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Katabathina VS, Ghannam S, Chen M, Desalme B, Gabos R, Emejulu I, Sandhu PK, Valente P, Dasyam AK, Prasad SR. Update on Pathologic Conditions, Imaging Findings, Prevention, and Management of Human Papillomavirus-related Neoplasms. Radiographics 2024; 44:e230179. [PMID: 39024173 DOI: 10.1148/rg.230179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Human papillomavirus (HPV) is the most common sexually transmitted infection that proliferates in the squamous epithelium and is the most common source of viral-related neoplasms. Low-risk subtypes (HPV-6 and -11) cause respiratory papillomas (laryngeal, tracheal, and bronchial) and condyloma acuminata of the penis, anus, and perineal region (anogenital warts). High-risk subtypes (HPV-16, -18, -31, and -33) are responsible for oropharyngeal squamous cell carcinoma (SCC) that involves the tongue base, tonsils, posterior pharyngeal wall, and larynx and malignancies of the anogenital region (cancers of the cervix, vagina, vulva, penis, and anal canal). Recent studies have increasingly shown a favorable treatment response and substantial differences in the overall prognosis associated with HPV-associated oropharyngeal cancers. Given this fact, oropharyngeal, cervical, and penile SCCs are classified as HPV-associated and HPV-independent cancers in the current World Health Organization classification. Imaging is essential in the early detection, diagnosis, and staging of HPV-associated cancers. Imaging also helps assess treatment response and postoperative complications and is used for long-term surveillance. HPV-associated oropharyngeal SCCs have well-defined borders and solid and cystic nodal metastases at imaging. Updated screening and vaccination guidelines are currently available that have great potential to decrease the overall disease burden and help control this worldwide public health concern. Novel therapeutic strategies, such as immunotherapies, are being explored, and imaging biomarkers that can predict treatment response and prognosis are being investigated; radiologists play a pivotal role in these efforts. ©RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
- Venkata S Katabathina
- From the Departments of Radiology (V.S.K., S.G., B.D., R.G., I.E.) and Pathology (P.V.), University of Texas Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229; Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (M.C., S.R.P.); Department of Radiology, Le Bonheur Children's Hospital, Memphis, Tenn (P.K.S.); and Department of Radiology, University of Pittsburg Medical Center, Pittsburg, Pa (A.K.D.)
| | - Sammar Ghannam
- From the Departments of Radiology (V.S.K., S.G., B.D., R.G., I.E.) and Pathology (P.V.), University of Texas Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229; Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (M.C., S.R.P.); Department of Radiology, Le Bonheur Children's Hospital, Memphis, Tenn (P.K.S.); and Department of Radiology, University of Pittsburg Medical Center, Pittsburg, Pa (A.K.D.)
| | - Melissa Chen
- From the Departments of Radiology (V.S.K., S.G., B.D., R.G., I.E.) and Pathology (P.V.), University of Texas Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229; Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (M.C., S.R.P.); Department of Radiology, Le Bonheur Children's Hospital, Memphis, Tenn (P.K.S.); and Department of Radiology, University of Pittsburg Medical Center, Pittsburg, Pa (A.K.D.)
| | - Brian Desalme
- From the Departments of Radiology (V.S.K., S.G., B.D., R.G., I.E.) and Pathology (P.V.), University of Texas Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229; Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (M.C., S.R.P.); Department of Radiology, Le Bonheur Children's Hospital, Memphis, Tenn (P.K.S.); and Department of Radiology, University of Pittsburg Medical Center, Pittsburg, Pa (A.K.D.)
| | - Ryan Gabos
- From the Departments of Radiology (V.S.K., S.G., B.D., R.G., I.E.) and Pathology (P.V.), University of Texas Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229; Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (M.C., S.R.P.); Department of Radiology, Le Bonheur Children's Hospital, Memphis, Tenn (P.K.S.); and Department of Radiology, University of Pittsburg Medical Center, Pittsburg, Pa (A.K.D.)
| | - Ifeadi Emejulu
- From the Departments of Radiology (V.S.K., S.G., B.D., R.G., I.E.) and Pathology (P.V.), University of Texas Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229; Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (M.C., S.R.P.); Department of Radiology, Le Bonheur Children's Hospital, Memphis, Tenn (P.K.S.); and Department of Radiology, University of Pittsburg Medical Center, Pittsburg, Pa (A.K.D.)
| | - Preet K Sandhu
- From the Departments of Radiology (V.S.K., S.G., B.D., R.G., I.E.) and Pathology (P.V.), University of Texas Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229; Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (M.C., S.R.P.); Department of Radiology, Le Bonheur Children's Hospital, Memphis, Tenn (P.K.S.); and Department of Radiology, University of Pittsburg Medical Center, Pittsburg, Pa (A.K.D.)
| | - Philip Valente
- From the Departments of Radiology (V.S.K., S.G., B.D., R.G., I.E.) and Pathology (P.V.), University of Texas Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229; Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (M.C., S.R.P.); Department of Radiology, Le Bonheur Children's Hospital, Memphis, Tenn (P.K.S.); and Department of Radiology, University of Pittsburg Medical Center, Pittsburg, Pa (A.K.D.)
| | - Anil K Dasyam
- From the Departments of Radiology (V.S.K., S.G., B.D., R.G., I.E.) and Pathology (P.V.), University of Texas Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229; Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (M.C., S.R.P.); Department of Radiology, Le Bonheur Children's Hospital, Memphis, Tenn (P.K.S.); and Department of Radiology, University of Pittsburg Medical Center, Pittsburg, Pa (A.K.D.)
| | - Srinivasa R Prasad
- From the Departments of Radiology (V.S.K., S.G., B.D., R.G., I.E.) and Pathology (P.V.), University of Texas Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229; Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (M.C., S.R.P.); Department of Radiology, Le Bonheur Children's Hospital, Memphis, Tenn (P.K.S.); and Department of Radiology, University of Pittsburg Medical Center, Pittsburg, Pa (A.K.D.)
