Radiation Medicine Commons^™

Radiomic Biomarkers Of Locoregional Recurrence: Prognostic Insights From Oral Cavity Squamous Cell Carcinoma Preoperative Ct Scans, Xiao Ling, Gregory S. Alexander, Jason Molitoris, Jinhyuk Choi, Lisa Schumaker, Phuoc Tran, Ranee Mehra, Daria Gaykalova, Lei Ren

Department of Radiation Oncology Faculty Papers

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 …

Preoperative 18f-Fdg Pet/Ct And Ct Radiomics For Identifying Aggressive Histopathological Subtypes In Early Stage Lung Adenocarcinoma, Wookjin Choi, Chia-Ju Liu, Sadegh Riyahi Alam, Jung Hun Oh, Raj Vaghjiani, John Humm, Wolfgang Weber, Prasad Adusumilli, Joseph Deasy, Wei Lu

Department of Radiation Oncology Faculty Papers

Lung adenocarcinoma (ADC) is the most common non-small cell lung cancer. Surgical resection is the primary treatment for early-stage lung ADC while lung-sparing surgery is an alternative for non-aggressive cases. Identifying histopathologic subtypes before surgery helps determine the optimal surgical approach. Predominantly solid or micropapillary (MIP) subtypes are aggressive and associated with a higher likelihood of recurrence and metastasis and lower survival rates. This study aims to non-invasively identify these aggressive subtypes using preoperative ¹⁸F-FDG PET/CT and diagnostic CT radiomics analysis. We retrospectively studied 119 patients with stage I lung ADC and tumors ≤ 2 cm, where 23 had …

Advances In The Radiological Evaluation Of And Theranostics For Glioblastoma, Grayson W Hooper, Shehbaz Ansari, Jason M Johnson, Daniel T Ginat

Student and Faculty Publications

Imaging is essential for evaluating patients with glioblastoma. Traditionally a multimodality undertaking, CT, including CT cerebral blood profusion, PET/CT with traditional fluorine-18 fluorodeoxyglucose (18F-FDG), and MRI have been the mainstays for diagnosis and post-therapeutic assessment. However, recent advances in these modalities, in league with the emerging fields of radiomics and theranostics, may prove helpful in improving diagnostic accuracy and treating the disease.

Investigating The Uncertainties In Ct Non-Small Cell Lung Cancer Radiomics, Gary Ge

Theses and Dissertations--Radiation Medicine

Radiomics is a technique that extracts quantitative features, termed radiomic features, from medical images using data-characterization algorithms. These radiomic features can be used to identify tissue characteristics and radiologic phenotyping that are not observable by clinicians in a non-invasive, low-cost manner, potentially generating image biomarkers for clinical decision. To date, there are still many uncertainties involved in radiomics which limit its clinical implementation. Herein, we propose to explore the impact of each component in the radiomics pipeline on predicting clinical outcomes. In Chapter II, we conduct a thorough review of CT lung cancer radiomics studies to examine the typical feature …

Sub-Region Based Radiomics Analysis For Survival Prediction In Oesophageal Tumours Treated By Definitive Concurrent Chemoradiotherapy, Congying Xie, Pengfei Yang, Xuebang Zhang, Lei Xu, Xiaoju Wang, Xiadong Li, Luhan Zhang, Ruifei Xie, Ling Yang, Zhao Jing, Hongfang Zhang, Lingyu Ding, Yu Kuang, Tianye Niu, Shixiu Wu

Health Physics & Diagnostic Sciences Faculty Publications

Background: Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. Methods: Independent patient cohorts from two hospitals were included for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for ‘Cox’ method was used for feature selection. The survival prediction model …

Radiomics - Using Artificial Intelligence In The Quest Towards Personalised Radiation Treatment, Ahmed Nadeem Abbasi, Agha Muhammad Hammad Khan, Bilal Mazhar Qureshi

Department of Radiation Oncology

No abstract provided.