department of Information Engineering, University of Florence, 50139, Florence, Italy
| | - Michele Pietragalla
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Linda Calistri
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Isacco Desideri
- Radiation Oncology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Francesco Mungai
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Luigi Bonasera
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Vittorio Miele
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
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Lo Iacono F, Maragna R, Pontone G, Corino VDA. A robust radiomic-based machine learning approach to detect cardiac amyloidosis using cardiac computed tomography. FRONTIERS IN RADIOLOGY 2023; 3:1193046. [PMID: 37588665 PMCID: PMC10426499 DOI: 10.3389/fradi.2023.1193046] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/26/2023] [Indexed: 08/18/2023]
Abstract
Introduction Cardiac amyloidosis (CA) shares similar clinical and imaging characteristics (e.g., hypertrophic phenotype) with aortic stenosis (AS), but its prognosis is generally worse than severe AS alone. Recent studies suggest that the presence of CA is frequent (1 out of 8 patients) in patients with severe AS. The coexistence of the two diseases complicates the prognosis and therapeutic management of both conditions. Thus, there is an urgent need to standardize and optimize the diagnostic process of CA and AS. The aim of this study is to develop a robust and reliable radiomics-based pipeline to differentiate the two pathologies. Methods Thirty patients were included in the study, equally divided between CA and AS. For each patient, a cardiac computed tomography (CCT) was analyzed by extracting 107 radiomics features from the LV wall. Feature robustness was evaluated by means of geometrical transformations to the ROIs and intra-class correlation coefficient (ICC) computation. Various correlation thresholds (0.80, 0.85, 0.90, 0.95, 1), feature selection methods [p-value, least absolute shrinkage and selection operator (LASSO), semi-supervised LASSO, principal component analysis (PCA), semi-supervised PCA, sequential forwards selection] and machine learning classifiers (k-nearest neighbors, support vector machine, decision tree, logistic regression and gradient boosting) were assessed using a leave-one-out cross-validation. Data augmentation was performed using the synthetic minority oversampling technique. Finally, explainability analysis was performed by using the SHapley Additive exPlanations (SHAP) method. Results Ninety-two radiomic features were selected as robust and used in the further steps. Best performances of classification were obtained using a correlation threshold of 0.95, PCA (keeping 95% of the variance, corresponding to 9 PCs) and support vector machine classifier reaching an accuracy, sensitivity and specificity of 0.93. Four PCs were found to be mainly dependent on textural features, two on first-order statistics and three on shape and size features. Conclusion These preliminary results show that radiomics might be used as non-invasive tool able to differentiate CA from AS using clinical routine available images.
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Affiliation(s)
- Francesca Lo Iacono
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Riccardo Maragna
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Gianluca Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Valentina D. A. Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
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Leijenaar RTH, Walsh S, Aliboni L, Sanchez VL, Leech M, Joyce R, Gillham C, Kridelka F, Hustinx R, Danthine D, Occhipinti M, Vos W, Guiot J, Lambin P, Lovinfosse P. External validation of a radiomic signature to predict p16 (HPV) status from standard CT images of anal cancer patients. Sci Rep 2023; 13:7198. [PMID: 37137947 PMCID: PMC10156720 DOI: 10.1038/s41598-023-34162-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 04/25/2023] [Indexed: 05/05/2023] Open
Abstract
The paper deals with the evaluation of the performance of an existing and previously validated CT based radiomic signature, developed in oropharyngeal cancer to predict human papillomavirus (HPV) status, in the context of anal cancer. For the validation in anal cancer, a dataset of 59 patients coming from two different centers was collected. The primary endpoint was HPV status according to p16 immunohistochemistry. Predefined statistical tests were performed to evaluate the performance of the model. The AUC obtained here in anal cancer is 0.68 [95% CI (0.32-1.00)] with F1 score of 0.78. This signature is TRIPOD level 4 (57%) with an RQS of 61%. This study provides proof of concept that this radiomic signature has the potential to identify a clinically relevant molecular phenotype (i.e., the HPV-ness) across multiple cancers and demonstrates potential for this radiomic signature as a CT imaging biomarker of p16 status.
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Affiliation(s)
| | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | | | | | - Michelle Leech
- Applied Radiation Therapy, Discipline of Radiation Therapy, Trinity St. James's Cancer Institute, Trinity College, Dublin, Ireland
| | - Ronan Joyce
- Department of Radiation Oncology, St. Luke's Radiation Oncology Network and St James's Hospital, Dublin, Ireland
| | - Charles Gillham
- Department of Radiation Oncology, St. Luke's Radiation Oncology Network and St James's Hospital, Dublin, Ireland
| | - Frédéric Kridelka
- Department of Obstetrics and Gynecology, University Hospital of Liège, Liège, Belgium
| | - Roland Hustinx
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - Denis Danthine
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | | | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Julien Guiot
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Pierre Lovinfosse
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium.
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22
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External validation of an MR-based radiomic model predictive of locoregional control in oropharyngeal cancer. Eur Radiol 2023; 33:2850-2860. [PMID: 36460924 DOI: 10.1007/s00330-022-09255-8] [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: 12/24/2021] [Revised: 09/27/2022] [Accepted: 10/02/2022] [Indexed: 12/05/2022]
Abstract
OBJECTIVES To externally validate a pre-treatment MR-based radiomics model predictive of locoregional control in oropharyngeal squamous cell carcinoma (OPSCC) and to assess the impact of differences between datasets on the predictive performance. METHODS Radiomic features, as defined in our previously published radiomics model, were extracted from the primary tumor volumes of 157 OPSCC patients in a different institute. The developed radiomics model was validated using this cohort. Additionally, parameters influencing performance, such as patient subgroups, MRI acquisition, and post-processing steps on prediction performance will be investigated. For this analysis, matched subgroups (based on human papillomavirus (HPV) status of the tumor, T-stage, and tumor subsite) and a subgroup with only patients with 4-mm slice thickness were studied. Also the influence of harmonization techniques (ComBat harmonization, quantile normalization) and the impact of feature stability across observers and centers were studied. Model performances were assessed by area under the curve (AUC), sensitivity, and specificity. RESULTS Performance of the published model (AUC/sensitivity/specificity: 0.74/0.75/0.60) drops when applied on the validation cohort (AUC/sensitivity/specificity: 0.64/0.68/0.60). The performance of the full validation cohort improves slightly when the model is validated using a patient group with comparable HPV status of the tumor (AUC/sensitivity/specificity: 0.68/0.74/0.60), using patients acquired with a slice thickness of 4 mm (AUC/sensitivity/specificity: 0.67/0.73/0.57), or when quantile harmonization was performed (AUC/sensitivity/specificity: 0.66/0.69/0.60). CONCLUSION The previously published model shows its generalizability and can be applied on data acquired from different vendors and protocols. Harmonization techniques and subgroup definition influence performance of predictive radiomics models. KEY POINTS • Radiomics, a noninvasive quantitative image analysis technique, can support the radiologist by enhancing diagnostic accuracy and/or treatment decision-making. • A previously published model shows its generalizability and could be applied on data acquired from different vendors and protocols.
