TY - JOUR
T1 - Automated Lymph Node and Extranodal Extension Assessment Improves Risk Stratification in Oropharyngeal Carcinoma
AU - Ye, Zezhong
AU - Mojahed-Yazdi, Reza
AU - Zapaishchykova, Anna
AU - Tak, Divyanshu
AU - Mahootiha, Maryam
AU - Pardo, Juan Carlos Climent
AU - Zielke, John
AU - Zha, Yining
AU - Guthier, Christian
AU - Tishler, Roy B.
AU - Margalit, Danielle N.
AU - Schoenfeld, Jonathan D.
AU - Haddad, Robert I.
AU - Uppaluri, Ravindra
AU - Haibe-Kains, Benjamin
AU - Fuller, Clifton D.
AU - Naser, Mohamed
AU - Burtness, Barbara A.
AU - Aerts, Hugo J.W.L.
AU - Hoebers, Frank
AU - Kann, Benjamin H.
N1 - Publisher Copyright:
© 2025 by American Society of Clinical Oncology
PY - 2026/2/10
Y1 - 2026/2/10
N2 - PURPOSE – Extranodal extension (ENE) is a biomarker in oropharyngeal carcinoma (OPC) but can only be diagnosed via surgical pathology. We applied an automated artificial intelligence (AI) imaging platform integrating lymph node autosegmentation with ENE prediction to determine the prognostic value of the number of predicted ENE nodes.MATERIALS AND METHODS – We conducted a multisite, retrospective study of 1, 733 OPC patients with pretreatment computed tomography who underwent definitive radiation therapy across three institutions. Malignant lymph nodes were segmented using a validated deep learning auto-segmentation model, and segmented lymph nodes were sequentially processed with a validated ENE prediction model to calculate number of nodes with AI-predicted ENE (AI-ENE) per patient. We evaluated associations of AI-ENE with disease outcomes using site-stratified, multivariable Cox regression, adjusting for human papillomavirus (HPV) status, smoking pack-years, tumor and nodal stage, age, and sex. We evaluated risk-stratification improvement when incorporating AI-ENE into the Radiation Therapy Oncology Group (RTOG)-0129 risk groupings and derived American Joint Committee on Cancer (AJCC) 8th edition staging with Uno C-indices and decision curve analyses.RESULTS – Overall, median AI-ENE node number was 1 (range, 0-6). AI-ENE node number was independently associated with poorer distant control (DC; hazard ratio [HR], 1.44 [95% CI, 1.23 to 1.69]; P < .001) and overall survival (OS; HR, 1.30 [95% CI, 1.16 to 1.46]; P < .001). Increasing AI-ENE node number was incrementally associated with worse outcome, particularly DC (P < .001). C-indices improved in the external data set when incorporating AI-ENE into RTOG-0129 groupings (OS: 0.70 v 0.65; DC: 0.65 v 0.57) and AJCC-8 stage (OS: 0.75 v 0.70; DC: 0.72 v 0.67; P < .001 for each). The largest improvements were observed among HPV-negative patients (C-index: +15% for OS, +14% for DC).CONCLUSION – Automated, AI-ENE node number is a novel risk factor for OPC that may better inform pretreatment risk stratification and decision-making.
AB - PURPOSE – Extranodal extension (ENE) is a biomarker in oropharyngeal carcinoma (OPC) but can only be diagnosed via surgical pathology. We applied an automated artificial intelligence (AI) imaging platform integrating lymph node autosegmentation with ENE prediction to determine the prognostic value of the number of predicted ENE nodes.MATERIALS AND METHODS – We conducted a multisite, retrospective study of 1, 733 OPC patients with pretreatment computed tomography who underwent definitive radiation therapy across three institutions. Malignant lymph nodes were segmented using a validated deep learning auto-segmentation model, and segmented lymph nodes were sequentially processed with a validated ENE prediction model to calculate number of nodes with AI-predicted ENE (AI-ENE) per patient. We evaluated associations of AI-ENE with disease outcomes using site-stratified, multivariable Cox regression, adjusting for human papillomavirus (HPV) status, smoking pack-years, tumor and nodal stage, age, and sex. We evaluated risk-stratification improvement when incorporating AI-ENE into the Radiation Therapy Oncology Group (RTOG)-0129 risk groupings and derived American Joint Committee on Cancer (AJCC) 8th edition staging with Uno C-indices and decision curve analyses.RESULTS – Overall, median AI-ENE node number was 1 (range, 0-6). AI-ENE node number was independently associated with poorer distant control (DC; hazard ratio [HR], 1.44 [95% CI, 1.23 to 1.69]; P < .001) and overall survival (OS; HR, 1.30 [95% CI, 1.16 to 1.46]; P < .001). Increasing AI-ENE node number was incrementally associated with worse outcome, particularly DC (P < .001). C-indices improved in the external data set when incorporating AI-ENE into RTOG-0129 groupings (OS: 0.70 v 0.65; DC: 0.65 v 0.57) and AJCC-8 stage (OS: 0.75 v 0.70; DC: 0.72 v 0.67; P < .001 for each). The largest improvements were observed among HPV-negative patients (C-index: +15% for OS, +14% for DC).CONCLUSION – Automated, AI-ENE node number is a novel risk factor for OPC that may better inform pretreatment risk stratification and decision-making.
UR - https://www.scopus.com/pages/publications/105029627715
UR - https://www.scopus.com/pages/publications/105029627715#tab=citedBy
U2 - 10.1200/JCO-24-02679
DO - 10.1200/JCO-24-02679
M3 - Article
C2 - 41435207
AN - SCOPUS:105029627715
SN - 0732-183X
VL - 44
SP - 386
EP - 399
JO - Journal of Clinical Oncology
JF - Journal of Clinical Oncology
IS - 5
ER -