Radiomic biomarkers of locoregional recurrence: prognostic insights from oral cavity squamous cell carcinoma preoperative CT scans

被引:1
作者
Ling, Xiao [1 ]
Alexander, Gregory S. [2 ]
Molitoris, Jason [1 ]
Choi, Jinhyuk [3 ]
Schumaker, Lisa [4 ]
Tran, Phuoc [1 ]
Mehra, Ranee [4 ]
Gaykalova, Daria [5 ,6 ,7 ]
Ren, Lei [1 ]
机构
[1] Univ Maryland, Sch Med, Dept Radiat Oncol, Baltimore, MD 20742 USA
[2] Thomas Jefferson Univ, Dept Radiat Oncol, Philadelphia, PA USA
[3] Kosin Univ, Div Breast Surg, Gospel Hosp, Busan, South Korea
[4] Univ Maryland, Sch Med, Marlene & Stewart Greenebaum Comprehens Canc Ctr, Baltimore, MD USA
[5] Univ Maryland, Sch Med, Inst Genome Sci, Baltimore, MD USA
[6] Univ Maryland, Marlene & Stewart Greenebaum Comprehens Canc Ctr, Med Ctr, Dept Otorhinolaryngol Head & Neck Surg, Baltimore, MD USA
[7] Johns Hopkins Univ, Sidney Kimmel Comprehens Canc Ctr, Dept Oncol, Baltimore, MD USA
来源
FRONTIERS IN ONCOLOGY | 2024年 / 14卷
基金
美国国家卫生研究院;
关键词
oral cavity squamous cell carcinoma; outcome prediction; biomarker; recurrence; classification; logistic regression; CT; radiomics; LYMPH-NODE METASTASIS; STAGE-I; HISTOPATHOLOGIC PARAMETERS; PREDICTIVE-VALUE; CANCER; HEAD; TONGUE; EXPRESSION; SURVIVAL; FEATURES;
D O I
10.3389/fonc.2024.1380599
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Introduction: This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients. Methods: Computed tomography scans were collected from 78 patients with OSCC who underwent surgical treatment at a single medical center. We extracted 1,092 radiomic features from gross tumor volume in each patient's pre-treatment CT. Clinical characteristics were also obtained, including race, sex, age, tobacco and alcohol use, tumor staging, and treatment modality. A feature selection algorithm was used to eliminate the most redundant features, followed by a selection of the best subset of the Logistic regression model (LRM). The best LRM model was determined based on the best prediction accuracy in terms of the area under Receiver operating characteristic curve. Finally, significant radiomic features in the final LRM model were identified as imaging biomarkers. Results and discussion: Two radiomics biomarkers, Large Dependence Emphasis (LDE) of the Gray Level Dependence Matrix (GLDM) and Long Run Emphasis (LRE) of the Gray Level Run Length Matrix (GLRLM) of the 3D Laplacian of Gaussian (LoG sigma=3), have demonstrated the capability to preoperatively distinguish patients with and without LR, exhibiting exceptional testing specificity (1.00) and sensitivity (0.82). The group with LRE > 2.99 showed a 3-year recurrence-free survival rate of 0.81, in contrast to 0.49 for the group with LRE <= 2.99. Similarly, the group with LDE > 120 showed a rate of 0.82, compared to 0.49 for the group with LDE <= 120. These biomarkers broaden our understanding of using radiomics to predict OSCC progression, enabling personalized treatment plans to enhance patient survival.
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页数:17
相关论文
共 83 条
[41]   Head and neck cancer [J].
Licitra, L ;
Locati, LD ;
Bossi, P .
ANNALS OF ONCOLOGY, 2004, 15 :267-273
[42]  
Ling X., 2020, L1-norm regularized L1-norm best-fit line problem
[43]   Identification of CT-based non-invasive radiomic biomarkers for overall survival prediction in oral cavity squamous cell carcinoma [J].
Ling, Xiao ;
Alexander, Gregory S. ;
Molitoris, Jason ;
Choi, Jinhyuk ;
Schumaker, Lisa ;
Mehra, Ranee ;
Gaykalova, Daria A. ;
Ren, Lei .
SCIENTIFIC REPORTS, 2023, 13 (01)
[44]  
Ling X, 2024, OPTIM LETT, V18, P2133, DOI 10.1007/s11590-023-02051-3
[45]   Exploiting salivary miR-31 as a clinical biomarker of oral squamous cell carcinoma [J].
Liu, Chung-Ji ;
Lin, Shu-Chun ;
Yang, Cheng-Chieh ;
Cheng, Hui-Wen ;
Chang, Kuo-Wei .
HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK, 2012, 34 (02) :219-224
[46]  
Lubab A., 2019, ADV SCI, V4, P115, DOI [10.25046/astesj, DOI 10.25046/ASTESJ]
[47]  
Marr D., 1982, Vision, P54
[48]   Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma [J].
Martens, Roland M. ;
Koopman, Thomas ;
Noij, Daniel P. ;
Pfaehler, Elisabeth ;
Ubelhor, Caroline ;
Sharma, Sughandi ;
Vergeer, Marije R. ;
Leemans, C. Rene ;
Hoekstra, Otto S. ;
Yaqub, Maqsood ;
Zwezerijnen, Gerben J. ;
Heymans, Martijn W. ;
Peeters, Carel F. W. ;
de Bree, Remco ;
de Graaf, Pim ;
Castelijns, Jonas A. ;
Boellaard, Ronald .
EJNMMI RESEARCH, 2020, 10 (01)
[49]  
Mazzaschi G., 2019, Ann Oncol, V30, pii1, DOI [10.1093/annonc/mdz072.001, DOI 10.1093/ANNONC/MDZ072.001]
[50]  
Menard S., 2002, APPL LOGISTIC REGRES, DOI [10.4135/9781412983433, DOI 10.4135/9781412983433]