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Bologna M, Corino V, Cavalieri S, Calareso G, Gazzani SE, Poli T, Ravanelli M, Mattavelli D, de Graaf P, Nauta I, Scheckenbach K, Licitra L, Mainardi L. Prognostic radiomic signature for head and neck cancer: development and validation on a multi-centric MRI dataset. Radiother Oncol 2023; 183:109638. [PMID: 37004837 DOI: 10.1016/j.radonc.2023.109638] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 03/10/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023]
Abstract
BACKGROUND AND PURPOSE Prognosis in locally advanced head and neck cancer (HNC) is currently based on TNM staging system and tumor subsite. However, quantitative imaging features (i.e., radiomic features) from magnetic resonance imaging (MRI) may provide additional prognostic info. The aim of this work is to develop and validate an MRI-based prognostic radiomic signature for locally advanced HNC. MATERIALS AND METHODS Radiomic features were extracted from T1- and T2-weighted MRI (T1w and T2w) using the segmentation of the primary tumor as mask. In total 1072 features (536 per image type) were extracted for each tumor. A retrospective multi-centric dataset (n=285) was used for features selection and model training. The selected features were used to fit a Cox proportional hazard regression model for overall survival (OS) that outputs the radiomic signature. The signature was then validated on a prospective multi-centric dataset (n=234). Prognostic performance for OS and disease-free survival (DFS) was evaluated using C-index. Additional prognostic value of the radiomic signature was explored. RESULTS The radiomic signature had C-index=0.64 for OS and C-index=0.60 for DFS in the validation set. The addition of the radiomic signature to other clinical features (TNM staging and tumor subsite) increased prognostic ability for both OS (HPV- C-index 0.63 to 0.65; HPV+ C-index 0.75 to 0.80) and DFS (HPV- C-index 0.58 to 0.61; HPV+ C-index 0.64 to 0.65). CONCLUSION An MRI-based prognostic radiomic signature was developed and prospectively validated. Such signature can successfully integrate clinical factors in both HPV+ and HPV- tumors. Grant support: European Union Horizon 2020 Framework Programme, Grant/Award, Number: 689715.
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Affiliation(s)
- Marco Bologna
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy.
| | - Valentina Corino
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano and Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Silvia Eleonora Gazzani
- Unit of Diagnostic and Interventional Radiology, Department of Surgical Sciences, University of Parma, Parma, Italy
| | - Tito Poli
- Unit of Maxillo-Facial Surgery, Department of Biomedical, Biotechnological and Translational Sciences (S.Bi.Bi.T.), University of Parma, Parma, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Davide Mattavelli
- Department of Otorhinolaryngology Head and Neck Surgery, Spedali Civili di Brescia and University of Brescia, Brescia, Italy
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Irene Nauta
- Department of Otolaryngology/Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, The Netherlands
| | - Kathrin Scheckenbach
- Department of Otolaryngology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano and Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan. Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
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Caprini E, D'Agnese G, Brennan PA, Rahimi S. Human papilomaviru-related oropharyngeal squamous cell carcinoma and radiomics: A new era? J Oral Pathol Med 2023; 52:300-304. [PMID: 36847112 DOI: 10.1111/jop.13419] [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: 10/12/2022] [Accepted: 02/21/2023] [Indexed: 03/01/2023]
Abstract
BACKGROUND The increase of the incidence of human papillomavirus dependent oropharyngeal squamous cell carcinoma is alarming, although we have greatly progressed in the classification and staging of this disease. We now know that human papillomavirus related oropharyngeal squamous cell carcinoma is a sub-type of head and neck squamous cell carcinoma with favourable prognosis and good response to therapy that needs a proper system of classification and staging. Thus, in routine practice it is essential to test patients for the presence of human papillomavirus. The most popular technique to assess human papillomavirus status is immunohistochemistry on biopsy samples with p16, which is an excellent surrogate for high-risk human papillomavirus infection. Another highly sensitive and specific tissue-based technique for the detection of human papillomavirus is RNAscope In situ hybridization that has a prohibitive cost, limiting its use in routine practice. Radiomics is an artificial intelligence based non-invasive method of computational analysis of computed tomography, magnetic resonance imaging, positron emission tomography, and ultrasound images. METHODS In this review, we summarise the last findings of radiomics applied to human papillomavirus associated oropharyngeal squamous cell carcinoma. RESULTS A growing body of evidence suggest that radiomics is able to characterise and detect early relapse after treatment, and enable development of tailored therapy of human papillomavirus positive oropharyngeal squamous cell carcinoma.
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Affiliation(s)
- Elisabetta Caprini
- Anatomia Patologica, Istituto Dermopatico dell'Immacolata IRCCS, Rome, Italy
| | - Giampaolo D'Agnese
- Information Technology, Istituto Dermopatico dell'immacolata IDI-IRCCS, Rome, Italy
| | - Peter A Brennan
- Department of Oral and Maxillofacial Surgery, Queen Alexandra Hospital, Portsmouth, UK
| | - Siavash Rahimi
- Anatomia Patologica, Istituto Dermopatico dell'Immacolata IRCCS, Rome, Italy
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Radiomics Applications in Head and Neck Tumor Imaging: A Narrative Review. Cancers (Basel) 2023; 15:cancers15041174. [PMID: 36831517 PMCID: PMC9954362 DOI: 10.3390/cancers15041174] [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: 12/21/2022] [Revised: 01/31/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
Recent advances in machine learning and artificial intelligence technology have ensured automated evaluation of medical images. As a result, quantifiable diagnostic and prognostic biomarkers have been created. We discuss radiomics applications for the head and neck region in this paper. Molecular characterization, categorization, prognosis and therapy recommendation are given special consideration. In a narrative manner, we outline the fundamental technological principles, the overall idea and usual workflow of radiomic analysis and what seem to be the present and potential challenges in normal clinical practice. Clinical oncology intends for all of this to ensure informed decision support for personalized and useful cancer treatment. Head and neck cancers present a unique set of diagnostic and therapeutic challenges. These challenges are brought on by the complicated anatomy and heterogeneity of the area under investigation. Radiomics has the potential to address these barriers. Future research must be interdisciplinary and focus on the study of certain oncologic functions and outcomes, with external validation and multi-institutional cooperation in order to achieve this.
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Boot PA, Mes SW, de Bloeme CM, Martens RM, Leemans CR, Boellaard R, van de Wiel MA, de Graaf P. Magnetic resonance imaging based radiomics prediction of Human Papillomavirus infection status and overall survival in oropharyngeal squamous cell carcinoma. Oral Oncol 2023; 137:106307. [PMID: 36657208 DOI: 10.1016/j.oraloncology.2023.106307] [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: 10/22/2022] [Revised: 12/28/2022] [Accepted: 01/08/2023] [Indexed: 01/19/2023]
Abstract
OBJECTIVES Human papillomavirus- (HPV) positive oropharyngeal squamous cell carcinoma (OPSCC) differs biologically and clinically from HPV-negative OPSCC and has a better prognosis. This study aims to analyze the value of magnetic resonance imaging (MRI)-based radiomics in predicting HPV status in OPSCC and aims to develop a prognostic model in OPSCC including HPV status and MRI-based radiomics. MATERIALS AND METHODS Manual delineation of 249 primary OPSCCs (91 HPV-positive and 159 HPV-negative) on pretreatment native T1-weighted MRIs was performed and used to extract 498 radiomic features per delineation. A logistic regression (LR) and random forest (RF) model were developed using univariate feature selection. Additionally, factor analysis was performed, and the derived factors were combined with clinical data in a predictive model to assess the performance on predicting HPV status. Additionally, factors were combined with clinical parameters in a multivariable survival regression analysis. RESULTS Both feature-based LR and RF models performed with an AUC of 0.79 in prediction of HPV status. Fourteen of the twenty most significant features were similar in both models, mainly concerning tumor sphericity, intensity variation, compactness, and tumor diameter. The model combining clinical data and radiomic factors (AUC = 0.89) outperformed the radiomics-only model in predicting OPSCC HPV status. Overall survival prediction was most accurate using the combination of clinical parameters and radiomic factors (C-index = 0.72). CONCLUSION Predictive models based on MR-radiomic features were able to predict HPV status with sufficient performance, supporting the role of MRI-based radiomics as potential imaging biomarker. Survival prediction improved by combining clinical features with MRI-based radiomics.
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Affiliation(s)
- Paulien A Boot
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Steven W Mes
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands; Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Otolaryngology - Head and Neck Surgery, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Christiaan M de Bloeme
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
| | - Roland M Martens
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
| | - C René Leemans
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands; Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Otolaryngology - Head and Neck Surgery, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Ronald Boellaard
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
| | - Mark A van de Wiel
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Epidemiology and Biostatistics, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Pim de Graaf
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands.