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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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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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Chen LL, Lauwers I, Verduijn G, Philippens M, Gahrmann R, Capala ME, Petit S. MRI for Differentiation between HPV-Positive and HPV-Negative Oropharyngeal Squamous Cell Carcinoma: A Systematic Review. Cancers (Basel) 2024; 16:2105. [PMID: 38893224 PMCID: PMC11171338 DOI: 10.3390/cancers16112105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
Human papillomavirus (HPV) is an important risk factor for oropharyngeal squamous cell carcinoma (OPSCC). HPV-positive (HPV+) cases are associated with a different pathophysiology, microstructure, and prognosis compared to HPV-negative (HPV-) cases. This review aimed to investigate the potential of magnetic resonance imaging (MRI) to discriminate between HPV+ and HPV- tumours and predict HPV status in OPSCC patients. A systematic literature search was performed on 15 December 2022 on EMBASE, MEDLINE ALL, Web of Science, and Cochrane according to PRISMA guidelines. Twenty-eight studies (n = 2634 patients) were included. Five, nineteen, and seven studies investigated structural MRI (e.g., T1, T2-weighted), diffusion-weighted MRI, and other sequences, respectively. Three out of four studies found that HPV+ tumours were significantly smaller in size, and their lymph node metastases were more cystic in structure than HPV- ones. Eleven out of thirteen studies found that the mean apparent diffusion coefficient was significantly higher in HPV- than HPV+ primary tumours. Other sequences need further investigation. Fourteen studies used MRI to predict HPV status using clinical, radiological, and radiomics features. The reported areas under the curve (AUC) values ranged between 0.697 and 0.944. MRI can potentially be used to find differences between HPV+ and HPV- OPSCC patients and predict HPV status with reasonable accuracy. Larger studies with external model validation using independent datasets are needed before clinical implementation.
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Affiliation(s)
- Linda L. Chen
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
| | - Iris Lauwers
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
| | - Gerda Verduijn
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
| | - Marielle Philippens
- Department of Radiotherapy, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands;
| | - Marta E. Capala
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
| | - Steven Petit
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
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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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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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8
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Altinok O, Guvenis A. Interpretable radiomics method for predicting human papillomavirus status in oropharyngeal cancer using Bayesian networks. Phys Med 2023; 114:102671. [PMID: 37708571 DOI: 10.1016/j.ejmp.2023.102671] [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: 03/01/2023] [Revised: 06/14/2023] [Accepted: 09/06/2023] [Indexed: 09/16/2023] Open
Abstract
OBJECTIVES To develop a simple interpretable Bayesian Network (BN) to classify HPV status in patients with oropharyngeal cancer. METHODS Two hundred forty-six patients, 216 of whom were HPV positive, were used in this study. We extracted 851 radiomics markers from patients' contrast-enhanced Computed Tomography (CT) images. Mens eX Machina (MXM) approach selected two most relevant predictors: sphericity and max2DDiameterRow. The area under the curve (AUC) demonstrated BN model performance in 30% of the data reserved for testing. A Support Vector Machine (SVM) based method was also implemented for comparison purposes. RESULTS The Mens eX Machina (MXM) approach selected two most relevant predictors: sphericity and max2DDiameterRow. Areas under the Curves (AUC) were found 0.78 and 0.72 on the training and test data, respectively. When using support vector machine (SVM) and 25 features, the AUC was found 0.83 on the test data. CONCLUSIONS The straightforward structure and power of interpretability of our BN model will help clinicians make treatment decisions and enable the non-invasive detection of HPV status from contrast-enhanced CT images. Higher accuracy can be obtained using more complex structures at the expense of lower interpretability. ADVANCES IN KNOWLEDGE Radiomics is being studied lately as a simple imaging data based HPV status detection technique which can be an alternative to laboratory approaches. However, it generally lacks interpretability. This work demonstrated the feasibility of using Bayesian networks based radiomics for predicting HPV positivity in an interpretable way.
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Affiliation(s)
- Oya Altinok
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey; Biomedical Engineering, Namik Kemal University, Tekirdağ, Turkey.
| | - Albert Guvenis
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
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9
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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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10
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Bhattacharya K, Mahajan A, Vaish R, Rane S, Shukla S, D'Cruz AK. Imaging of Neck Nodes in Head and Neck Cancers - a Comprehensive Update. Clin Oncol (R Coll Radiol) 2023; 35:429-445. [PMID: 37061456 DOI: 10.1016/j.clon.2023.03.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 02/08/2023] [Accepted: 03/22/2023] [Indexed: 03/29/2023]
Abstract
Cervical lymph node metastases from head and neck squamous cell cancers significantly reduce disease-free survival and worsen overall prognosis and, hence, deserve more aggressive management and follow-up. As per the eighth edition of the American Joint Committee on Cancer staging manual, extranodal extension, especially in human papillomavirus-negative cancers, has been incorporated in staging as it is important in deciding management and significantly impacts the outcome of head and neck squamous cell cancer. Lymph node imaging with various radiological modalities, including ultrasound, computed tomography and magnetic resonance imaging, has been widely used, not only to demonstrate nodal involvement but also for guided histopathological evaluation and therapeutic intervention. Computed tomography and magnetic resonance imaging, together with positron emission tomography, are used widely for the follow-up of treated patients. Finally, there is an emerging role for artificial intelligence in neck node imaging that has shown promising results, increasing the accuracy of detection of nodal involvement, especially normal-appearing nodes. The aim of this review is to provide a comprehensive overview of the diagnosis and management of involved neck nodes with a focus on sentinel node anatomy, pathogenesis, imaging correlates (including radiogenomics and artificial intelligence) and the role of image-guided interventions.
