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Choi Y, Bang J, Kim SY, Seo M, Jang J. Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net. Eur Radiol 2024; 34:5389-5400. [PMID: 38243135 DOI: 10.1007/s00330-024-10585-y] [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: 06/03/2023] [Revised: 12/05/2023] [Accepted: 12/17/2023] [Indexed: 01/21/2024]
Abstract
PURPOSE To evaluate deep learning-based segmentation models for oropharyngeal squamous cell carcinoma (OPSCC) using CT and MRI with nnU-Net. METHODS This single-center retrospective study included 91 patients with OPSCC. The patients were grouped into the development (n = 56), test 1 (n = 13), and test 2 (n = 22) cohorts. In the development cohort, OPSCC was manually segmented on CT, MR, and co-registered CT-MR images, which served as the ground truth. The multimodal and multichannel input images were then trained using a self-configuring nnU-Net. For evaluation metrics, dice similarity coefficient (DSC) and mean Hausdorff distance (HD) were calculated for test cohorts. Pearson's correlation and Bland-Altman analyses were performed between ground truth and prediction volumes. Intraclass correlation coefficients (ICCs) of radiomic features were calculated for reproducibility assessment. RESULTS All models achieved robust segmentation performances with DSC of 0.64 ± 0.33 (CT), 0.67 ± 0.27 (MR), and 0.65 ± 0.29 (CT-MR) in test cohort 1 and 0.57 ± 0.31 (CT), 0.77 ± 0.08 (MR), and 0.73 ± 0.18 (CT-MR) in test cohort 2. No significant differences were found in DSC among the models. HD of CT-MR (1.57 ± 1.06 mm) and MR models (1.36 ± 0.61 mm) were significantly lower than that of the CT model (3.48 ± 5.0 mm) (p = 0.037 and p = 0.014, respectively). The correlation coefficients between the ground truth and prediction volumes for CT, MR, and CT-MR models were 0.88, 0.93, and 0.9, respectively. MR models demonstrated excellent mean ICCs of radiomic features (0.91-0.93). CONCLUSION The self-configuring nnU-Net demonstrated reliable and accurate segmentation of OPSCC on CT and MRI. The multimodal CT-MR model showed promising results for the simultaneous segmentation on CT and MRI. CLINICAL RELEVANCE STATEMENT Deep learning-based automatic detection and segmentation of oropharyngeal squamous cell carcinoma on pre-treatment CT and MRI would facilitate radiologic response assessment and radiotherapy planning. KEY POINTS • The nnU-Net framework produced a reliable and accurate segmentation of OPSCC on CT and MRI. • MR and CT-MR models showed higher DSC and lower Hausdorff distance than the CT model. • Correlation coefficients between the ground truth and predicted segmentation volumes were high in all the three models.
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Affiliation(s)
- Yangsean Choi
- Department of Radiology, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea.
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Centre, 43 Olympic-Ro 88, Songpa-Gu, Seoul, 05505, Republic of Korea.
| | - Jooin Bang
- Department of Otolaryngology-Head and Neck Surgery, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea
| | - Sang-Yeon Kim
- Department of Otolaryngology-Head and Neck Surgery, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea
| | - Minkook Seo
- Department of Radiology, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea
| | - Jinhee Jang
- Department of Radiology, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea
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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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Bourdillon AT. Computer Vision-Radiomics & Pathognomics. Otolaryngol Clin North Am 2024:S0030-6665(24)00072-0. [PMID: 38910065 DOI: 10.1016/j.otc.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.
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Affiliation(s)
- Alexandra T Bourdillon
- Department of Otolaryngology-Head & Neck Surgery, University of California-San Francisco, San Francisco, CA 94115, USA.