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Shah D, Gehani A, Mahajan A, Chakrabarty N. Advanced Techniques in Head and Neck Cancer Imaging: Guide to Precision Cancer Management. Crit Rev Oncog 2023; 28:45-62. [PMID: 37830215 DOI: 10.1615/critrevoncog.2023047799] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Precision treatment requires precision imaging. With the advent of various advanced techniques in head and neck cancer treatment, imaging has become an integral part of the multidisciplinary approach to head and neck cancer care from diagnosis to staging and also plays a vital role in response evaluation in various tumors. Conventional anatomic imaging (CT scan, MRI, ultrasound) remains basic and focuses on defining the anatomical extent of the disease and its spread. Accurate assessment of the biological behavior of tumors, including tumor cellularity, growth, and response evaluation, is evolving with recent advances in molecular, functional, and hybrid/multiplex imaging. Integration of these various advanced diagnostic imaging and nonimaging methods aids understanding of cancer pathophysiology and provides a more comprehensive evaluation in this era of precision treatment. Here we discuss the current status of various advanced imaging techniques and their applications in head and neck cancer imaging.
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Affiliation(s)
- Diva Shah
- Senior Consultant Radiologist, Department of Radiodiagnosis, HCG Cancer Centre, Ahmedabad, 380060, Gujarat, India
| | - Anisha Gehani
- Department of Radiology and Imaging Sciences, Tata Medical Centre, New Town, WB 700160, India
| | - Abhishek Mahajan
- Department of Radiology, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, L7 8YA, United Kingdom
| | - Nivedita Chakrabarty
- Department of Radiodiagnosis, Tata Memorial Hospital, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), 400012, Mumbai, India
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Gabryś HS, Gote-Schniering J, Brunner M, Bogowicz M, Blüthgen C, Frauenfelder T, Guckenberger M, Maurer B, Tanadini-Lang S. Transferability of radiomic signatures from experimental to human interstitial lung disease. Front Med (Lausanne) 2022; 9:988927. [DOI: 10.3389/fmed.2022.988927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 10/31/2022] [Indexed: 11/18/2022] Open
Abstract
BackgroundInterstitial lung disease (ILD) defines a group of parenchymal lung disorders, characterized by fibrosis as their common final pathophysiological stage. To improve diagnosis and treatment of ILD, there is a need for repetitive non-invasive characterization of lung tissue by quantitative parameters. In this study, we investigated whether CT image patterns found in mice with bleomycin induced lung fibrosis can be translated as prognostic factors to human patients diagnosed with ILD.MethodsBleomycin was used to induce lung fibrosis in mice (n_control = 36, n_experimental = 55). The patient cohort consisted of 98 systemic sclerosis (SSc) patients (n_ILD = 65). Radiomic features (n_histogram = 17, n_texture = 137) were extracted from microCT (mice) and HRCT (patients) images. Predictive performance of the models was evaluated with the area under the receiver-operating characteristic curve (AUC). First, predictive performance of individual features was examined and compared between murine and patient data sets. Second, multivariate models predicting ILD were trained on murine data and tested on patient data. Additionally, the models were reoptimized on patient data to reduce the influence of the domain shift on the performance scores.ResultsPredictive power of individual features in terms of AUC was highly correlated between mice and patients (r = 0.86). A model based only on mean image intensity in the lung scored AUC = 0.921 ± 0.048 in mice and AUC = 0.774 (CI95% 0.677-0.859) in patients. The best radiomic model based on three radiomic features scored AUC = 0.994 ± 0.013 in mice and validated with AUC = 0.832 (CI95% 0.745-0.907) in patients. However, reoptimization of the model weights in the patient cohort allowed to increase the model’s performance to AUC = 0.912 ± 0.058.ConclusionRadiomic signatures of experimental ILD derived from microCT scans translated to HRCT of humans with SSc-ILD. We showed that the experimental model of BLM-induced ILD is a promising system to test radiomic models for later application and validation in human cohorts.
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Touska P, Connor S. Imaging of human papilloma virus associated oropharyngeal squamous cell carcinoma and its impact on diagnosis, prognostication, and response assessment. Br J Radiol 2022; 95:20220149. [PMID: 35687667 PMCID: PMC9815738 DOI: 10.1259/bjr.20220149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/22/2022] [Accepted: 06/07/2022] [Indexed: 01/13/2023] Open
Abstract
The clinical behaviour and outcomes of patients with oropharyngeal cancer (OPC) may be dichotomised according to their association with human papilloma virus (HPV) infection. Patients with HPV-associated disease (HPV+OPC) have a distinct demographic profile, clinical phenotype and demonstrate considerably better responses to chemoradiotherapy. This has led to a reappraisal of staging and treatment strategies for HPV+OPC, which are underpinned by radiological data. Structural modalities, such as CT and MRI can provide accurate staging information. These can be combined with ultrasound-guided tissue sampling and functional techniques (such as diffusion-weighted MRI and 18F-fludeoxyglucose positron emission tomography-CT) to monitor response to treatment, derive prognostic information, and to identify individuals who might benefit from intensification or deintensification strategies. Furthermore, advanced MRI techniques, such as intravoxel incoherent motion and perfusion MRI as well as application of artificial intelligence and radiomic techniques, have shown promise in treatment response monitoring and prognostication. The following review will consider the contemporary role and knowledge on imaging in HPV+OPC.
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Affiliation(s)
- Philip Touska
- Department of Radiology, Guy’s and St. Thomas’ NHS Foundation Trust, London, United Kingdom
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Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell Carcinoma. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Background: Oropharyngeal squamous cell carcinoma (OPSCC) associated with human papillomavirus (HPV) has higher rates of locoregional control and a better prognosis than HPV-negative OPSCC. These differences are due to some unique biological characteristics that are also visible through advanced imaging modalities. We investigated the ability of a multifactorial model based on both clinical factors and diffusion-weighted imaging (DWI) to determine the HPV status in OPSCC. Methods: The apparent diffusion coefficient (ADC) and the perfusion-free tissue diffusion coefficient D were derived from DWI, both in the primary tumor (PT) and lymph node (LN). First- and second-order radiomic features were extracted from ADC and D maps. Different families of machine learning (ML) algorithms were trained on our dataset using five-fold cross-validation. Results: A cohort of 144 patients was evaluated retrospectively, which was divided into a training set (n = 95) and a validation set (n = 49). The 50th percentile of DPT, the inverse difference moment of ADCLN, smoke habits, and tumor subsite (tonsil versus base of the tongue) were the most relevant predictors. Conclusions: DWI-based radiomics, together with patient-related parameters, allowed us to obtain good diagnostic accuracies in differentiating HPV-positive from HPV-negative patients. A substantial decrease in predictive power was observed in the validation cohort, underscoring the need for further analyses on a larger sample size.
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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Role of Texture Analysis in Oropharyngeal Carcinoma: A Systematic Review of the Literature. Cancers (Basel) 2022; 14:cancers14102445. [PMID: 35626048 PMCID: PMC9139172 DOI: 10.3390/cancers14102445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/02/2022] [Accepted: 05/10/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary The incidence of squamous cell carcinomas of the oropharynx has rapidly increased in the last two decades due to human papilloma virus infection (HPV). HPV-positive and HPV-negative squamous cell tumours differ in radiological imaging, treatment, and prognosis; therefore, differential diagnosis is mandatory. Radiomics with texture analysis is an innovative technique that has been used increasingly in recent years to characterise the tissue heterogeneity of certain structures such as neoplasms or organs by measuring the spatial distribution of pixel values on radiological imaging. This review delineates the application of texture analysis in oropharyngeal tumours and explores how radiomics may potentially improve clinical decision-making. Abstract Human papilloma virus infection (HPV) is associated with the development of lingual and palatine tonsil carcinomas. Diagnosing, differentiating HPV-positive from HPV-negative cancers, and assessing the presence of lymph node metastases or recurrences by the visual interpretation of images is not easy. Texture analysis can provide structural information not perceptible to human eyes. A systematic literature search was performed on 16 February 2022 for studies with a focus on texture analysis in oropharyngeal cancers. We conducted the research on PubMed, Scopus, and Web of Science platforms. Studies were screened for inclusion according to the preferred reporting items for systematic reviews. Twenty-six studies were included in our review. Nineteen articles related specifically to the oropharynx and seven articles analysed the head and neck area with sections dedicated to the oropharynx. Six, thirteen, and seven articles used MRI, CT, and PET, respectively, as the imaging techniques by which texture analysis was performed. Regarding oropharyngeal tumours, this review delineates the applications of texture analysis in (1) the diagnosis, prognosis, and assessment of disease recurrence or persistence after therapy, (2) early differentiation of HPV-positive versus HPV-negative cancers, (3) the detection of cancers not visualised by imaging alone, and (4) the assessment of lymph node metastases from unknown primary carcinomas.