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Affiliation(s)
- K Bhattacharya
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - A Mahajan
- The Clatterbridge Cancer Centre, NHS Foundation Trust, Liverpool, UK.
| | - R Vaish
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - S Rane
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - S Shukla
- Homi Bhabha Cancer Hospital, Varanasi, Uttar Pradesh, India
| | - A K D'Cruz
- Apollo Hospitals, India; Union International Cancer Control (UICC), Geneva, Switzerland; Foundation of Head Neck Oncology, India
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11
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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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12
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HPV impact on oropharyngeal cancer radiological staging: 7th vs 8th edition of AJCC TNM classification. Clin Imaging 2022; 93:39-45. [DOI: 10.1016/j.clinimag.2022.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 10/04/2022] [Accepted: 10/18/2022] [Indexed: 11/05/2022]
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13
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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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14
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Adrian G, Carlsson H, Kjellén E, Sjövall J, Zackrisson B, Nilsson P, Gebre-Medhin M. Primary tumor volume and prognosis for patients with p16-positive and p16-negative oropharyngeal squamous cell carcinoma treated with radiation therapy. Radiat Oncol 2022; 17:107. [PMID: 35701827 PMCID: PMC9195357 DOI: 10.1186/s13014-022-02074-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 05/27/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The prescribed radiation dose to patients with oropharyngeal squamous cell carcinoma (OPSCC) is standardized, even if the prognosis for individual patients may differ. Easy-at-hand pre-treatment risk stratification methods are valuable to individualize therapy. In the current study we assessed the prognostic impact of primary tumor volume for p16-positive and p16-negative tumors and in relationship to other prognostic factors for outcome in patients with OPSCC treated with primary radiation therapy (RT). METHODS Five hundred twenty-three OPSCC patients with p16-status treated with primary RT (68.0 Gy to 73.1 Gy in 7 weeks, or 68.0 Gy in 4.5 weeks), with or without concurrent chemotherapy, within three prospective trials were included in the study. Local failure (LF), progression free survival (PFS) and overall survival (OS) in relationship to the size of the primary gross tumor volume (GTV-T) and other prognostic factors were investigated. Efficiency of intensified RT (RT with total dose 73.1 Gy or given within 4.5 weeks) was analyzed in relationship to tumor volume. RESULTS The volume of GTV-T and p16-status were found to be the strongest prognostic markers for LF, PFS and OS. For p16-positive tumors, an increase in tumor volume had a significantly higher negative prognostic impact compared with p16-negative tumors. Within a T-classification, patients with a smaller tumor, compared with a larger tumor, had a better prognosis. The importance of tumor volume remained after adjusting for nodal status, age, performance status, smoking status, sex, and hemoglobin-level. The adjusted hazard ratio for OS per cm3 increase in tumor volume was 2.3% (95% CI 0-4.9) for p16-positive and 1.3% (95% 0.3-2.2) for p16-negative. Exploratory analyses suggested that intensified RT could mitigate the negative impact of a large tumor volume. CONCLUSIONS Outcome for patients with OPSCC treated with RT is largely determined by tumor volume, even when adjusting for other established prognostic factors. Tumor volume is significantly more influential for patients with p16-positive tumors. Patients with large tumor volumes might benefit by intensified RT to improve survival.
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Affiliation(s)
- Gabriel Adrian
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund University, Lund, Sweden. .,Division of Oncology, Department of Clinical Sciences Lund, Skåne University Hospital, Lund University, Lund, Sweden.
| | - Henrik Carlsson
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund University, Lund, Sweden.,Division of Oncology, Department of Clinical Sciences Lund, Skåne University Hospital, Lund University, Lund, Sweden
| | - Elisabeth Kjellén
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund University, Lund, Sweden
| | - Johanna Sjövall
- Department of Otorhinolaryngology -Head and Neck Surgery, Skåne University Hospital, Lund University, Lund, Sweden
| | - Björn Zackrisson
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Per Nilsson
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund University, Lund, Sweden.,Department of Clinical Sciences, Medical Radiation Physics, Skåne University Hospital, Lund University, Lund, Sweden
| | - Maria Gebre-Medhin
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund University, Lund, Sweden
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15
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Vijayvargiya P, Trivedi S, Rupji M, Song H, Liu Y, Jiang R, Kaka AS, Chen GZ, Stokes W, Steuer C, Shin DM, Beitler JJ, Patel MR, Aiken A, Saba NF. Comparison of the Seventh and Eighth Edition of American Joint Committee on Cancer (AJCC) Staging for Selected and Nonselected Oropharyngeal Squamous Cell Carcinomas. Oncologist 2022; 27:48-56. [PMID: 35305095 PMCID: PMC8842371 DOI: 10.1093/oncolo/oyab001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 11/15/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES The eighth edition American Joint Committee on Cancer (AJCC) Staging incorporates significant changes to the seventh edition in the staging of oropharyngeal squamous cell carcinomas (OPSCC). An important change was the inclusion of OPSCC associated with the human papilloma virus (HPV). Our goal is to compare the performance of both staging systems for patients with HPV-selected and unselected clinical characteristics for OPSCC. METHODS Using the Surveillance, Epidemiology, and End Results (SEER) database, 2004-2016, we identified patients with likely HPV-associated OPSCC based on surrogate markers (white males aged <65 years old with squamous cell carcinomas of the tonsil and base of tongue), excluding those who underwent surgery. We re-classified these patients using seventh and eighth edition staging for HPV-selected OPSCC and compared the prediction performance of both staging editions for overall survival (OS) and disease-specific survival (DSS). We performed the same analysis for clinically unselected patients with OPSCC. RESULTS Our analysis included 9554 patients with a median follow-up of 67 months. Comparing the eighth versus seventh edition for our HPV-selected cohort, clinical staging changed for 92.3% of patients and 10-year OS was 62.2%, 61.2%, 35.3%, and 15.5% for Stage I, II, III, and IV, versus 52.9%, 59.2%, 61.6%, 55.1%, 38.3%, and 15.5% for stage I, II, III, IVA, IVB, and IVC, respectively. A similar pattern was observed for 10-year DSS. The concordance statistics for our HPV-selected cohort were improved for both AJCC 7 (0.6260) and AJCC 8 (0.6846) compared with the unselected cohort, 0.5860 and 0.6457 for AJCC 7 and 8, respectively. CONCLUSION The overall performance of discrimination improved from AJCC 7 to AJCC 8 for both clinically selected and unselected patients, but more notably for our HPV-selected cohort. Despite the lack of statistically significant differentiation between Stages I and II in AJCC 8 in either groups, markedly improved discrimination was observed between Stages I/II, III, and IV in the HPV-selected cohort.