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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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Wu TC, Liu YL, Chen JH, Chen TY, Ko CC, Lin CY, Kao CY, Yeh LR, Su MY. Radiomics analysis for the prediction of locoregional recurrence of locally advanced oropharyngeal cancer and hypopharyngeal cancer. Eur Arch Otorhinolaryngol 2024; 281:1473-1481. [PMID: 38127096 DOI: 10.1007/s00405-023-08380-4] [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: 08/11/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
PURPOSE By radiomic analysis of the postcontrast CT images, this study aimed to predict locoregional recurrence (LR) of locally advanced oropharyngeal cancer (OPC) and hypopharyngeal cancer (HPC). METHODS A total of 192 patients with stage III-IV OPC or HPC from two independent cohort were randomly split into a training cohort with 153 cases and a testing cohort with 39 cases. Only primary tumor mass was manually segmented. Radiomic features were extracted using PyRadiomics, and then the support vector machine was used to build the radiomic model with fivefold cross-validation process in the training data set. For each case, a radiomics score was generated to indicate the probability of LR. RESULTS There were 94 patients with LR assigned in the progression group and 98 patients without LR assigned in the stable group. There was no significant difference of TNM staging, treatment strategies and common risk factors between these two groups. For the training data set, the radiomics model to predict LR showed 83.7% accuracy and 0.832 (95% CI 0.72, 0.87) area under the ROC curve (AUC). For the test data set, the accuracy and AUC slightly declined to 79.5% and 0.770 (95% CI 0.64, 0.80), respectively. The sensitivity/specificity of training and test data set for LR prediction were 77.6%/89.6%, and 66.7%/90.5%, respectively. CONCLUSIONS The image-based radiomic approach could provide a reliable LR prediction model in locally advanced OPC and HPC. Early identification of those prone to post-treatment recurrence would be helpful for appropriate adjustments to treatment strategies and post-treatment surveillance.
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Affiliation(s)
- Te-Chang Wu
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan
- Department of Medical Sciences Industry, Chang Jung Christian University, Tainan, Taiwan
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, USA
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, USA
- Department of Medical Imaging, E-DA Hospital, Kaohsiung, Taiwan
| | - Tai-Yuan Chen
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan
- Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan, Taiwan
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan
- Center of General Education, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Chiao-Yun Lin
- Department of Medical Imaging, E-DA Hospital, Kaohsiung, Taiwan
- College of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Cheng-Yi Kao
- College of Medicine, I-Shou University, Kaohsiung, Taiwan
- Division of Medical Radiology, E-DA Cancer Hospital, Kaohsiung, Taiwan
| | - Lee-Ren Yeh
- Department of Medical Imaging, E-DA Hospital, Kaohsiung, Taiwan.
- Department of Medical Imaging and Radiological Sciences, College of Medicine, I-Shou University, No. 1 Yida Road, Jiaosu Village, Yanchao District, Kaohsiung, 824, Taiwan.
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, 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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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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Semprini J. Examining the effect of Medicaid expansion on early detection of head and neck cancer of the oral cavity and pharynx by HPV-type and generosity of dental benefits. Cancer Rep (Hoboken) 2023; 6:e1840. [PMID: 37248803 PMCID: PMC10432424 DOI: 10.1002/cnr2.1840] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/09/2023] [Accepted: 05/22/2023] [Indexed: 05/31/2023] Open
Abstract
BACKGROUND Over a decade of evidence supports the claim that increased access to insurance through Medicaid expansions improves early detection of cancer. Yet, evidence linking Medicaid expansions to early detection of head and neck cancers (HNC) of the oral cavity and pharynx, specifically, may be limited by the lack of attention to Human Papillomavirus (HPV) etiology, generosity of dental coverage, and valid inference analyzing state cancer registry data. AIMS This study reexamined the effect of Medicaid expansion on early detection of HPV+/- HNC in states offering extensive dental benefits. MATERIALS AND METHODS Specialized data from the Surveillance, Epidemiology, and End Results (SEER) program was analyzed to account for, previously unmeasurable, differential detection patterns of HNCs associated with HPV. Then, to identify the effect of increasing Medicaid eligibility on staging patterns in states offering extensive benefits amidst potentially non-common trends between states, a "Triple Differences" design identifies the differential effect of Medicaid Expansion (with dental coverage) on HPV-negative HNCs relative to the change in HPV-positive HNCs. For valid inference analyzing a small number of state clusters (12) in cancer registry data, each regression model applies a Wild Cluster Bootstrap. RESULTS Expanding Medicaid eligibility was found to be associated with a decrease in the proportion of distant-stage diagnoses of HPV(-) HNCs, but only among states which increased Medicaid dental generosity at the time of Medicaid expansion. CONCLUSIONS These results suggest that adding extensive Medicaid dental benefits was the primary mechanism impacting HNC detection. This study highlights the potential positive spillover effects of policies which increase access to public dental coverage for low-income adults, while also showing the limitation of access to dental services for improving early detection of HPV+ HNCs.