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Jha AK, Mithun S, Purandare NC, Kumar R, Rangarajan V, Wee L, Dekker A. Radiomics: a quantitative imaging biomarker in precision oncology. Nucl Med Commun 2022; 43:483-493. [PMID: 35131965 DOI: 10.1097/mnm.0000000000001543] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Cancer treatment is heading towards precision medicine driven by genetic and biochemical markers. Various genetic and biochemical markers are utilized to render personalized treatment in cancer. In the last decade, noninvasive imaging biomarkers have also been developed to assist personalized decision support systems in oncology. The imaging biomarkers i.e., radiomics is being researched to develop specific digital phenotype of tumor in cancer. Radiomics is a process to extract high throughput data from medical images by using advanced mathematical and statistical algorithms. The radiomics process involves various steps i.e., image generation, segmentation of region of interest (e.g. a tumor), image preprocessing, radiomic feature extraction, feature analysis and selection and finally prediction model development. Radiomics process explores the heterogeneity, irregularity and size parameters of the tumor to calculate thousands of advanced features. Our study investigates the role of radiomics in precision oncology. Radiomics research has witnessed a rapid growth in the last decade with several studies published that show the potential of radiomics in diagnosis and treatment outcome prediction in oncology. Several radiomics based prediction models have been developed and reported in the literature to predict various prediction endpoints i.e., overall survival, progression-free survival and recurrence in various cancer i.e., brain tumor, head and neck cancer, lung cancer and several other cancer types. Radiomics based digital phenotypes have shown promising results in diagnosis and treatment outcome prediction in oncology. In the coming years, radiomics is going to play a significant role in precision oncology.
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Affiliation(s)
- Ashish Kumar Jha
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Sneha Mithun
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Nilendu C Purandare
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Rakesh Kumar
- Department of Nuclear Medicine, All India Institute of Medical Science, New Delhi, India
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
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The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment. Neuroradiology 2022; 64:1639-1647. [PMID: 35459957 PMCID: PMC9271107 DOI: 10.1007/s00234-022-02959-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 04/07/2022] [Indexed: 11/19/2022]
Abstract
Purpose
Human papillomavirus (HPV) status assessment is crucial for decision making in oropharyngeal cancer patients. In last years, several articles have been published investigating the possible role of radiomics in distinguishing HPV-positive from HPV-negative neoplasms. Aim of this review was to perform a systematic quality assessment of radiomic studies published on this topic. Methods Radiomics studies on HPV status prediction in oropharyngeal cancer patients were selected. The Radiomic Quality Score (RQS) was assessed by three readers to evaluate their methodological quality. In addition, possible correlations between RQS% and journal type, year of publication, impact factor, and journal rank were investigated. Results After the literature search, 19 articles were selected whose RQS median was 33% (range 0–42%). Overall, 16/19 studies included a well-documented imaging protocol, 13/19 demonstrated phenotypic differences, and all were compared with the current gold standard. No study included a public protocol, phantom study, or imaging at multiple time points. More than half (13/19) included feature selection and only 2 were comprehensive of non-radiomic features. Mean RQS was significantly higher in clinical journals. Conclusion Radiomics has been proposed for oropharyngeal cancer HPV status assessment, with promising results. However, these are supported by low methodological quality investigations. Further studies with higher methodological quality, appropriate standardization, and greater attention to validation are necessary prior to clinical adoption. Supplementary Information The online version contains supplementary material available at 10.1007/s00234-022-02959-0.
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Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance. Radiol Med 2022; 127:518-525. [PMID: 35320464 PMCID: PMC9098537 DOI: 10.1007/s11547-022-01468-7] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 02/11/2022] [Indexed: 10/29/2022]
Abstract
PURPOSE To evaluate stability and machine learning-based classification performance of radiomic features of spine bone tumors using diffusion- and T2-weighted magnetic resonance imaging (MRI). MATERIAL AND METHODS This retrospective study included 101 patients with histology-proven spine bone tumor (22 benign; 38 primary malignant; 41 metastatic). All tumor volumes were manually segmented on morphologic T2-weighted sequences. The same region of interest (ROI) was used to perform radiomic analysis on ADC map. A total of 1702 radiomic features was considered. Feature stability was assessed through small geometrical transformations of the ROIs mimicking multiple manual delineations. Intraclass correlation coefficient (ICC) quantified feature stability. Feature selection consisted of stability-based (ICC > 0.75) and significance-based selections (ranking features by decreasing Mann-Whitney p-value). Class balancing was performed to oversample the minority (i.e., benign) class. Selected features were used to train and test a support vector machine (SVM) to discriminate benign from malignant spine tumors using tenfold cross-validation. RESULTS A total of 76.4% radiomic features were stable. The quality metrics for the SVM were evaluated as a function of the number of selected features. The radiomic model with the best performance and the lowest number of features for classifying tumor types included 8 features. The metrics were 78% sensitivity, 68% specificity, 76% accuracy and AUC 0.78. CONCLUSION SVM classifiers based on radiomic features extracted from T2- and diffusion-weighted imaging with ADC map are promising for classification of spine bone tumors. Radiomic features of spine bone tumors show good reproducibility rates.
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Fontaine P, Andrearczyk V, Oreiller V, Abler D, Castelli J, Acosta O, De Crevoisier R, Vallières M, Jreige M, Prior JO, Depeursinge A. Cleaning Radiotherapy Contours for Radiomics Studies, is it Worth it? A Head and Neck Cancer Study. Clin Transl Radiat Oncol 2022; 33:153-158. [PMID: 35243026 PMCID: PMC8881196 DOI: 10.1016/j.ctro.2022.01.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 01/27/2022] [Indexed: 12/24/2022] Open
Abstract
PET images features are more stable across different delineation of the same target. Shape family features are more stable. The survival model based on Dedicated contours achieved better performance for predicting PFS.
