Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma

被引:43
作者
Haider, Stefan P. [1 ,2 ]
Zeevi, Tal [3 ]
Baumeister, Philipp [2 ]
Reichel, Christoph [2 ]
Sharaf, Kariem [2 ]
Forghani, Reza [4 ,5 ,6 ]
Kann, Benjamin H. [7 ]
Judson, Benjamin L. [8 ]
Prasad, Manju L. [9 ]
Burtness, Barbara [10 ]
Mahajan, Amit [1 ]
Payabvash, Seyedmehdi [1 ]
机构
[1] Yale Sch Med, Sect Neuroradiol, Dept Radiol & Biomed Imaging, 789 Howard Ave, New Haven, CT 06519 USA
[2] Ludwig Maximilians Univ Munchen, Dept Otorhinolaryngol, Univ Hosp, Marchioninistr 15, D-81377 Munich, Germany
[3] Yale Sch Med, Dept Radiol & Biomed Imaging, Ctr Translat Imaging Anal & Machine Learning, 333 Cedar St, New Haven, CT 06510 USA
[4] McGill Univ, Hlth Ctr, Dept Diagnost Radiol & Augmented Intelligence, 1650 Cedar Ave, Montreal, PQ H3G 1A4, Canada
[5] McGill Univ, Hlth Ctr, Precis Hlth Lab, 1650 Cedar Ave, Montreal, PQ H3G 1A4, Canada
[6] Res Inst, 1650 Cedar Ave, Montreal, PQ H3G 1A4, Canada
[7] Harvard Med Sch, Dana Farber Canc Inst, Dept Radiat Oncol, 450 Brookline Ave, Boston, MA 02215 USA
[8] Yale Sch Med, Dept Surg, Div Otolaryngol, 330 Cedar St, New Haven, CT 06520 USA
[9] Yale Sch Med, Dept Pathol, 310 Cedar St, New Haven, CT 06520 USA
[10] Yale Sch Med, Dept Internal Med, Sect Med Oncol, 25 York St, New Haven, CT 06520 USA
关键词
radiomics; oropharyngeal squamous cell carcinoma; PET; CT; quantitative imaging; HPV; imaging biomarker; survival analysis; risk stratification; head and neck cancer; PAPILLOMAVIRUS-POSITIVE HEAD; TEXTURAL FEATURES; NECK CANCERS; PREDICTION; EPIDEMIOLOGY; ROBUSTNESS; VALIDATION; SYSTEM; IMPACT; IMAGES;
D O I
10.3390/cancers12071778
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Accurate risk-stratification can facilitate precision therapy in oropharyngeal squamous cell carcinoma (OPSCC). We explored the potential added value of baseline positron emission tomography (PET)/computed tomography (CT) radiomic features for prognostication and risk stratification of OPSCC beyond the American Joint Committee on Cancer (AJCC) 8th edition staging scheme. Using institutional and publicly available datasets, we included OPSCC patients with known human papillomavirus (HPV) status, without baseline distant metastasis and treated with curative intent. We extracted 1037 PET and 1037 CT radiomic features quantifying lesion shape, imaging intensity, and texture patterns from primary tumors and metastatic cervical lymph nodes. Utilizing random forest algorithms, we devised novel machine-learning models for OPSCC progression-free survival (PFS) and overall survival (OS) using "radiomics" features, "AJCC" variables, and the "combined" set as input. We designed both single- (PET or CT) and combined-modality (PET/CT) models. Harrell's C-index quantified survival model performance; risk stratification was evaluated in Kaplan-Meier analysis. A total of 311 patients were included. In HPV-associated OPSCC, the best "radiomics" model achieved an average C-index +/- standard deviation of 0.62 +/- 0.05 (p= 0.02) for PFS prediction, compared to 0.54 +/- 0.06 (p= 0.32) utilizing "AJCC" variables. Radiomics-based risk-stratification of HPV-associated OPSCC was significant for PFS and OS. Similar trends were observed in HPV-negative OPSCC. In conclusion, radiomics imaging features extracted from pre-treatment PET/CT may provide complimentary information to the current AJCC staging scheme for survival prognostication and risk-stratification of HPV-associated OPSCC.
引用
收藏
页码:1 / 16
页数:16
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