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Sahin O, Kamel S, Wahid KA, Dede C, Taku N, He R, Naser MA, Sharafi S, Mäkitie A, Kann BH, Kaski K, Sahlsten J, Jaskari J, Amit M, Chronowski GM, Diaz EM, Garden AS, Goepfert RP, Guenette JP, Gunn GB, Hirvonen J, Hoebers F, Hutcheson KA, Guha-Thakurta N, Johnson J, Kaya D, Khanpara SD, Nyman K, Lai SY, Lango M, Learned KO, Lee A, Lewis CM, Maniakas A, Moreno AC, Myers JN, Phan J, Pytynia KB, Rosenthal DI, Sandulache VC, Schellingerhout D, Shah SJ, Sikora AG, Mohamed ASR, Chen MM, Fuller CD. International Multi-Specialty Expert Physician Preoperative Identification of Extranodal Extension n Oropharyngeal Cancer Patients using Computed Tomography: Prospective Blinded Human Inter-Observer Performance Evaluation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.02.25.23286432. [PMID: 36865096 PMCID: PMC9980252 DOI: 10.1101/2023.02.25.23286432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Importance Extranodal extension (pENE) is a critical prognostic factor in oropharyngeal cancer (OPC) that drives therapeutic disposition. Determination of pENE from radiological imaging has been associated with high inter-observer variability. However, the impact of clinician specialty on human observer performance of imaging-detected extranodal extension (iENE) remains poorly understood. Objective To characterize the impact of clinician specialty on the accuracy of pre-operative iENE in human papillomavirus-positive (HPV+) OPC using computed tomography (CT) images. Design Setting and Participants This prospective observational human performance study analyzed pre-therapy CT images from 24 HPV+ OPC patients, with duplication of 6 scans (n=30) of which 21 were pathologically confirmed pENE. Thirty-four expert observers, including 11 radiologists, 12 surgeons, and 11 radiation oncologists, independently assessed these scans for iENE and reported human-detected radiologic criteria and observer confidence. Main Outcomes and Measures The primary outcomes included accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and Brier score for each physician, compared to ground-truth pENE. The significance of radiographic signs for prediction of pENE were determined through logistic regression analysis. Fleiss' kappa measured interobserver agreement, and Hanley-MacNeil AUC discrimination testing. Results Median accuracy across all specialties was 0.57 (95%CI 0.39 to 0.73), with no specialty showing discriminate performance greater than random estimation (median AUC 0.64, 95%CI 0.44 to 0.83). Significant differences between radiologists and surgeons in Brier scores (0.33 vs. 0.26, p < 0.01), radiation oncologists and surgeons in sensitivity (0.48 vs. 0.69, p > 0.1), and radiation oncologists and radiologists/surgeons in specificity (0.89 vs. 0.56, p > 0.1). Indistinct capsular contour and nodal necrosis were significant predictors of correct pENE status among all specialties. Interobserver agreement was weak for all the radiographic criteria, regardless of specialty (κ<0.6). Conclusions and Relevance Multiobserver testing shows physician discrimination of HPV+OPC pENE on pre-operative CT remains non-different than blind guessing, with high interrater variability and low diagnostic accuracy, regardless of clinician specialty. While minor differences in diagnostic performance among specialties are noted, they do not significantly affect the overall poor agreement and discrimination rates observed. The findings underscore the need for further research into automated detection systems or enhanced imaging techniques to improve the accuracy and reliability of iENE assessments in clinical practice.
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Affiliation(s)
| | - Onur Sahin
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Kareem A. Wahid
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Cem Dede
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Nicolette Taku
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Renjie He
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Setareh Sharafi
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Antti Mäkitie
- University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Benjamin H. Kann
- Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | | | | | | | - Moran Amit
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Eduardo M. Diaz
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Adam S. Garden
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Jeffrey P. Guenette
- Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - G. Brandon Gunn
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Jussi Hirvonen
- Tampere University, Faculty of Medicine and Health Technology and Tampere University Hospital, Tampere, Finland
| | - Frank Hoebers
- Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | | | | | - Jason Johnson
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Diana Kaya
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Kristofer Nyman
- University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Stephen Y. Lai
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Miriam Lango
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Kim O. Learned
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Anna Lee
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Carol M. Lewis
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Amy C. Moreno
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Jack Phan
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | | | - Vlad C. Sandulache
- Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, USA
| | | | - Shalin J. Shah
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Abdallah S. R. Mohamed
- The University of Texas MD Anderson Cancer Center, Houston, USA
- Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, USA
| | - Melissa M. Chen
- The University of Texas MD Anderson Cancer Center, Houston, USA
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Higaki M, Koizumi K, Ando T, Ito N, Obayashi F, Hamada A, Yanamoto S. Extranodal extension in oral squamous cell carcinoma: clinical and histopathological analysis. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 137:632-639. [PMID: 38679502 DOI: 10.1016/j.oooo.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 03/06/2024] [Accepted: 03/17/2024] [Indexed: 05/01/2024]
Abstract
OBJECTIVE Extranodal extension (ENE) is an important prognostic factor in oral squamous cell carcinoma (OSCC). This study aimed to evaluate the clinical significance of stratifying minor or major ENE in OSCC. STUDY DESIGN This retrospective cohort study included 75 patients who had undergone neck dissection for OSCC and were classified as pN+. ENE was measured using hematoxylin-eosin-stained specimens and stratified into major (ENEma, >2 mm) and minor (ENEmi, ≤2 mm) by distance. Their association with survival, locoregional relapse, and distant metastases were assessed. RESULTS Of 49 patients with pathological ENE, 23 had ENEmi, and 26 had ENEma. The 5-year overall survival (OS) was 38%, 66%, and 76% in the ENEma, ENEmi, and no ENE groups, respectively. Compared with no ENE, ENEma was associated with significantly decreased 5-year cumulative OS and disease-specific survival. ENEma was a risk factor for decreased OS (HR: 2.54, 95% CI: 1.04-6.18, P = .040) in the multivariable Cox regression analysis, and was associated with distant metastasis. CONCLUSION In patients with OSCC, ENEma is associated with a significantly poorer prognosis; therefore stratifying ENE is clinically relevant. ENEma may increase the risk of distant metastasis; therefore, new treatment modalities that contribute to distant metastasis control are required.
