Identification of CT-based non-invasive radiomic biomarkers for overall survival prediction in oral cavity squamous cell carcinoma

Ling, Xiao; Alexander, Gregory S.; Molitoris, Jason; Choi, Jinhyuk; Schumaker, Lisa; Mehra, Ranee; Gaykalova, Daria A.; Ren, Lei

doi:10.1038/s41598-023-48048-x

Cited by 8 publications

(2 citation statements)

References 63 publications

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“…A study ( 47 ) found a significant association between PET/CT radiomic features and Head and Neck locoregional recurrence. Our pilot study ( 48 ) demonstrated two potential Radiomic overall survival biomarkers. However, the identification of non-invasive factors for 2-year locoregional recurrence after primary surgery in OSCC patients remains lacking.…”

Section: Introductionmentioning

confidence: 95%

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

Ling,

Alexander,

Molitoris

et al. 2024

Front. Oncol.

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IntroductionThis study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients.MethodsComputed 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 discussionTwo 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 σ=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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Section: Introductionmentioning

confidence: 95%

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

Ling,

Alexander,

Molitoris

et al. 2024

Front. Oncol.

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“…A crucial task given the complex anatomy of the head and neck where traditional biopsy may be challenging. Radiomic signatures are not limited to identification; they have potential in correlating with treatment responses [ 21 , 22 ], prognostic outcomes [ 23 , 24 ], and even genetic expressions [ 25 ]. Such insights are invaluable in customizing treatment for head and neck cancer patients.…”

Section: Introductionmentioning

confidence: 99%

Multimodality radiomics for tumor prognosis in nasopharyngeal carcinoma

Khongwirotphan,

Oonsiri,

Kitpanit

et al. 2024

PLoS ONE

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Background The prognosis of nasopharyngeal carcinoma (NPC) is challenging due to late-stage identification and frequently undetectable Epstein-Barr virus (EBV) DNA. Incorporating radiomic features, which quantify tumor characteristics from imaging, may enhance prognosis assessment. Purpose To investigate the predictive power of radiomic features on overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) in NPC. Materials and methods A retrospective analysis of 183 NPC patients treated with chemoradiotherapy from 2010 to 2019 was conducted. All patients were followed for at least three years. The pretreatment CT images with contrast medium, MR images (T1W and T2W), as well as gross tumor volume (GTV) contours, were used to extract radiomic features using PyRadiomics v.2.0. Robust and efficient radiomic features were chosen using the intraclass correlation test and univariate Cox proportional hazard regression analysis. They were then combined with clinical data including age, gender, tumor stage, and EBV DNA level for prognostic evaluation using Cox proportional hazard regression models with recursive feature elimination (RFE) and were optimized using 20 repetitions of a five-fold cross-validation scheme. Results Integrating radiomics with clinical data significantly enhanced the predictive power, yielding a C-index of 0.788 ± 0.066 to 0.848 ± 0.079 for the combined model versus 0.745 ± 0.082 to 0.766 ± 0.083 for clinical data alone (p<0.05). Multimodality radiomics combined with clinical data offered the highest performance. Despite the absence of EBV DNA, radiomics integration significantly improved survival predictions (C-index ranging from 0.770 ± 0.070 to 0.831 ± 0.083 in combined model versus 0.727 ± 0.084 to 0.734 ± 0.088 in clinical model, p<0.05). Conclusions The combination of multimodality radiomic features from CT and MR images could offer superior predictive performance for OS, PFS, and DMFS compared to relying on conventional clinical data alone.

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