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Affiliation(s)
- Pooja Vijayvargiya
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Sumita Trivedi
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Manali Rupji
- Department of Biostatistics and Bioinformatics Shared Resource, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Haocan Song
- Department of Biostatistics and Bioinformatics Shared Resource, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Yuan Liu
- Department of Biostatistics and Bioinformatics Shared Resource, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Renjian Jiang
- Department of Biostatistics and Bioinformatics Shared Resource, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Azeem S Kaka
- Department of Otolaryngology and Head and Neck Surgery, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Georgia Z Chen
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - William Stokes
- Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Conor Steuer
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Dong M Shin
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Jonathan J Beitler
- Department of Otolaryngology and Head and Neck Surgery, Winship Cancer Institute, Emory University, Atlanta, GA, USA
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA
- Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Mihir R Patel
- Department of Otolaryngology and Head and Neck Surgery, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Ashley Aiken
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
| | - Nabil F Saba
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA
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16
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Chin O, Alshafai L, O'Sullivan B, Su J, Hope A, Bartlett E, Hansen AR, Waldron J, Chepeha D, Xu W, Huang SH, Yu E. Inter-rater concordance and operating definitions of radiologic nodal feature assessment in human papillomavirus-positive oropharyngeal carcinoma. Oral Oncol 2022; 125:105716. [PMID: 35038657 DOI: 10.1016/j.oraloncology.2022.105716] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 12/21/2021] [Accepted: 01/06/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND AND PURPOSE This study aims to evaluate the reliability of radiologic nodal feature assessment in clinical node-positive human papillomavirus-positive oropharyngeal carcinoma. MATERIALS AND METHODS Baseline CTs or MRIs of clinical node-positive human papillomavirus-positive oropharyngeal carcinoma diagnosed between 2012 and 2015 were reviewed independently by two neuroradiologists for seven nodal features: radiologic nodal involvement, cystic change, presence of necrosis, clustering, conglomeration, coalescence, and extranodal extension. Consensus operating definitions were derived after discussion. The features were re-reviewed in a randomly selected cohort. Levels of certainty (probability of presence: <25%, ∼50%, ∼75%, and >90%) were recorded. Interrater concordance was calculated using Cohen's kappa coefficient. RESULTS A total of 413 patients (826 necks) were eligible. At initial review, the inter-rater kappa values for: radiologic nodal involvement, cystic change, necrosis, clustering, conglomeration, coalescence, and extranodal extension were 0.92, 0.64, 0.48, 0.32, 0.32, 0.62, and 0.56, respectively. A re-review of 94 randomly selected cases (188 necks) after consolidation of operating definitions for nodal features showed that the inter-rater kappa values of these features were 0.83, 0.62, 0.58, 0.32, 0.18, 0.68, and 0.74 when considering ≥50% certainty as positive, and improved to 0.94, 0.66, 0.59, 0.33, 0.19, 0.76, and 0.86 when considering ≥75% certainty as positive. CONCLUSION Clearly defined nomenclature results in improved interrater reliability when assessing radiologic nodal features, especially for coalescent adenopathy and extranodal extension. Higher levels of certainty are associated with higher inter-rater agreement. Radiology reporting should include clear definitions of clinically relevant nodal features as well as levels of certainty to serve various needs in clinical care and research.
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Affiliation(s)
- Olivia Chin
- Department of Neuroradiology, University of Toronto, Toronto, Canada
| | - Laila Alshafai
- Department of Neuroradiology, University of Toronto, Toronto, Canada; Department of Neuroradiology and Head and Neck Imaging, Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada
| | - Brian O'Sullivan
- Department of Radiation Oncology, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Department of Otolaryngology - Head & Neck Surgery, University of Toronto, Toronto, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Jie Su
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Department of Biostatistics, Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada
| | - Andrew Hope
- Department of Radiation Oncology, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Eric Bartlett
- Department of Neuroradiology, University of Toronto, Toronto, Canada; Department of Neuroradiology and Head and Neck Imaging, Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada
| | - Aaron R Hansen
- Division of Medical Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada
| | - John Waldron
- Department of Radiation Oncology, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Douglas Chepeha
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Wei Xu
- Department of Biostatistics, Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada
| | - Shao Hui Huang
- Department of Radiation Oncology, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Department of Otolaryngology - Head & Neck Surgery, University of Toronto, Toronto, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.
| | - Eugene Yu
- Department of Neuroradiology, University of Toronto, Toronto, Canada; Department of Neuroradiology and Head and Neck Imaging, Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada.
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17
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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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Salzillo TC, Taku N, Wahid KA, McDonald BA, Wang J, van Dijk LV, Rigert JM, Mohamed ASR, Wang J, Lai SY, Fuller CD. Advances in Imaging for HPV-Related Oropharyngeal Cancer: Applications to Radiation Oncology. Semin Radiat Oncol 2021; 31:371-388. [PMID: 34455992 DOI: 10.1016/j.semradonc.2021.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
While there has been an overall decline of tobacco and alcohol-related head and neck cancer in recent decades, there has been an increased incidence of HPV-associated oropharyngeal cancer (OPC). Recent research studies and clinical trials have revealed that the cancer biology and clinical progression of HPV-positive OPC is unique relative to its HPV-negative counterparts. HPV-positive OPC is associated with higher rates of disease control following definitive treatment when compared to HPV-negative OPC. Thus, these conditions should be considered unique diseases with regards to treatment strategies and survival. In order to sufficiently characterize HPV-positive OPC and guide treatment strategies, there has been a considerable effort to diagnose, prognose, and track the treatment response of HPV-associated OPC through advanced imaging research. Furthermore, HPV-positive OPC patients are prime candidates for radiation de-escalation protocols, which will ideally reduce toxicities associated with radiation therapy and has prompted additional imaging research to detect radiation-induced changes in organs at risk. This manuscript reviews the various imaging modalities and current strategies for tackling these challenges as well as provides commentary on the potential successes and suggested improvements for the optimal treatment of these tumors.