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Affiliation(s)
- Jason Semprini
- Department of Health Management and PolicyUniversity of Iowa College of Public HealthIowa CityIowaUSA
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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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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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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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Zhang H, Guo D, Liu H, He X, Qiao X, Liu X, Liu Y, Zhou J, Zhou Z, Liu X, Fang Z. MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018. Diagnostics (Basel) 2022; 12:diagnostics12051043. [PMID: 35626199 PMCID: PMC9139717 DOI: 10.3390/diagnostics12051043] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 02/04/2023] Open
Abstract
Differentiating hepatocellular carcinoma (HCC) from other primary liver malignancies in the Liver Imaging Reporting and Data System (LI-RADS) M (LR-M) tumours noninvasively is critical for patient treatment options, but visual evaluation based on medical images is a very challenging task. This study aimed to evaluate whether magnetic resonance imaging (MRI) models based on radiomics features could further improve the ability to classify LR-M tumour subtypes. A total of 102 liver tumours were defined as LR-M by two radiologists based on LI-RADS and were confirmed to be HCC (n = 31) and non-HCC (n = 71) by surgery. A radiomics signature was constructed based on reproducible features using the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression algorithms with tenfold cross-validation. Logistic regression modelling was applied to establish different models based on T2-weighted imaging (T2WI), arterial phase (AP), portal vein phase (PVP), and combined models. These models were verified independently in the validation cohort. The area under the curve (AUC) of the models based on T2WI, AP, PVP, T2WI + AP, T2WI + PVP, AP + PVP, and T2WI + AP + PVP were 0.768, 0.838, 0.778, 0.880, 0.818, 0.832, and 0.884, respectively. The combined model based on T2WI + AP + PVP showed the best performance in the training cohort and validation cohort. The discrimination efficiency of each radiomics model was significantly better than that of junior radiologists’ visual assessment (p < 0.05; Delong). Therefore, the MRI-based radiomics models had a good ability to discriminate between HCC and non-HCC in LR-M tumours, providing more options to improve the accuracy of LI-RADS classification.
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Affiliation(s)
- Haiping Zhang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Dajing Guo
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Huan Liu
- GE Healthcare, Shanghai 201203, China;
| | - Xiaojing He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Xiaofeng Qiao
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Xinjie Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Yangyang Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Jun Zhou
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Zhiming Zhou
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Xi Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Zheng Fang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
- Correspondence: ; Tel.: +86-23-63693238
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王 寅, 雷 大. [Research progress in CT-based radiomics constructing hypopharyngeal cancer and multisystem tumor prediction model]. LIN CHUANG ER BI YAN HOU TOU JING WAI KE ZA ZHI = JOURNAL OF CLINICAL OTORHINOLARYNGOLOGY, HEAD, AND NECK SURGERY 2022; 36:158-162. [PMID: 35172558 PMCID: PMC10128304 DOI: 10.13201/j.issn.2096-7993.2022.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Indexed: 04/30/2023]
Abstract
Radiomics, a technique for quantitative analysis of tumor imaging information through high-throughput extraction, uses a non-invasive way to capture a large number of internal heterogeneity characteristics of tumors, providing imaging basis for tumor staging and typing, tumor invasion site and distant metastasis, postoperative induction chemotherapy and prognosis, and providing new ideas and new thinking for the field of personalized precision medicine of tumors. This review aims to briefly summarize the latest research progress of imaging omics in the diagnosis and treatment design of head and neck tumor, and to discuss the research progress of constructing the treatment plan and prognosis evaluation model of hypopharyngeal cancer based on imaging omics, and to predict and forecast its development direction and clinical application.
