Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2025
Automated Lymph Node and Extranodal Extension Assessment Improves Risk Stratification in Oropharyngeal Carcinoma.
СтатьяМногоцентровое исследованиеПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Ye Zezhong, Mojahed-Yazdi Reza, Zapaishchykova Anna, Tak Divyanshu, Mahootiha Maryam, Pardo Juan Carlos Climent, Zielke John, Zha Yining, Guthier Christian, Tishler Roy B, Margalit Danielle N, Schoenfeld Jonathan D, Haddad Robert I, Uppaluri Ravindra, Haibe-Kains Benjamin, Fuller Clifton D, Naser Mohamed, Burtness Barbara A, Aerts Hugo J W L, Hoebers Frank, Kann Benjamin H
Аннотация
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.
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.