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Affiliation(s)
- Travis C Salzillo
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Nicolette Taku
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Kareem A Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Brigid A McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Jarey Wang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Lisanne V van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Jillian M Rigert
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX; Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Jihong Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Stephen Y Lai
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
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Prognostic Value of Apparent Diffusion Coefficient in Oropharyngeal Carcinoma. Clin Neuroradiol 2021; 31:1037-1048. [PMID: 33877396 PMCID: PMC8648632 DOI: 10.1007/s00062-021-01014-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 03/22/2021] [Indexed: 11/24/2022]
Abstract
Purpose To investigate clinical and radiological factors predicting worse outcome after (chemo)radiotherapy ([C]RT) in oropharyngeal squamous cell carcinoma (OPSCC) with a focus on apparent diffusion coefficient (ADC). Methods This retrospective study included 67 OPSCC patients, treated with (C)RT with curative intent and diagnosed during 2013–2017. Human papilloma virus (HPV) association was detected with p16 immunohistochemistry. Of all 67 tumors, 55 were p16 positive, 9 were p16 negative, and in 3 the p16 status was unknown. Median follow-up time was 38 months. We analyzed pretreatment magnetic resonance imaging (MRI) for factors predicting disease-free survival (DFS) and locoregional recurrence (LRR), including primary tumor volume and the largest metastasis. Crude and p16-adjusted hazard ratios were analyzed using Cox proportional hazards model. Interobserver agreement was evaluated. Results Disease recurred in 13 (19.4%) patients. High ADC predicted poor DFS, but not when the analysis was adjusted for p16. A break in RT (hazard ratio, HR = 3.972, 95% confidence interval, CI 1.445–10.917, p = 0.007) and larger metastasis volume (HR = 1.041, 95% CI 1.007–1.077, p = 0.019) were associated with worse DFS. A primary tumor larger than 7 cm3 was associated with increased LRR rate (HR = 4.861, 1.042–22.667, p = 0.044). Among p16-positive tumors, mean ADC was lower in grade 3 tumors compared to lower grade tumors (0.736 vs. 0.883; p = 0.003). Conclusion Low tumor ADC seems to be related to p16 positivity and therefore should not be used independently to evaluate disease prognosis or to choose patients for treatment deintensification.
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20
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Radiological differences in computed tomography findings and texture analysis between cystic lymph node metastases of human papillomavirus-positive oropharyngeal cancer and second branchial cysts. Pol J Radiol 2021; 86:e177-e182. [PMID: 33828630 PMCID: PMC8018266 DOI: 10.5114/pjr.2021.104940] [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: 05/04/2020] [Accepted: 08/25/2020] [Indexed: 11/24/2022] Open
Abstract
Purpose The study aimed to analyse radiological differences in computed tomography (CT) findings and texture analysis between cystic lymph node metastases (CNM) in human papillomavirus (HPV)-positive oropharyngeal cancer (OPC) and second branchial cleft cysts (2nd BC). Material and methods Patients with pathological evidence of CNM-HPV-OPC and 2nd BC, who underwent contrast-enhanced CT, were retrospectively evaluated. The evaluated characteristics include age, sex, and CT findings. CT findings included the maximum and minimum transverse diameters, maximum caudal diameter, thickness of the peripheral wall, presence of internal septation, presence of surrounding fat stranding, location, and 40 texture parameters. Results A total of 13 patients had CNM-HPV-OPC (19 lesions), while 20 patients had 2nd BC (20 lesions). Patients with 2nd BC were significantly younger than those with CNM-HPV-OPC (p < 0.001). In terms of diameter, 2nd BC lesions were significantly larger than the CNM-HPV-OPC lesions (p < 0.001). CNM-HPV OPC lesions had significantly thicker walls than 2nd BC lesions (p < 0.001). CNM-HPV-OPC lesions had significantly higher association with internal septations than 2nd BC lesions (p < 0.001). Second BC lesions were significantly less common at level III than CNM-HPV-OPC lesions (p = 0.047). Among the 40 texture parameters measured, 8 had significant differences (p ≤ 0.001). Conclusions There were significant differences in CT findings and textural parameters between CNM-HPV-OPC and 2nd BC lesions. These results may help in differentiating one from the other.
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21
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Fomin I, Hughes I, Bhuta S. Oropharyngeal squamous cell carcinomas: Can imaging findings predict HPV status? J Med Imaging Radiat Oncol 2021; 65:175-181. [PMID: 33591580 DOI: 10.1111/1754-9485.13140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 09/08/2020] [Accepted: 12/03/2020] [Indexed: 11/28/2022]
Abstract
PURPOSE This is first Australian population-based retrospective study in HPV-induced OPSCCs utilising CT. Contrast enhanced computed tomography (CECT) was utilised to assess if imaging findings of metastatic cystic lymph nodes (LNs) can predict human papillomavirus (HPV) status, as defined by p16 immunostaining of oropharyngeal squamous cell carcinomas (OPSCCs). MATERIALS AND METHODS The location and p16 status of primary tumours and LNs were recorded. Metastatic LNs were assessed for size, shape, margins and cystic changes. Hounsfield Units (HU) value of primary tumours and LNs were measured and tabulated. p16-positive and p16-negative groups were compared with respect to these variables. The two-sample t test and two-sample Mann-Whitney test was used. RESULTS A total of 364 CECT scans were reviewed with 209 patients (187 p16 positive and 22 p16 negative). Primary sites of OPSCCs were tonsils (58.8%), base of tongue (37.4%) and other oropharyngeal sites (3.8%). The HU values of p16-positive OPSCCs with mean of 78.6HU; 95% CI (76.5-80.8) were lower for p16-negative tumours, mean 96.0 HU CI 95% (85.5-106.6) for all oropharyngeal sub-sites. The mean HU values of p16-positive and p16-negative metastatic LNs were 38.8 HU 95% CI: (13-103 HU) versus 88.7 HU 95% CI: (54-131) (P < 0.0001). CONCLUSIONS The association between p16-positive status and the tonsillar cancer site is very high. Imaging features of p16-positive metastatic LNs include relatively large cystic neck nodes with low HU values. This is an imaging signature of p16-positive OPSCCs and can potentially influence patient's early diagnosis and prognosis.