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Affiliation(s)
- 寅 王
- 山东大学齐鲁医院耳鼻咽喉科 国家卫健委耳鼻喉科学重点实验室(济南,250000)
| | - 大鹏 雷
- 山东大学齐鲁医院耳鼻咽喉科 国家卫健委耳鼻喉科学重点实验室(济南,250000)
- 雷大鹏,
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Li Z, Liu Z, Guo Y, Wang S, Qu X, Li Y, Pan Y, Zhang L, Su D, Yang Q, Tao X, Yue Q, Xian J. Dual-energy CT-based radiomics nomogram in predicting histological differentiation of head and neck squamous carcinoma: a multicenter study. Neuroradiology 2021; 64:361-369. [PMID: 34860278 DOI: 10.1007/s00234-021-02860-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/09/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE To develop and validate a dual-energy CT (DECT)-based radiomics nomogram from multicenter trials for predicting the histological differentiation of head and neck squamous cell carcinoma (HNSCC). METHODS A total of 178 patients (112 in the training and 66 in the validation cohorts) from eight institutions with histologically proven HNSCCs were included in this retrospective study. Radiomics-signature models were constructed from features extracted from virtual monoenergetic images (VMI) and iodine-based material decomposition images (IMDI), reconstructed from venous-phase DECT images. Clinical factors were also assessed to build a clinical model. Multivariate logistic regression analysis was used to develop a nomogram combining the radiomics signature models and clinical model for predicting poorly differentiated HNSCC and moderately well-differentiated HNSCC. The predictive performance of the clinical model, radiomics signature models, and nomogram was compared. The calibration degree of the nomogram was also assessed. RESULTS The tumor location, VMI-signature, and IMDI-signature were associated with the degree of HNSCC differentiation, and areas under the ROC curves (AUCs) were 0.729, 0.890, and 0.833 in the training cohort and 0.627, 0.859, and 0.843 in the validation cohort, respectively. The nomogram incorporating tumor location and two radiomics-signature models yielded the best performance in training (AUC = 0.987) and validation (AUC = 0.968) cohorts with a good calibration degree. CONCLUSION The nomogram that integrated the DECT-based radiomics-signature models and tumor location showed good performance in predicting histological differentiation degree of HNSCC, providing a novel combination for predicting HNSCC differentiation.
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Affiliation(s)
- Zheng Li
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1, DongJiaoMinXiang Street, Dongcheng District, Beijing, 100730, China
| | - Zhaohui Liu
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1, DongJiaoMinXiang Street, Dongcheng District, Beijing, 100730, China
| | - Yan Guo
- Pharmaceutical Diagnostics, Precision Health Institute, GE Healthcare China, Beijing, 100176, China
| | - Sicong Wang
- Pharmaceutical Diagnostics, Precision Health Institute, GE Healthcare China, Beijing, 100176, China
| | - Xiaoxia Qu
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1, DongJiaoMinXiang Street, Dongcheng District, Beijing, 100730, China
| | - Yajun Li
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Yucheng Pan
- Department of Radiology, Eye Ear Nose and Throat Hospital of Fudan University, Shanghai, 200031, China
| | - Longjiang Zhang
- Department of Diagnostic Radiology, General Hospital of Eastern Theater Command/Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Danke Su
- Imaging Center, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Qian Yang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Xiaofeng Tao
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Qiang Yue
- Department of Radiology, West China Hospital, West China Medical School, Sichuan University, Chengdu, 610041, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1, DongJiaoMinXiang Street, Dongcheng District, Beijing, 100730, China.
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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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