Publications by authors named "C Conlin"
The Restriction Spectrum Imaging restriction score (RSIrs) has been shown to improve the accuracy for diagnosis of clinically significant prostate cancer (csPCa) compared to standard DWI. Both diffusion and T properties of prostate tissue contribute to the signal measured in DWI, and studies have demonstrated that each may be valuable for distinguishing csPCa from benign tissue. The purpose of this retrospective study was to (1) determine whether prostate T varies across RSI compartments and in the presence of csPCa, and (2) evaluate whether csPCa detection with RSIrs is improved by acquiring multiple scans at different TEs to measure compartmental T (cT).
View Article and Find Full Text PDFIntroduction: Structural stigma has important health implications for sexual minority individuals, including alcohol and tobacco use, and mental health. This study examined associations of structural stigma with alcohol and tobacco use and internalizing symptoms while considering sexual identity changes and multiple dimensions of sexual orientation among adolescents and adults in the U.S.
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- The study aims to create a calibration technique to standardize echo times (TE) for using restriction spectrum imaging (RSI) as a biomarker for detecting significant prostate cancer.
- Researchers analyzed data from 197 patients, with a focus on 97 diagnosed with clinically significant prostate cancer, to compare RSI measurements taken at different TE values.
- Results showed that calibration significantly reduced errors in RSI measurements, improving sensitivity to 66% and specificity to 72% in classifying clinically significant prostate cancer.
Article Synopsis
- The study investigates the effectiveness of a specialized breast MRI technique (BS-RSI3C) to differentiate between cancerous lesions and benign ones in women at high risk for breast cancer.
- Researchers used a specific type of MRI on a group of 187 women, focusing on those with additional imaging recommendations or high-risk profiles before biopsies.
- Results showed significant differences in MRI signal characteristics among various types of lesions, indicating potential improvements in identifying cancerous versus benign lesions through this advanced imaging method.
Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free survival (PFS). Materials and Methods This retrospective study included 1397 MRI scans in 1297 patients with glioblastoma, including an internal set of 243 MRI scans (January 2010 to June 2022) for model training and cross-validation and four external test cohorts. Cellular tumor maps were segmented by two radiologists on the basis of imaging, clinical history, and pathologic findings.
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