A vast majority of studies in the radiomics field are based on contours originating from radiotherapy planning. This kind of delineation (e.g. Gross Tumor Volume, GTV) is often larger than the true tumoral volume, sometimes including parts of other organs (e.g. trachea in Head and Neck, H&N studies) and the impact of such over-segmentation was little investigated so far. In this paper, we propose to evaluate and compare the performance between models using two contour types: those from radiotherapy planning, and those specifically delineated for radiomics studies. For the latter, we modified the radiotherapy contours to fit the true tumoral volume. The two contour types were compared when predicting Progression-Free Survival (PFS) using Cox models based on radiomics features extracted from FluoroDeoxyGlucose-Positron Emission Tomography (FDG-PET) and CT images of 239 patients with oropharyngeal H&N cancer collected from five centers, the data from the 2020 HECKTOR challenge. Using Dedicated contours demonstrated better performance for predicting PFS, where Harell’s concordance indices of 0.61 and 0.69 were achieved for Radiotherapy and Dedicated contours, respectively. Using automatically Resegmented contours based on a fixed intensity range was associated with a C-index of 0.63. These results illustrate the importance of using clean dedicated contours that are close to the true tumoral volume in radiomics studies, even when tumor contours are already available from radiotherapy treatment planning
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Affiliation(s)
- Pierre Fontaine
- Univ Rennes, CLCC Eugene Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
- Institute of Information Systems, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Vincent Andrearczyk
- Institute of Information Systems, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Valentin Oreiller
- Institute of Information Systems, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Daniel Abler
- Institute of Information Systems, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Joel Castelli
- Univ Rennes, CLCC Eugene Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Oscar Acosta
- Univ Rennes, CLCC Eugene Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Renaud De Crevoisier
- Univ Rennes, CLCC Eugene Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Mario Jreige
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - John O Prior
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Adrien Depeursinge
- Institute of Information Systems, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
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Bagher Ebadian H, Siddiqui F, Ghanem A, Zhu S, Lu M, Movsas B, Chetty IJ. Radiomics outperforms clinical factors in characterizing human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinomas. Biomed Phys Eng Express 2021; 8. [PMID: 34781281 DOI: 10.1088/2057-1976/ac39ab] [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: 08/05/2021] [Accepted: 11/15/2021] [Indexed: 11/11/2022]
Abstract
Purpose:To utilize radiomic features extracted from CT images to characterize Human Papilloma Virus (HPV) for patients with oropharyngeal cancer squamous cell carcinoma (OPSCC).Methods:One hundred twenty-eight OPSCC patients with known HPV-status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16-protein testing) were retrospectively studied. Radiomic features (11 feature-categories) were extracted in 3D from contrast-enhanced (CE)-CT images of gross-tumor-volumes using 'in-house' software ('ROdiomiX') developed and validated following the image-biomarker-standardization-initiative (IBSI) guidelines. Six clinical factors were investigated: Age-at-Diagnosis, Gender, Total-Charlson, Alcohol-Use, Smoking-History, and T-Stage. A Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) technique combined with a Generalized-Linear-Model (Lasso-GLM) were applied to perform regularization in the radiomic and clinical feature spaces to identify the ranking of optimal feature subsets with most representative information for prediction of HPV. Lasso-GLM models/classifiers based on clinical factors only, radiomics only, and combined clinical and radiomics (ensemble/integrated) were constructed using random-permutation-sampling. Tests of significance (One-way ANOVA), average Area-Under-Receiver-Operating-Characteristic (AUC), and Positive and Negative Predictive values (PPV and NPV) were computed to estimate the generalization-error and prediction performance of the classifiers.Results:Five clinical factors, including T-stage, smoking status, and age, and 14 radiomic features, including tumor morphology, and intensity contrast were found to be statistically significant discriminators between HPV positive and negative cohorts. Performances for prediction of HPV for the 3 classifiers were: Radiomics-Lasso-GLM: AUC/PPV/NPV=0.789/0.755/0.805; Clinical-Lasso-GLM: 0.676/0.747/0.672, and Integrated/Ensemble-Lasso-GLM: 0.895/0.874/0.844. Results imply that the radiomics-based classifier enabled better characterization and performance prediction of HPV relative to clinical factors, and that the combination of both radiomics and clinical factors yields even higher accuracy characterization and predictive performance.Conclusion:Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results in support of the role of radiomic features towards characterization of HPV in patients with OPSCC.
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Affiliation(s)
- Hassan Bagher Ebadian
- Department of Radiation Oncology , Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Detroit, Michigan, 48202, UNITED STATES
| | - Farzan Siddiqui
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Ahmed Ghanem
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Simeng Zhu
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Mei Lu
- Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Benjamin Movsas
- Dept of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, 48202, UNITED STATES
| | - Indrin J Chetty
- Dept of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI 48202-2689, USA, Detroit, Michigan, 48202, UNITED STATES
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Smyczynska U, Grabia S, Nowicka Z, Papis-Ubych A, Bibik R, Latusek T, Rutkowski T, Fijuth J, Fendler W, Tomasik B. Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models. Cancers (Basel) 2021; 13:cancers13215584. [PMID: 34771747 PMCID: PMC8582656 DOI: 10.3390/cancers13215584] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/02/2021] [Accepted: 11/04/2021] [Indexed: 12/03/2022] Open
Abstract
Simple Summary Radiation-induced hypothyroidism (RIHT) commonly develops in cancer survivors that receive radiation therapy for cancers in the head and neck region. The state-of-art normal tissue complication probability (NTCP) models perform satisfactorily; however, they do not use the whole spectrum of information that can be obtained from imaging techniques. The radiomic approach offers the ability to efficiently mine features, which are imperceptible to the human eye, but may provide crucial data about the patient’s condition. We gathered CT images and clinical data from 98 patients undergoing radiotherapy for head and neck cancers, 27 of whom later developed RIHT. For them, we created machine-learning models to predict RIHT using automatically extracted radiomic features and appropriate clinical and dosimetric parameters. We also validated the well-established external state-of-art NTCP models on our datasets and observed that our radiomic-based models performed very similarly to them. This shows that automated tools may perform as well as the current standard but can be theoretically applied faster and be implemented into existing imaging software used when planning radiotherapy. Abstract State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33–35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). We showed that radiomic-based models did not outperform state-of-art NTCP models (p > 0.05). The potential benefit of radiomic-based approach is that it is dose-independent, and models can be used prior to treatment planning allowing faster selection of susceptible population.
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Affiliation(s)
- Urszula Smyczynska
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland; (U.S.); (S.G.); (Z.N.); (B.T.)
| | - Szymon Grabia
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland; (U.S.); (S.G.); (Z.N.); (B.T.)
| | - Zuzanna Nowicka
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland; (U.S.); (S.G.); (Z.N.); (B.T.)
| | - Anna Papis-Ubych
- Department of Radiotherapy, N. Copernicus Memorial Regional Specialist Hospital, 93-513 Lodz, Poland; (A.P.-U.); (J.F.)
| | - Robert Bibik
- Department of Radiation Oncology, Oncology Center of Radom, 26-600 Radom, Poland;
| | - Tomasz Latusek
- Radiotherapy Department, Maria Sklodowska-Curie National Research Institute of Oncology (MSCNRIO)—Branch in Gliwice, 44-101 Gliwice, Poland;
| | - Tomasz Rutkowski
- I Radiation and Clinical Oncology Department, Maria Sklodowska-Curie National Research Institute of Oncology (MSCNRIO)—Branch in Gliwice, 44-101 Gliwice, Poland;
| | - Jacek Fijuth
- Department of Radiotherapy, N. Copernicus Memorial Regional Specialist Hospital, 93-513 Lodz, Poland; (A.P.-U.); (J.F.)
- Department of Radiotherapy, Chair of Oncology, Medical University of Lodz, 93-509 Lodz, Poland
| | - Wojciech Fendler
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland; (U.S.); (S.G.); (Z.N.); (B.T.)
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Correspondence:
| | - Bartlomiej Tomasik
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland; (U.S.); (S.G.); (Z.N.); (B.T.)
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
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Song B, Yang K, Garneau J, Lu C, Li L, Lee J, Stock S, Braman NM, Koyuncu CF, Toro P, Fu P, Koyfman SA, Lewis JS, Madabhushi A. Radiomic Features Associated With HPV Status on Pretreatment Computed Tomography in Oropharyngeal Squamous Cell Carcinoma Inform Clinical Prognosis. Front Oncol 2021; 11:744250. [PMID: 34557418 PMCID: PMC8454409 DOI: 10.3389/fonc.2021.744250] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/16/2021] [Indexed: 12/18/2022] Open
Abstract
Purpose There is a lack of biomarkers for accurately prognosticating outcome in both human papillomavirus-related (HPV+) and tobacco- and alcohol-related (HPV-) oropharyngeal squamous cell carcinoma (OPSCC). The aims of this study were to i) develop and evaluate radiomic features within (intratumoral) and around tumor (peritumoral) on CT scans to predict HPV status; ii) investigate the prognostic value of the radiomic features for both HPV- and HPV+ patients, including within individual AJCC eighth edition-defined stage groups; and iii) develop and evaluate a clinicopathologic imaging nomogram involving radiomic, clinical, and pathologic factors for disease-free survival (DFS) prediction for HPV+ patients. Experimental Design This retrospective study included 582 OPSCC patients, of which 462 were obtained from The Cancer Imaging Archive (TCIA) with available tumor segmentation and 120 were from Cleveland Clinic Foundation (CCF, denoted as SCCF) with HPV+ OPSCC. We subdivided the TCIA cohort into training (ST, 180 patients) and validation (SV, 282 patients) based on an approximately 3:5 ratio for HPV status prediction. The top 15 radiomic features that were associated with HPV status were selected by the minimum redundancy-maximum relevance (MRMR) using ST and evaluated on SV. Using 3 of these 15 top HPV status-associated features, we created radiomic risk scores for both HPV+ (RRSHPV+) and HPV- patients (RRSHPV-) through a Cox regression model to predict DFS. RRSHPV+ was further externally validated on SCCF. Nomograms for the HPV+ population (Mp+RRS) were constructed. Both RRSHPV+ and Mp+RRS were used to prognosticate DFS for the AJCC eighth edition-defined stage I, stage II, and stage III patients separately. Results RRSHPV+ was prognostic for DFS for i) the whole HPV+ population [hazard ratio (HR) = 1.97, 95% confidence interval (CI): 1.35-2.88, p < 0.001], ii) the AJCC eighth stage I population (HR = 1.99, 95% CI: 1.04-3.83, p = 0.039), and iii) the AJCC eighth stage II population (HR = 3.61, 95% CI: 1.71-7.62, p < 0.001). HPV+ nomogram Mp+RRS (C-index, 0.59; 95% CI: 0.54-0.65) was also prognostic of DFS (HR = 1.86, 95% CI: 1.27-2.71, p = 0.001). Conclusion CT-based radiomic signatures are associated with both HPV status and DFS in OPSCC patients. With additional validation, the radiomic signature and its corresponding nomogram could potentially be used for identifying HPV+ OPSCC patients who might be candidates for therapy deintensification.
