Dr. Lin Li Profile

Dr. Lin Li

Student at Case Western Reserve Univ

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Publications (2)

Proceedings Article | 13 March 2019 Paper

Radiomic features derived from pre-operative multi-parametric MRI of prostate cancer are associated with Decipher risk score

Lin Li, Rakesh Shiradkar, Ahmad Algohary, Patrick Leo, Cristina Magi-Galluzzi, Eric Klein, Andrei Purysko, Anant Madabhushi

Proceedings Volume 10950, 109503Y (2019) https://doi.org/10.1117/12.2512606

KEYWORDS: Feature extraction, Prostate cancer, Cancer, Magnetic resonance imaging, Pathology, Prostate, Tissues, Diffusion, Image registration

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Decipher, a genomic test, is used to predict the likelihood of metastasis and prostate cancer (PCa) specific mortality based on expression patterns of 22 RNA markers from radical prostatectomy (RP) specimens. It has been shown to be strongly correlated with metastasis-free prognosis and has been integrated with the National Comprehensive Cancer Network (NCCN) guidelines. However, Decipher is expensive and tissue destructive. Radiomic features refer to the high-throughput computational texture or shape features extracted from radiographic scans. Radiomic features derived from multi-parametric magnetic resonance imaging (mpMRI) of prostate cancer have been shown to be associated with clinically significant PCa. In this study, we sought to evaluate whether radiomic features derived from T2-weighted MRI (T2WI) and apparent diffusion coefficient (ADC) maps of the prostate could distinguish different Decipher risk groups (low, intermediate and high). We also explored correlations between Decipher risk associated radiomic features and features relating to gland morphology on corresponding digitized surgical specimens. A retrospectively acquired, de-identified cohort of 70 PCa patients (N = 74 lesions) who underwent 3T mpMRI prior to RP and Decipher tests after RP were used in this study. The Decipher risk score, ranging from 0 to 1, was used to categorize patients into low/intermediate (D₁) and high (D₂) risk groups. A multivariate logistic regression model was trained (N = 37 lesions) using radiomic features selected via elastic-net regularization to predict the Decipher risk groups. The model was evaluated on a hold-out test set (N = 37 lesions) and resulted in an area under the receiver operating characteristic curve (AUC) = 0:80. Our model outperformed the prediction using PIRADS v2 (AUC = 0:67), but showed comparable performance with Gleason Grade Group (GGG) (AUC = 0:80). We observed that the best discriminating radiomic features were correlated with gland morphology and gland packing on corresponding histopathology (R = 0.43, p < 0.05).

Proceedings Article | 21 March 2016 Paper

Multi-modality registration via multi-scale textural and spectral embedding representations

Lin Li, Mirabela Rusu, Satish Viswanath, Gregory Penzias, Shivani Pahwa, Jay Gollamudi, Anant Madabhushi

Proceedings Volume 9784, 978446 (2016) https://doi.org/10.1117/12.2217639

KEYWORDS: Image registration, Magnetic resonance imaging, Independent component analysis, Cancer, Feature extraction, Chromium, Prostate, Image segmentation, In vivo imaging, Pathology

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Intensity-based similarity measures assume that the original signal intensity of different modality images can provide statistically consistent information regarding the two modalities to be co-registered. In multi-modal registration problems, however, intensity-based similarity measures are often inadequate to identify an optimal transformation. Texture features can improve the performance of the multi-modal co-registration by providing more similar appearance representations of the two images to be co-registered, compared to the signal intensity representations. Furthermore, texture features extracted at different length scales (neighborhood sizes) can reveal similar underlying structural attributes between the images to be co-registered similarities that may not be discernible on the signal intensity representation alone. However one limitation of using texture features is that a number of them may be redundant and dependent and hence there is a need to identify non-redundant representations. Additionally it is not clear which features at which specific scales reveal similar attributes across the images to be co-registered. To address this problem, we introduced a novel approach for multimodal co-registration that employs new multi-scale image representations. Our approach comprises 4 distinct steps: (1) texure feature extraction at each length scale within both the target and template images, (2) independent component analysis (ICA) at each texture feature length scale, and (3) spectrally embedding (SE) the ICA components (ICs) obtained for the texture features at each length scale, and finally (4) identifying and combining the optimal length scales at which to perform the co-registration. To combine and co-register across different length scales, -mutual information (-MI) was applied in the high dimensional space of spectral embedding vectors to facilitate co-registration. To validate our multi-scale co-registration approach, we aligned 45 pairs of prostate MRI and histology images corresponding to the prostate with the objective of mapping extent of prostate cancer annotated by a pathologist on the pathology onto the pre-operative MRI. The registration results showed higher correlation between template and target images with average correlation ratio of 0.927 compared to 0.914 for intensity-based registration. Additionally an improvement in the dice similarity coefficient (DSC) of 13.6% was observed for the multi-scale registration compared to intensity-based registration and an 1.26% DSC improvement compared to registration involving the best individual scale.

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