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Affiliation(s)
- Igor Fomin
- Department of Medical Imaging, Gold Coast University Hospital, Southport, Queensland, Australia.,School of Medicine, Griffith University, Southport, Queensland, Australia
| | - Ian Hughes
- Department of Research Governance and Development, Gold Coast University Hospital, Southport, Queensland, Australia
| | - Sandeep Bhuta
- Department of Medical Imaging, Gold Coast University Hospital, Southport, Queensland, Australia.,School of Medicine, Griffith University, Southport, Queensland, Australia
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22
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Trinh JM, Thomas J, Salleron J, Henrot P. Differences in clinical and imaging characteristics between p16-positive non-smokers and p16-positive smokers or p16-negative patients in oropharyngeal carcinoma. Sci Rep 2021; 11:3314. [PMID: 33558647 PMCID: PMC7870833 DOI: 10.1038/s41598-021-82999-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 01/25/2021] [Indexed: 11/24/2022] Open
Abstract
The eighth edition of the TNM classifies oropharyngeal squamous cell carcinomas (OSCCs) depending on p16 status. Some imaging features are reportedly associated more frequently with p16-positive (P16+) OSCC than p16-negative (p16−) OSCC. However, classical risk factors such as tobacco use were not specifically considered when assessing these imaging features. We aimed to evaluate whether P16+ OSCCs have different epidemiological, clinical, prognostic and imaging features depending on smoking status, and to compare P16+ and p16− groups. A retrospective study of data from 85 patients with P16+ OSCC (41 non-smokers, 44 smokers) and 36 with p16− OSCC from 2011 to 2020 was carried out, assessing epidemiological data, clinical aspects of the tumour and presence of adenopathy. Staging was assessed according to the seventh and eighth editions of the TNM. Compared with P16+ OSCC non-smokers, P16+ OSCC smokers had tumours that were less well-defined (36.6% vs 77.8%, p < 0.001), more ulcerated (85.4% vs 44.4%, p < 0.001) and more necrotic (53.7% vs 25%, p = 0.012). There was also less downstaging from N2 or N3 of the seventh edition of the TNM to N1 of the eighth edition for smokers than non-smokers (22.7% vs 43.9%, p = 0.042). Compared with p16− tumours, more P16+ tumours had well-defined contours (55.8% vs 22.2%, p = 0.001), were exophytic (89.6% vs 72.2%, p = 0.023), less necrotic (40.3% vs 80.6%, p < 0.001), less ulcerated (97.2% vs 66.2%, p = 0.006) and involved less muscle tissue (26.0% vs 47.2%, p = 0.027).P16+ OSCCs of smokers show clinical, imaging and prognostic differences with P16+ OSCCs of non-smokers.
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Affiliation(s)
- Jean-Michel Trinh
- Institut de Cancérologie de Lorraine, 6 Avenue de Bourgogne, 54519, Vandœuvre-lès-Nancy, France.
| | - Jacques Thomas
- Institut de Cancérologie de Lorraine, 6 Avenue de Bourgogne, 54519, Vandœuvre-lès-Nancy, France
| | - Julia Salleron
- Institut de Cancérologie de Lorraine, 6 Avenue de Bourgogne, 54519, Vandœuvre-lès-Nancy, France
| | - Philippe Henrot
- Institut de Cancérologie de Lorraine, 6 Avenue de Bourgogne, 54519, Vandœuvre-lès-Nancy, France
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Suh CH, Lee KH, Choi YJ, Chung SR, Baek JH, Lee JH, Yun J, Ham S, Kim N. Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status. Sci Rep 2020; 10:17525. [PMID: 33067484 PMCID: PMC7568530 DOI: 10.1038/s41598-020-74479-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 08/23/2020] [Indexed: 02/06/2023] Open
Abstract
We investigated the ability of machine-learning classifiers on radiomics from pre-treatment multiparametric magnetic resonance imaging (MRI) to accurately predict human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC). This retrospective study collected data of 60 patients (48 HPV-positive and 12 HPV-negative) with newly diagnosed histopathologically proved OPSCC, who underwent head and neck MRIs consisting of axial T1WI, T2WI, CE-T1WI, and apparent diffusion coefficient (ADC) maps from diffusion-weighted imaging (DWI). The median age was 59 years (the range being 35 to 85 years), and 83.3% of patients were male. The imaging data were randomised into a training set (32 HPV-positive and 8 HPV-negative OPSCC) and a test set (16 HPV-positive and 4 HPV-negative OPSCC) in each fold. 1618 quantitative features were extracted from manually delineated regions-of-interest of primary tumour and one definite lymph node in each sequence. After feature selection by using the least absolute shrinkage and selection operator (LASSO), three different machine-learning classifiers (logistic regression, random forest, and XG boost) were trained and compared in the setting of various combinations between four sequences. The highest diagnostic accuracies were achieved when using all sequences, and the difference was significant only when the combination did not include the ADC map. Using all sequences, logistic regression and the random forest classifier yielded higher accuracy compared with the that of the XG boost classifier, with mean area under curve (AUC) values of 0.77, 0.76, and 0.71, respectively. The machine-learning classifier of non-invasive and quantitative radiomics signature could guide the classification of the HPV status.
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Affiliation(s)
- Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Kyung Hwa Lee
- Department of Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.,Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Young Jun Choi
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea.
| | - Sae Rom Chung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Jung Hwan Baek
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Jeong Hyun Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Jihye Yun
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Sungwon Ham
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea. .,Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea.
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24
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Sohn B, Choi YS, Ahn SS, Kim H, Han K, Lee SK, Kim J. Machine Learning Based Radiomic HPV Phenotyping of Oropharyngeal SCC: A Feasibility Study Using MRI. Laryngoscope 2020; 131:E851-E856. [PMID: 33070337 DOI: 10.1002/lary.28889] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 05/26/2020] [Accepted: 06/08/2020] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To investigate whether a radiomic MRI feature-based prediction model can differentiate oropharyngeal squamous cell carcinoma (SCC) according to the human papillomavirus (HPV) status. STUDY DESIGN Retrospective cohort study. METHODS Pretreatment MRI data from 62 consecutive patients with oropharyngeal SCC were retrospectively reviewed, and chronologically allocated to training (n = 43) and test sets (n = 19). Enhancing tumors were semi-automatically segmented on each slice of the postcontrast T1WI to span the entire tumor volume, after registration of T2WI to postcontrast T1WI; 170 radiomic features were extracted from the entire tumor volume. Relevant features were selected and radiomics models were trained using least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation, after subsampling of training sets using synthetic minority over-sampling technique to mitigate data imbalance. The selected features, weighted by their respective coefficients, were combined linearly to yield a radiomics score. The diagnostic performance of the radiomic score was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS Six radiomic features, which revealed strong association with HPV status of oropharyngeal SCC, were selected using LASSO. The radiomics model yielded excellent performance on the training set (AUC, 0.982 [95% CI, 0.942-1.000]) and moderate performance on the test set (AUC, 0.744 [95% CI, 0.496-0.991]) for differentiating oropharyngeal SCC according to HPV status. CONCLUSIONS Radiomics-based MRI phenotyping differentiates oropharyngeal SCC according to HPV status, and thus, is a potential imaging biomarker. LEVEL OF EVIDENCE 3 Laryngoscope, 131:E851-E856, 2021.