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Affiliation(s)
- Bolin Song
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States
| | - Kailin Yang
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, United States
| | - Jonathan Garneau
- Department of Otolaryngology and Head and Neck Surgery, University of Virginia, Charlottesville, VA, United States
| | - Cheng Lu
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States
| | - Lin Li
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States
| | - Jonathan Lee
- Imaging Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Sarah Stock
- Imaging Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Nathaniel M Braman
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States
| | - Can Fahrettin Koyuncu
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States
| | - Paula Toro
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Shlomo A Koyfman
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, United States
| | - James S Lewis
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Anant Madabhushi
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States.,Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, United States
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40
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Alamoudi AO. Radiomics, aptamers and nanobodies: New insights in cancer diagnostics and imaging. Hum Antibodies 2021; 29:1-15. [PMID: 33554897 DOI: 10.3233/hab-200436] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
At present, cancer is a major health issue and the second leading cause of mortality worldwide. Researchers have been working hard on investigating not only improved therapeutics but also on early detection methods, both critical to increasing treatment efficacy and developing methods for disease prevention. Diagnosis of cancers at an early stage can promote timely medical intervention and effective treatment and will result in inhibiting tumor growth and development. Several advances have been made in the diagnostics and imagining technologies for early tumor detection and deciding an effective therapy these include radiomics, nanobodies, and aptamers. Here in this review, we summarize the main applications of radiomics, aptamers, and the use of nanobody-based probes for molecular imaging applications in diagnosis, treatment planning, and evaluations in the field of oncology to develop quantitative and personalized medicine. The preclinical data reported to date are quite promising, and it is predicted that nanobody-based molecular imaging agents will play an important role in the diagnosis and management of different cancer types in near future.
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Bagher-Ebadian H, Zhu S, Siddiqui F, Lu M, Movsas B, Chetty IJ. Technical Note: On the development of an outcome-driven frequency filter for improving Radiomics-based modeling of Human Papilloma Virus (HPV) in patients with oropharyngeal squamous cell carcinomas. Med Phys 2021; 48:7552-7562. [PMID: 34390003 DOI: 10.1002/mp.15159] [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: 05/12/2021] [Revised: 07/08/2021] [Accepted: 08/03/2021] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To implement an outcome-driven frequency filter for improving radiomics-based modeling of human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinoma (OPSCC). METHODS AND MATERIALS One hundred twenty-eight OPSCC patients with known HPV status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16 protein testing) were retrospectively studied. A 3D Discrete Fourier Transform was applied on contrast-enhanced CT images of patient gross tumor volumes (GTV's) to transform intensity distributions to the frequency domain and estimate frequency power spectrums of HPV- and HPV+ patient cohorts. Statistical analyses were performed to rank frequency bands contributing towards prediction of HPV status. An outcome-driven frequency filter was designed accordingly and applied to GTV frequency information. 3D Inverse-Discrete-Fourier-Transform was applied to reconstruct HPV-related frequency-filtered images. Radiomics features (11 feature-categories) were extracted from pre- and post- frequency filtered images using our previously published 'ROdiomiX' software. Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) combined with a Generalized-Linear-Model (Lasso-GLM) was developed to identify and rank feature subsets with optimal information for prediction of HPV+/-. Radiomics-based Lasso-GLM classifiers (pre- and post-frequency filtered) were constructed and validated using a random permutation sampling and nested cross-validation techniques. Average Area Under Receiver Operating Characteristic (AUC), and Positive and Negative Predictive values (PPV, NPV) were computed to estimate generalization error and prediction performance. RESULTS Among 192 radiomic features, 15 features were found to be statistically significant discriminators between HPV+/- cohorts on post-frequency filtered CE-CT images; 14 such radiomic features were observed on pre-frequency filtered datasets. Discriminant features included tumor morphology and intensity contrast. Performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were: AUC/PPV/NPV = 0.789/0.755/0.805 and 0.850/0.808/0.877 respectively. Nested-CV performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were: AUC/PPV/NPV = 0.814/0.725/0.877 and 0.890/0.820/0.911 respectively. CONCLUSION Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results on the importance of frequency analysis prior to radiomic feature extraction toward enhancement of model performance for characterizing HPV in patients with OPSCC. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Simeng Zhu
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Farzan Siddiqui
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Mei Lu
- Department of Public Health, Henry Ford Health System, Michigan, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
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Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Lambin P, Bottari F, Tsoutzidis N, Miraglio B, Walsh S, Vos W, Hustinx R, Ferreira M, Lovinfosse P, Leijenaar RTH. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev 2021; 42:426-440. [PMID: 34309893 DOI: 10.1002/med.21846] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 07/05/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022]
Abstract
Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.
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Affiliation(s)
- Julien Guiot
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Akshayaa Vaidyanathan
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Louis Deprez
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Fadila Zerka
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Denis Danthine
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Anne-Noelle Frix
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | | | | | | | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Roland Hustinx
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium.,GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Marta Ferreira
- GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium
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Liu X, Maleki F, Muthukrishnan N, Ovens K, Huang SH, Pérez-Lara A, Romero-Sanchez G, Bhatnagar SR, Chatterjee A, Pusztaszeri MP, Spatz A, Batist G, Payabvash S, Haider SP, Mahajan A, Reinhold C, Forghani B, O’Sullivan B, Yu E, Forghani R. Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models. Cancers (Basel) 2021; 13:cancers13153723. [PMID: 34359623 PMCID: PMC8345201 DOI: 10.3390/cancers13153723] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 07/07/2021] [Accepted: 07/20/2021] [Indexed: 12/17/2022] Open
Abstract
Simple Summary Head and neck squamous cell carcinoma (HNSCC) is the most common mucosal malignancy of the head and neck and a leading cause of cancer death. HNSCC arises from different primary anatomical locations that are typically combined during radiomic analyses assuming that the radiomic features, i.e., quantitative image-based features, are similar based on histopathologic characteristics. However, whether these quantitative features are comparable across tumor sites remains unknown. The aim of our retrospective study was to assess if systematic differences exist between radiomic features based on different tumor sites in HNSCC and how they might affect machine learning model performance in endpoint prediction. Using a population of 605 HNSCC patients, we observed significant differences in radiomic features of tumors from different locations and showed that these differences can impact machine learning model performance. This suggests that tumor site should be considered when developing and evaluating radiomics-based models. Abstract Current radiomic studies of head and neck squamous cell carcinomas (HNSCC) are typically based on datasets combining tumors from different locations, assuming that the radiomic features are similar based on histopathologic characteristics. However, molecular pathogenesis and treatment in HNSCC substantially vary across different tumor sites. It is not known if a statistical difference exists between radiomic features from different tumor sites and how they affect machine learning model performance in endpoint prediction. To answer these questions, we extracted radiomic features from contrast-enhanced neck computed tomography scans (CTs) of 605 patients with HNSCC originating from the oral cavity, oropharynx, and hypopharynx/larynx. The difference in radiomic features of tumors from these sites was assessed using statistical analyses and Random Forest classifiers on the radiomic features with 10-fold cross-validation to predict tumor sites, nodal metastasis, and HPV status. We found statistically significant differences (p-value ≤ 0.05) between the radiomic features of HNSCC depending on tumor location. We also observed that differences in quantitative features among HNSCC from different locations impact the performance of machine learning models. This suggests that radiomic features may reveal biologic heterogeneity complementary to current gold standard histopathologic evaluation. We recommend considering tumor site in radiomic studies of HNSCC.
