Ji Young Lee1, Miran Han2, Kap Seon Kim3, Su-Jin Shin4, Jin Wook Choi3, Eun Ju Ha3. 1. Department of Radiology, Hanyang University Medical Center, Seoul, Republic of Korea. 2. Department of Radiology, Ajou University School of Medicine, Ajou University Medical Center, 164, World Cup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea. miranhanajou@gmail.com. 3. Department of Radiology, Ajou University School of Medicine, Ajou University Medical Center, 164, World Cup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea. 4. Department of Pathology, Hanyang University Medical Center, Seoul, Republic of Korea.
Abstract
PURPOSE: To evaluate the diagnostic performance of texture analysis for discriminating human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC) in the primary tumours and metastatic lymph nodes. METHODS: Ninety-five patients with primary tumour and 91 with metastatic lymph nodes with confirmed HPV status, who underwent pretreatment contrast-enhanced CT (CECT), were included as the discovery population. CT texture analysis was performed using commercially available software. Differences between HPV-positive and HPV-negative groups were analysed using the χ2 test (or Mann-Whitney U test) and independent t test (or Fisher's exact test). ROC curve analysis was performed to discriminate HPV status according to heterogeneity parameters. Diagnostic accuracy was evaluated in the separate validation population (n = 36) from an outside hospital. RESULTS: HPV positivity was 52.6% for primary tumours and 56.0% for metastatic lymph nodes. The entropy and standard deviation (SD) values in the HPV-positive group were significantly lower. Entropy using the medium filter was the best discriminator between HPV-positive and HPV-negative primary OPSCCs (AUC, 0.85) and SD without the filter for metastatic lymph nodes (AUC, 0.82). Diagnostic accuracy of entropy for the primary tumour was 80.0% in the discovery group and 75.0% in the validation group. In cases of metastatic lymph node, the accuracy of SD was 79.1% and 78.8%, respectively. CONCLUSION: Significant differences were found in heterogeneity parameters from texture analysis of pretreatment CECT, according to HPV status. Texture analysis could be used as an adjunctive tool for diagnosis of HPV status in clinical practice.
PURPOSE: To evaluate the diagnostic performance of texture analysis for discriminating human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC) in the primary tumours and metastatic lymph nodes. METHODS: Ninety-five patients with primary tumour and 91 with metastatic lymph nodes with confirmed HPV status, who underwent pretreatment contrast-enhanced CT (CECT), were included as the discovery population. CT texture analysis was performed using commercially available software. Differences between HPV-positive and HPV-negative groups were analysed using the χ2 test (or Mann-Whitney U test) and independent t test (or Fisher's exact test). ROC curve analysis was performed to discriminate HPV status according to heterogeneity parameters. Diagnostic accuracy was evaluated in the separate validation population (n = 36) from an outside hospital. RESULTS:HPV positivity was 52.6% for primary tumours and 56.0% for metastatic lymph nodes. The entropy and standard deviation (SD) values in the HPV-positive group were significantly lower. Entropy using the medium filter was the best discriminator between HPV-positive and HPV-negative primary OPSCCs (AUC, 0.85) and SD without the filter for metastatic lymph nodes (AUC, 0.82). Diagnostic accuracy of entropy for the primary tumour was 80.0% in the discovery group and 75.0% in the validation group. In cases of metastatic lymph node, the accuracy of SD was 79.1% and 78.8%, respectively. CONCLUSION: Significant differences were found in heterogeneity parameters from texture analysis of pretreatment CECT, according to HPV status. Texture analysis could be used as an adjunctive tool for diagnosis of HPV status in clinical practice.
Authors: M Ravanelli; A Grammatica; E Tononcelli; R Morello; M Leali; S Battocchio; G M Agazzi; M Buglione di Monale E Bastia; R Maroldi; P Nicolai; D Farina Journal: AJNR Am J Neuroradiol Date: 2018-09-13 Impact factor: 3.825
Authors: William M Lydiatt; Snehal G Patel; Brian O'Sullivan; Margaret S Brandwein; John A Ridge; Jocelyn C Migliacci; Ashley M Loomis; Jatin P Shah Journal: CA Cancer J Clin Date: 2017-01-27 Impact factor: 508.702
Authors: H Kuno; M M Qureshi; M N Chapman; B Li; V C Andreu-Arasa; K Onoue; M T Truong; O Sakai Journal: AJNR Am J Neuroradiol Date: 2017-10-12 Impact factor: 3.825