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Affiliation(s)
- Beomseok Sohn
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Yoon Seong Choi
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Hwiyoung Kim
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Jinna Kim
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
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Garda AE, Sheedy SP, Haddock MG, Hallemeier CL. Cystic Lymph Node Metastases From HPV-Associated Squamous Cell Carcinoma of the Anal Canal. Pract Radiat Oncol 2019; 10:e111-e115. [PMID: 31866578 DOI: 10.1016/j.prro.2019.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 11/20/2019] [Accepted: 12/11/2019] [Indexed: 11/27/2022]
Affiliation(s)
- Allison E Garda
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
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Pathologic response to neoadjuvant chemotherapy in HPV‐associated oropharynx cancer. Head Neck 2019; 42:417-425. [DOI: 10.1002/hed.26022] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 10/19/2019] [Accepted: 11/06/2019] [Indexed: 12/11/2022] Open
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27
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Vidiri A, Marzi S, Gangemi E, Benevolo M, Rollo F, Farneti A, Marucci L, Spasiano F, Sperati F, Di Giuliano F, Pellini R, Sanguineti G. Intravoxel incoherent motion diffusion-weighted imaging for oropharyngeal squamous cell carcinoma: Correlation with human papillomavirus Status. Eur J Radiol 2019; 119:108640. [DOI: 10.1016/j.ejrad.2019.08.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 07/17/2019] [Accepted: 08/11/2019] [Indexed: 01/04/2023]
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28
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Billfalk-Kelly A, Yu E, Su J, O'Sullivan B, Waldron J, Ringash J, Bartlett E, Perez-Ordonez B, Weinreb I, Bayley A, Bratman SV, Cho J, Giuliani M, Hope A, Hosni A, Kim J, Hansen AR, de Almeida J, Tong L, Xu W, Huang SH. Radiologic Extranodal Extension Portends Worse Outcome in cN+ TNM-8 Stage I Human Papillomavirus–Mediated Oropharyngeal Cancer. Int J Radiat Oncol Biol Phys 2019; 104:1017-1027. [DOI: 10.1016/j.ijrobp.2019.03.047] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 03/01/2019] [Accepted: 03/23/2019] [Indexed: 11/16/2022]
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29
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Ren J, Xu W, Su J, Ren X, Cheng D, Chen Z, Bender N, Mirshams M, Habbous S, de Almeida JR, Perez-Ordonez B, Goldstein DP, Wang JR, Bratman SV, Huang SH, Jang R, Zhao Y, Waterboer T, Hung RJ, Liu G. Multiple imputation and clinico-serological models to predict human papillomavirus status in oropharyngeal carcinoma: An alternative when tissue is unavailable. Int J Cancer 2019; 146:2166-2174. [PMID: 31269236 DOI: 10.1002/ijc.32548] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 01/28/2019] [Accepted: 02/12/2019] [Indexed: 02/05/2023]
Abstract
In cancer epidemiological studies, determination of human papillomavirus (HPV) in oropharyngeal squamous cell carcinoma (OPSCC) typically depends on the availability of tumor tissue testing, and/or tumor tissue access. Identifying alternative methods for estimating HPV status can improve the quality of such studies when tissue is unavailable. We developed multiple predictive models for tumor HPV status and prognosis by combining both clinico-epidemiological variables and either serological multiplex assays of HPV or multiple imputation of HPV status (HPVmi ). Sensitivity, specificity and accuracy of these methods compared to either p16 immunostaining (p16 IHC) or survival were assessed. When compared to a reference of tumor tissue p16 IHC in 783 OPSCC patients, the clinic-HPVsero model incorporating a composite of 20 HPV serological antibodies (HPVsero ) and 4 clinical factors (c-index: 0.96) performed better than using HPVsero (c-index: 0.92) or HPVmi (c-index: 0.76) alone. However, the model that contained a single HPV16 E6 antibody combined with four clinical variables, performed extremely well (clinic-s1-16E6; c-index: 0.95). When defining HPV status by HPVsero , s1-16E6, HPVmi or through p16 IHC, each of these definitions demonstrated improved overall and disease-free survival in HPV-positive OPSCC patients, when compared to HPV-negative patients (adjusted hazard ratios between 0.25 and 0.63). Our study demonstrates that when blood samples are available, a model that utilizes a single s1-16E6 antibody combined with several clinical features has excellent test performance characteristics to estimate HPV status and prognosis. When neither blood nor tumor tissue is available, multiple imputation, calibrated on local population characteristics, remains a viable, but suboptimal option.