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Affiliation(s)
- Xiaoyang Liu
- Princess Margaret Hospital, University of Toronto, University Health Network, Toronto, ON M5G 2C1, Canada; (X.L.); (S.H.H.); (B.O.)
- Department of Radiology, Brigham and Women’s Hospital, Harvard University, Cambridge, MA 02115, USA
- Department of Medical Imaging, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Farhad Maleki
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
| | - Nikesh Muthukrishnan
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
| | - Katie Ovens
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
| | - Shao Hui Huang
- Princess Margaret Hospital, University of Toronto, University Health Network, Toronto, ON M5G 2C1, Canada; (X.L.); (S.H.H.); (B.O.)
- Princess Margaret Cancer Centre, Department of Radiation Oncology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Almudena Pérez-Lara
- Segal Cancer Centre & Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada; (A.P.-L.); (G.R.-S.); (G.B.)
| | - Griselda Romero-Sanchez
- Segal Cancer Centre & Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada; (A.P.-L.); (G.R.-S.); (G.B.)
| | - Sahir Rai Bhatnagar
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1A2, Canada
| | | | | | - Alan Spatz
- Division of Pathology, Jewish General Hospital, Montreal, QC H3Y 1E2, Canada; (M.P.P.); (A.S.)
| | - Gerald Batist
- Segal Cancer Centre & Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada; (A.P.-L.); (G.R.-S.); (G.B.)
| | - Seyedmehdi Payabvash
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (S.P.); (S.P.H.); (A.M.)
| | - Stefan P. Haider
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (S.P.); (S.P.H.); (A.M.)
| | - Amit Mahajan
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (S.P.); (S.P.H.); (A.M.)
| | - Caroline Reinhold
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
| | - Behzad Forghani
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
- Segal Cancer Centre & Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada; (A.P.-L.); (G.R.-S.); (G.B.)
| | - Brian O’Sullivan
- Princess Margaret Hospital, University of Toronto, University Health Network, Toronto, ON M5G 2C1, Canada; (X.L.); (S.H.H.); (B.O.)
- Princess Margaret Cancer Centre, Department of Radiation Oncology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Eugene Yu
- Princess Margaret Hospital, University of Toronto, University Health Network, Toronto, ON M5G 2C1, Canada; (X.L.); (S.H.H.); (B.O.)
- Department of Medical Imaging, University of Toronto, Toronto, ON M5S 1A1, Canada
- Correspondence: (E.Y.); (R.F.)
| | - Reza Forghani
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
- Segal Cancer Centre & Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada; (A.P.-L.); (G.R.-S.); (G.B.)
- Correspondence: (E.Y.); (R.F.)
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Gul M, Bonjoc KJC, Gorlin D, Wong CW, Salem A, La V, Filippov A, Chaudhry A, Imam MH, Chaudhry AA. Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers. Front Oncol 2021; 11:639326. [PMID: 34307123 PMCID: PMC8293690 DOI: 10.3389/fonc.2021.639326] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 06/08/2021] [Indexed: 11/21/2022] Open
Abstract
Radiomics is an emerging field in radiology that utilizes advanced statistical data characterizing algorithms to evaluate medical imaging and objectively quantify characteristics of a given disease. Due to morphologic heterogeneity and genetic variation intrinsic to neoplasms, radiomics have the potential to provide a unique insight into the underlying tumor and tumor microenvironment. Radiomics has been gaining popularity due to potential applications in disease quantification, predictive modeling, treatment planning, and response assessment - paving way for the advancement of personalized medicine. However, producing a reliable radiomic model requires careful evaluation and construction to be translated into clinical practices that have varying software and/or medical equipment. We aim to review the diagnostic utility of radiomics in otorhinolaryngology, including both cancers of the head and neck as well as the thyroid.
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Affiliation(s)
- Maryam Gul
- Amaze Research Foundation, Department of Biomarker Discovery, Anaheim, CA, United States
| | - Kimberley-Jane C. Bonjoc
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - David Gorlin
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Chi Wah Wong
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Amirah Salem
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Vincent La
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Aleksandr Filippov
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Abbas Chaudhry
- Amaze Research Foundation, Department of Biomarker Discovery, Anaheim, CA, United States
| | - Muhammad H. Imam
- Florida Cancer Specialists, Department of Oncology, Orlando, FL, United States
| | - Ammar A. Chaudhry
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
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Masson I, Da-Ano R, Lucia F, Doré M, Castelli J, Goislard de Monsabert C, Ramée JF, Sellami S, Visvikis D, Hatt M, Schick U. Statistical harmonization can improve the development of a multicenter CT-based radiomic model predictive of nonresponse to induction chemotherapy in laryngeal cancers. Med Phys 2021; 48:4099-4109. [PMID: 34008178 DOI: 10.1002/mp.14948] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 04/18/2021] [Accepted: 05/06/2021] [Indexed: 12/22/2022] Open
Abstract
PURPOSE To develop a radiomic model predicting nonresponse to induction chemotherapy in laryngeal cancers, from multicenter pretherapeutic contrast-enhanced computed tomography (CE-CT) and evaluate the benefit of feature harmonization in such a context. METHODS Patients (n = 104) eligible for laryngeal preservation chemotherapy were included in five centers. Primary tumor was manually delineated on the CE-CT images. The following radiomic features were extracted with an in-house software (MIRAS v1.1, LaTIM UMR 1101): intensity, shape, and textural features derived from Gray-Level Co-occurrence Matrix (GLCM), Neighborhood Gray Tone Difference Matrix (NGTDM), Gray-Level Run Length Matrix (GLRLM), and Gray-Level Size Zone Matrix (GLSZM). Harmonization was performed using ComBat after unsupervised hierarchical clustering, used to determine labels automatically, given the high heterogeneity of imaging characteristics across and within centers. Patients with similar feature distributions were grouped with unsupervised clustering into an optimal number of clusters (2) determined with "silhouette scoring." Statistical harmonization was then carried out with ComBat on these 2 identified clusters. The cohort was split into training/validation (n = 66) and testing (n = 32) sets. Area under the receiver operating characteristics curves (AUC) were used to evaluate the ability of radiomic features (before and after harmonization) to predict nonresponse to chemotherapy, and specificity (Sp) and sensitivity (Se) were used to quantify their performance in the testing set. RESULTS Without harmonization, none of the features identified as predictive in the training set remained significant in the testing set. After ComBat, one textural feature identified in the training set keeps a predictive trend in the testing set-Zone Percentage, derived from the GLSZM, was predictive of nonresponse in the training set (AUC = 0.62, Se = 70%, Sp = 64%, P = 0.04) and obtained a satisfactory performance in the testing set (Se = 80%, Sp = 67%, P = 0.03), although significance was limited by the size of the testing set. These results are consistent with previously published findings in head and neck cancers. CONCLUSIONS Radiomic features from CE-CT could help in the selection of patients for induction chemotherapy in laryngeal cancers, with relatively good sensitivity and specificity in predicting lack of response. Statistical harmonization with ComBat and unsupervised clustering seems to improve the predictive value of features extracted in such a heterogeneous multicenter setting.