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Affiliation(s)
- Mirai Higaki
- Department of Oral Oncology, Division of Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Minami-ku, Hiroshima Japan.
| | - Koichi Koizumi
- Department of Oral Oncology, Division of Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Minami-ku, Hiroshima Japan
| | - Toshinori Ando
- Center for Oral Clinical Examination, Hiroshima University Hospital, Minami-ku, Hiroshima, Japan
| | - Nanako Ito
- Department of Oral Oncology, Division of Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Minami-ku, Hiroshima Japan
| | - Fumitaka Obayashi
- Department of Oral Oncology, Division of Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Minami-ku, Hiroshima Japan
| | - Atsuko Hamada
- Department of Oral Oncology, Division of Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Minami-ku, Hiroshima Japan
| | - Souichi Yanamoto
- Department of Oral Oncology, Division of Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Minami-ku, Hiroshima Japan
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Bradley PJ. Extranodal extension in head and neck squamous cell carcinoma: need for accurate pretherapeutic staging to select optimum treatment and minimize toxicity. Curr Opin Otolaryngol Head Neck Surg 2024; 32:71-80. [PMID: 38116845 DOI: 10.1097/moo.0000000000000956] [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: 12/21/2023]
Abstract
PURPOSE OF REVIEW In 2017, the American Joint Committee on Cancer (AJCC) introduced the inclusion of extracapsular nodal extension (ENE) into the N staging of nonviral head and neck squamous cell carcinoma (HNSCC), while retaining the traditional N classification based on the number and sizes of metastatic nodes. The extent of ENE was further defined as microscopic ENE (ENEmi) and major ENE (ENEma) based on extent of disease beyond the nodal capsule (≤ or > 2 mm). This article reviews the evidence and progress made since these changes were introduced. RECENT FINDINGS The 'gold standard' for evaluation ENE is histopathologic examination, the current preferred primary treatment of patients with HNSCC is by radiation-based therapy ± chemotherapy or biotherapy. The current pretreatment staging is by imaging, which needs improved reliability of radiologic rENE assessment with reporting needs to consider both sensitivity and specificity (currently computed tomography images have high-specificity but low-sensitivity). Adjuvant chemotherapy is indicated for patients with ENEma to enhance disease control, whereas for patients with ENEmi, there is a need to assess the benefit of adjuvant chemotherapy. Evidence that the presence of pENE in HPV-positive oropharyngeal carcinoma is an independent prognostic factor and should be considered for inclusion in future AJCC editions has recently emerged. SUMMARY There remains a paucity of data on the reliability of imaging in the staging of rENE, more so the for the accurate assessment of ENEmi. Optimistic early results from use of artificial intelligence/deep learning demonstrate progress and may pave the way for better capabilities in tumor staging, treatment outcome prediction, resulting in improved survival outcomes.
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Affiliation(s)
- Patrick J Bradley
- Department of Otorhinolaryngology, Head and Neck Surgery, Nottingham University Hospitals, Queens Medical Centre Campus, Nottingham, UK
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Qu J, Su T, Pan B, Zhang T, Chen X, Zhu X, Chen Y, Zhang Z, Jin Z. Free-Breathing StarVIBE Sequence for the Detection of Extranodal Extension in Head and Neck Cancer: An Image Quality and Diagnostic Performance Study. Cancers (Basel) 2023; 15:4992. [PMID: 37894359 PMCID: PMC10605568 DOI: 10.3390/cancers15204992] [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: 07/19/2023] [Revised: 09/27/2023] [Accepted: 10/02/2023] [Indexed: 10/29/2023] Open
Abstract
(1) Background: This study aims to evaluate the image quality of abnormal cervical lymph nodes in head and neck cancer and the diagnostic performance of detecting extranodal extension (ENE) using free-breathing StarVIBE. (2) Methods: In this retrospective analysis, 80 consecutive head and neck cancer patients underwent StarVIBE before neck dissection at an academic center. Image quality was compared with conventional VIBE available for 28 of these patients. A total of 73 suspicious metastatic lymph nodes from 40 patients were found based on morphology and enhancement pattern on StarVIBE. Sensitivity (SN), specificity (SP), and odds ratios were calculated for each MR feature from StarVIBE to predict pathologic ENE. (3) Results: StarVIBE showed significantly superior image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for enlarged lymph nodes compared to VIBE. The MR findings of "invading adjacent planes" (SN, 0.54; SP, 1.00) and "matted nodes" (SN, 0.72; SP, 0.89) emerged as notable observations. The highest diagnostic performance was attained by combining these two features (SN, 0.93; SP, 0.89). (4) Conclusions: This study confirms that StarVIBE offers superior image quality for abnormal lymph nodes compared to VIBE, and it can accurately diagnose ENE by utilizing a composite MR criterion in head and neck cancer.