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Affiliation(s)
- Jianjun Ren
- Department of Otolaryngology - Head and Neck Surgery, and National Clinical Research Center for Geriatrics, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, China.,Medical Biophysics, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Wei Xu
- Department of Biostatistics, Princess Margaret Cancer Centre and Dalla Lana School of Public Health, Toronto, Ontario, Canada
| | - Jie Su
- Department of Biostatistics, Princess Margaret Cancer Centre and Dalla Lana School of Public Health, Toronto, Ontario, Canada
| | - Xue Ren
- Department of Economic Statistics, School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Dangxiao Cheng
- Medical Biophysics, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Zhuo Chen
- Medical Biophysics, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Noemi Bender
- Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Maryam Mirshams
- Medical Biophysics, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Steven Habbous
- Medical Biophysics, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - John R de Almeida
- Department of Otolaryngology- Head and Neck Surgery, Princess Margaret Cancer Centre - University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Bayardo Perez-Ordonez
- Department of Laboratory Medicine and Pathology, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - David P Goldstein
- Department of Otolaryngology- Head and Neck Surgery, Princess Margaret Cancer Centre - University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Jennifer R Wang
- Department of Otolaryngology- Head and Neck Surgery, Princess Margaret Cancer Centre - University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Scott V Bratman
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Shao Hui Huang
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Raymond Jang
- Department of Medicine, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Yu Zhao
- Department of Otolaryngology - Head and Neck Surgery, and National Clinical Research Center for Geriatrics, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, China
| | - Tim Waterboer
- Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rayjean J Hung
- Prosserman Centre for Population Health Research, Lunenfeld Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Geoffrey Liu
- Medical Biophysics, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Medicine, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
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Rock K, O’Sullivan B, Chen ZJ, Xu ZY, Li JS, Huang SH. Surgery- vs Radiation-Based Therapy for p16+/HPV-Related Oropharyngeal Cancers. CURRENT OTORHINOLARYNGOLOGY REPORTS 2018. [DOI: 10.1007/s40136-018-0214-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Leijenaar RTH, Bogowicz M, Jochems A, Hoebers FJP, Wesseling FWR, Huang SH, Chan B, Waldron JN, O'Sullivan B, Rietveld D, Leemans CR, Brakenhoff RH, Riesterer O, Tanadini-Lang S, Guckenberger M, Ikenberg K, Lambin P. Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study. Br J Radiol 2018; 91:20170498. [PMID: 29451412 PMCID: PMC6223271 DOI: 10.1259/bjr.20170498] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 01/17/2018] [Accepted: 02/12/2018] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVES Human papillomavirus (HPV) positive oropharyngeal cancer (oropharyngeal squamous cell carcinoma, OPSCC) is biologically and clinically different from HPV negative OPSCC. Here, we evaluate the use of a radiomic approach to identify the HPV status of OPSCC. METHODS Four independent cohorts, totaling 778 OPSCC patients with HPV determined by p16 were collected. We randomly assigned 80% of all data for model training (N = 628) and 20% for validation (N = 150). On the pre-treatment CT images, 902 radiomic features were calculated from the gross tumor volume. Multivariable modeling was performed using least absolute shrinkage and selection operator. To assess the impact of CT artifacts in predicting HPV (p16), a model was developed on all training data (Mall) and on the artifact-free subset of training data (Mno art). Models were validated on all validation data (Vall), and the subgroups with (Vart) and without (Vno art) artifacts. Kaplan-Meier survival analysis was performed to compare HPV status based on p16 and radiomic model predictions. RESULTS The area under the receiver operator curve for Mall and Mno art ranged between 0.70 and 0.80 and was not significantly different for all validation data sets. There was a consistent and significant split between survival curves with HPV status determined by p16 [p = 0.007; hazard ratio (HR): 0.46], Mall (p = 0.036; HR: 0.55) and Mno art (p = 0.027; HR: 0.49). CONCLUSION This study provides proof of concept that molecular information can be derived from standard medical images and shows potential for radiomics as imaging biomarker of HPV status. Advances in knowledge: Radiomics has the potential to identify clinically relevant molecular phenotypes.
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Affiliation(s)
- Ralph TH Leijenaar
- The D-Lab: Decision Support for Precision Medicine GROW - School for Oncology and Developmental Biology & MCCC Maastricht University Medical Centre+ Maastricht, Maastricht, Netherlands
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Arthur Jochems
- The D-Lab: Decision Support for Precision Medicine GROW - School for Oncology and Developmental Biology & MCCC Maastricht University Medical Centre+ Maastricht, Maastricht, Netherlands
| | - Frank JP Hoebers
- Department of Radiation Oncology, GROW- School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Frederik WR Wesseling
- Department of Radiation Oncology, GROW- School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Sophie H Huang
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada
| | - Biu Chan
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada
| | - John N Waldron
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada
| | - Brian O'Sullivan
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada
| | - Derek Rietveld
- Department of Radiation Oncology, VU University Medical Center, Amsterdam, Netherlands
| | - C Rene Leemans
- Department of Otolaryngology/Head and Neck Surgery, VU University Medical Center, Amsterdam, Netherlands
| | - Ruud H Brakenhoff
- Department of Otolaryngology/Head and Neck Surgery, VU University Medical Center, Amsterdam, Netherlands
| | - Oliver Riesterer
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Kristian Ikenberg
- Institute of Pathology and Molecular Pathology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine GROW - School for Oncology and Developmental Biology & MCCC Maastricht University Medical Centre+ Maastricht, Maastricht, Netherlands
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Huang SH, O'Sullivan B, Waldron J. The Current State of Biological and Clinical Implications of Human Papillomavirus-Related Oropharyngeal Cancer. Semin Radiat Oncol 2018; 28:17-26. [DOI: 10.1016/j.semradonc.2017.08.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Yu K, Zhang Y, Yu Y, Huang C, Liu R, Li T, Yang L, Morris JS, Baladandayuthapani V, Zhu H. Radiomic analysis in prediction of Human Papilloma Virus status. Clin Transl Radiat Oncol 2017; 7:49-54. [PMID: 29594229 PMCID: PMC5862639 DOI: 10.1016/j.ctro.2017.10.001] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Revised: 09/29/2017] [Accepted: 10/02/2017] [Indexed: 12/31/2022] Open
Abstract
Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV status. Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods. Our approach ranked the highest in the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI) grand challenge: Oropharynx Cancer (OPC) Radiomics Challenge, Human Papilloma Virus (HPV) Status Prediction. Further analysis on the most relevant radiomic features distinguishing HPV positive and negative subjects suggested that HPV positive patients usually have smaller and simpler tumors.
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Affiliation(s)
- Kaixian Yu
- Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, United States
| | - Youyi Zhang
- Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, United States
| | - Yang Yu
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, United States
| | - Chao Huang
- Department of Biostatistics, University of North Carolina at Chapel Hill, United States
| | - Rongjie Liu
- Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, United States
| | - Tengfei Li
- Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, United States
| | - Liuqing Yang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, United States
| | - Jeffrey S Morris
- Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, United States
| | | | - Hongtu Zhu
- Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, United States
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