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Affiliation(s)
| | | | - François Lucia
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.,Radiation Oncology Department, University Hospital, Brest, France
| | - Mélanie Doré
- Department of Radiation Oncology, Institut de cancérologie de l'Ouest René-Gauducheau, Saint-Herblain, France
| | - Joel Castelli
- Radiotherapy Department Cancer, Institute Eugène Marquis, Rennes, France.,University of Rennes 1, LTSI, Rennes, France
| | | | - Jean-François Ramée
- Department of Medical Oncology, Centre Hospitalier de Vendée, La Roche sur Yon, France
| | - Selima Sellami
- Radiation Oncology Department, University Hospital, Brest, France.,Radiotherapy Department, Centre Hospitalier de Cornouaille, Quimper, France
| | | | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Ulrike Schick
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.,Radiation Oncology Department, University Hospital, Brest, France
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46
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A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study. Diagnostics (Basel) 2021; 11:diagnostics11060979. [PMID: 34071518 PMCID: PMC8229740 DOI: 10.3390/diagnostics11060979] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 05/24/2021] [Accepted: 05/25/2021] [Indexed: 01/06/2023] Open
Abstract
Baseline clinical prognostic factors for recurrent and/or metastatic (RM) head and neck squamous cell carcinoma (HNSCC) treated with immunotherapy are lacking. CT-based radiomics may provide additional prognostic information. A total of 85 patients with RM-HNSCC were enrolled for this study. For each tumor, radiomic features were extracted from the segmentation of the largest tumor mass. A pipeline including different feature selection steps was used to train a radiomic signature prognostic for 10-month overall survival (OS). Features were selected based on their stability to geometrical transformation of the segmentation (intraclass correlation coefficient, ICC > 0.75) and their predictive power (area under the curve, AUC > 0.7). The predictive model was developed using the least absolute shrinkage and selection operator (LASSO) in combination with the support vector machine. The model was developed based on the first 68 enrolled patients and tested on the last 17 patients. Classification performance of the radiomic risk was evaluated accuracy and the AUC. The same metrics were computed for some baseline predictors used in clinical practice (volume of largest lesion, total tumor volume, number of tumor lesions, number of affected organs, performance status). The AUC in the test set was 0.67, while accuracy was 0.82. The performance of the radiomic score was higher than the one obtainable with the clinical variables (largest lesion volume: accuracy 0.59, AUC = 0.55; number of tumoral lesions: accuracy 0.71, AUC 0.36; number of affected organs: accuracy 0.47; AUC 0.42; total tumor volume: accuracy 0.59, AUC 0.53; performance status: accuracy 0.41, AUC = 0.47). Radiomics may provide additional baseline prognostic value compared to the variables used in clinical practice.
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47
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Bogowicz M, Pavic M, Riesterer O, Finazzi T, Garcia Schüler H, Holz-Sapra E, Rudofsky L, Basler L, Spaniol M, Ambrusch A, Hüllner M, Guckenberger M, Tanadini-Lang S. Targeting Treatment Resistance in Head and Neck Squamous Cell Carcinoma - Proof of Concept for CT Radiomics-Based Identification of Resistant Sub-Volumes. Front Oncol 2021; 11:664304. [PMID: 34123824 PMCID: PMC8191457 DOI: 10.3389/fonc.2021.664304] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/06/2021] [Indexed: 12/18/2022] Open
Abstract
Purpose Radiomics has already been proposed as a prognostic biomarker in head and neck cancer (HNSCC). However, its predictive power in radiotherapy has not yet been studied. Here, we investigated a local radiomics approach to distinguish between tumor sub-volumes with different levels of radiosensitivity as a possible target for radiation dose intensification. Materials and Methods Of 40 patients (n=28 training and n=12 validation) with biopsy confirmed locally recurrent HNSCC, pretreatment contrast-enhanced CT images were registered with follow-up PET/CT imaging allowing identification of controlled (GTVcontrol) vs non-controlled (GTVrec) tumor sub-volumes on pretreatment imaging. A bi-regional model was built using radiomic features extracted from pretreatment CT in the GTVrec and GTVcontrol to differentiate between those regions. Additionally, concept of local radiomics was implemented to perform detection task. The original tumor volume was divided into sub-volumes with no prior information on the location of recurrence. Radiomic features from those sub-volumes were then used to detect recurrent sub-volumes using multivariable logistic regression. Results Radiomic features extracted from non-controlled regions differed significantly from those in controlled regions (training AUC = 0.79 CI 95% 0.66 - 0.91 and validation AUC = 0.88 CI 95% 0.72 – 1.00). Local radiomics analysis allowed efficient detection of non-controlled sub-volumes both in the training AUC = 0.66 (CI 95% 0.56 – 0.75) and validation cohort 0.70 (CI 95% 0.53 – 0.86), however performance of this model was inferior to bi-regional model. Both models indicated that sub-volumes characterized by higher heterogeneity were linked to tumor recurrence. Conclusion Local radiomics is able to detect sub-volumes with decreased radiosensitivity, associated with location of tumor recurrence in HNSCC in the pre-treatment CT imaging. This proof of concept study, indicates that local CT radiomics can be used as predictive biomarker in radiotherapy and potential target for dose intensification.
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Affiliation(s)
- Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matea Pavic
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Oliver Riesterer
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Centre for Radiation Oncology KSA-KSB, Cantonal Hospital Aarau, Aarau, Switzerland
| | - Tobias Finazzi
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Helena Garcia Schüler
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Edna Holz-Sapra
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Leonie Rudofsky
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Lucas Basler
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Manon Spaniol
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Andreas Ambrusch
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martin Hüllner
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) Using Radiomics: The Impact of the Variation of CT Scanner. Cancers (Basel) 2021; 13:cancers13092269. [PMID: 34066857 PMCID: PMC8125906 DOI: 10.3390/cancers13092269] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 04/29/2021] [Accepted: 05/06/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Recent studies exploring the application of radiomics features in medicine have shown promising results. However, variation in imaging parameters may impact the robustness of these features. Feature robustness may then in turn affect the prediction performance of the machine learning models built upon these features. While numerous studies have tested feature robustness against a variety of imaging parameters, the extent to which feature robustness affects predictions remains unclear. A particularly notable application of radiomics in clinical oncology is the prediction of Human Papillomavirus (HPV) association in Oropharyngeal cancer. In this study we explore how CT scanner type affects the performance of radiomics features for HPV association prediction and highlight the need to implement precautionary approaches so as to minimize this effect. Abstract Studies have shown that radiomic features are sensitive to the variability of imaging parameters (e.g., scanner models), and one of the major challenges in these studies lies in improving the robustness of quantitative features against the variations in imaging datasets from multi-center studies. Here, we assess the impact of scanner choice on computed tomography (CT)-derived radiomic features to predict the association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV). This experiment was performed on CT image datasets acquired from two different scanner manufacturers. We demonstrate strong scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments reveal the effect of scanner manufacturer on the robustness of radiomic features, and the extent of this dependency is reflected in the performance of HPV prediction models. The results of this study highlight the importance of implementing an appropriate approach to reducing the impact of imaging parameters on radiomic features and consequently on the machine learning models, without removing features which are deemed non-robust but may contain learning information.
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Song LL, Chen SJ, Chen W, Shi Z, Wang XD, Song LN, Chen DS. Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images. BMC Med Imaging 2021; 21:54. [PMID: 33743615 PMCID: PMC7981906 DOI: 10.1186/s12880-021-00581-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 03/07/2021] [Indexed: 01/04/2023] Open
Abstract
Background Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared. Results The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity = 0.90 and 0.88, specificity = 0.82 and 0.80, positive predictive value = 0.86 and 0.84, negative predictive value = 0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p > 0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p < 0.001) and validation (0.90 vs. 0.68, p = 0.001) cohorts. Conclusions The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-021-00581-9.
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Affiliation(s)
- Le-le Song
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Shun-Jun Chen
- The Department of Ultrasound, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Wang Chen
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Zhan Shi
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Xiao-Dong Wang
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Li-Na Song
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Dian-Sen Chen
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China.
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50
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Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, Mattonen SA, El Naqa I. Machine and deep learning methods for radiomics. Med Phys 2021; 47:e185-e202. [PMID: 32418336 DOI: 10.1002/mp.13678] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 05/22/2019] [Accepted: 06/13/2019] [Indexed: 12/12/2022] Open
Abstract
Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three-dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open-source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics-based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.
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Affiliation(s)
- Michele Avanzo
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, 33081, Italy
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA
| | | | - Martin Vallières
- Medical Physics Unit, McGill University, Montreal, QC, Canada.,Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Arvind Rao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48103, USA
| | - Olivier Morin
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Sarah A Mattonen
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA
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