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Affiliation(s)
- Jiangming Qu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China
| | - Tong Su
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China
| | - Boju Pan
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China
| | - Tao Zhang
- Department of Stomatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China
| | - Xingming Chen
- Department of Otolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China
| | - Xiaoli Zhu
- Department of Otolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China
| | - Yu Chen
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China
| | - Zhuhua Zhang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China
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Huang TT, Lin YC, Yen CH, Lan J, Yu CC, Lin WC, Chen YS, Wang CK, Huang EY, Ho SY. Prediction of extranodal extension in head and neck squamous cell carcinoma by CT images using an evolutionary learning model. Cancer Imaging 2023; 23:84. [PMID: 37700385 PMCID: PMC10496246 DOI: 10.1186/s40644-023-00601-7] [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: 05/02/2023] [Accepted: 08/08/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Extranodal extension (ENE) in head and neck squamous cell carcinoma (HNSCC) correlates to poor prognoses and influences treatment strategies. Deep learning may yield promising performance of predicting ENE in HNSCC but lack of transparency and interpretability. This work proposes an evolutionary learning method, called EL-ENE, to establish a more interpretable ENE prediction model for aiding clinical diagnosis. METHODS There were 364 HNSCC patients who underwent neck lymph node (LN) dissection with pre-operative contrast-enhanced computerized tomography images. All the 778 LNs were divided into training and test sets with the ratio 8:2. EL-ENE uses an inheritable bi-objective combinatorial genetic algorithm for optimal feature selection and parameter setting of support vector machine. The diagnostic performances of the ENE prediction model and radiologists were compared using independent test datasets. RESULTS The EL-ENE model achieved the test accuracy of 80.00%, sensitivity of 81.13%, and specificity of 79.44% for ENE detection. The three radiologists achieved the mean diagnostic accuracy of 70.4%, sensitivity of 75.6%, and specificity of 67.9%. The features of gray-level texture and 3D morphology of LNs played essential roles in predicting ENE. CONCLUSIONS The EL-ENE method provided an accurate, comprehensible, and robust model to predict ENE in HNSCC with interpretable radiomic features for expanding clinical knowledge. The proposed transparent prediction models are more trustworthy and may increase their acceptance in daily clinical practice.
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Affiliation(s)
- Tzu-Ting Huang
- Department of Radiation Oncology and Proton & Radiation Therapy Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 129, Dapi Road, Niaosong District, Kaohsiung, Taiwan
- Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, No. 1001 University Road, Hsinchu, Taiwan
| | - Yi-Chen Lin
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, No. 75 Po- Ai Street, Hsinchu, Taiwan
| | - Chia-Heng Yen
- Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, No. 1001 University Road, Hsinchu, Taiwan
| | - Jui Lan
- Department of Anatomic Pathology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Dapi Road, Niaosong District, Kaohsiung, Taiwan
| | - Chiun-Chieh Yu
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Dapi Road, Niaosong District, Kaohsiung, Taiwan
| | - Wei-Che Lin
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Dapi Road, Niaosong District, Kaohsiung, Taiwan
| | - Yueh-Shng Chen
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Dapi Road, Niaosong District, Kaohsiung, Taiwan
| | - Cheng-Kang Wang
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Dapi Road, Niaosong District, Kaohsiung, Taiwan
| | - Eng-Yen Huang
- Department of Radiation Oncology and Proton & Radiation Therapy Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 129, Dapi Road, Niaosong District, Kaohsiung, Taiwan.
- School of Medicine, College of Medicine, National Sun Yat-sen University, No. 70, Lienhai Rd, 80424, Kaohsiung, Taiwan.
| | - Shinn-Ying Ho
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, No. 75 Po- Ai Street, Hsinchu, Taiwan.
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, No. 1001 University Road, Hsinchu, Taiwan.
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS 2 B), National Yang Ming Chiao Tung University, No. 75 Po-Ai Street, Hsinchu, Taiwan.
- College of Health Sciences, Kaohsiung Medical University, No. 100, Shih-Chuan 1st Road, Sanmin District, Kaohsiung, Taiwan.
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