Biology subjects

Ma, D. J.

Publications and source records attributed to Ma, D. J..

3 recordsLinked to original sources

bioRxiv2023

Integrative Spatial Analysis of H&E and IHC Images Identifies Prognostic Immune Subtypes Correlated with Progression-Free Survival in HPV-Related Oropharyngeal Squamous Cell Carcinoma

PurposeDeep learning techniques excel at identifying tumor-infiltrating lymphocytes (TILs) and immune phenotypes in hematoxylin and eosin (H&E)-stained slides. However, their ability to elucidate detailed functional characteristics of diverse cellular phenotypes within tumor immune microenvironment (TME) is limited. We aimed to enhance our understanding of cellular composition and functional characteristics across TME regions and improve patient stratification by integrating H&E with adjacent immunohistochemistry (IHC) images. MethodsA retrospective study was conducted on patients with HPV(+) oropharyngeal squamous cell carcinoma (OPSCC). Using paired H&E and IHC slides for 11 proteins, a DL pipeline was used to quantify tumor, stroma, and TILs in the TME. Patients were classified into immune inflamed (IN), immune excluded (IE), or immune desert (ID) phenotypes. By registering the IHC and H&E slides, we integrated IHC data to capture protein expression in the corresponding tumor regions. We further stratified patients into specific immune subtypes, such as IN, with increased or reduced CD8+ cells, based on the abundance of these proteins. This characterization provided functional insight into the H&E-based subtypes. ResultsAnalysis of 88 primary tumors and 70 involved lymph node tissue images revealed an improved prognosis in patients classified as IN in primary tumors with high CD8 and low CD163 expression (p = 0.007). Multivariate Cox regression analysis confirmed a significantly better prognosis for these subtypes. ConclusionsIntegrating H&E and IHC data enhances the functional characterization of immune phenotypes of the TME with biological interpretability, and improves patient stratification in HPV(+) OPSCC.

cancer biology↗

bioRxiv2022

Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning

A novel convolutional neural network based spectral registration (CNN-SR) approach is introduced to achieve efficient and accurate simultaneous frequency-and-phase correction (FPC) of single-voxel MEGA-PRESS magnetic resonance spectroscopy (MRS) data. For this approach, one neural network was trained and validated using a published simulated and in vivo MEGA-PRESS MRS dataset with a wide-range of artificial frequency (0-20 Hz) and phase (0-90{degrees}) offsets applied. The proposed CNN-SR approach was subsequently tested and compared to the sequential FPC deep learning approaches and demonstrated more effective and accurate performance. Furthermore, a large random Gaussian signal-to-noise ratio (SNR 20 and SNR 2.5) and line broadening (0-20 ms) was introduced to the original simulated dataset to investigate our model performance compared to the other deep learning models. The testing showed that CNN-SR was a more accurate quantification tool and resulted in a lower SNR when compared with the other deep learning methods, due to having smaller mean absolute errors in both frequency and phase offset predictions. For Off spectra, the CNN-SR model was capable of correcting frequency offsets with 0.014 {+/-} 0.010 Hz and phase offsets with 0.104 {+/-} 0.076{degrees} absolute errors on average for unseen simulated data with SNR 20 and correcting frequency offsets with 0.678 {+/-} 0.883 Hz and phase offsets with 2.367 {+/-} 2.616{degrees} absolute errors on average at very low SNR (2.5) and line broadening (0-20 ms) introduced. In addition, we tested the simulated dataset with additional SNR and line broadening on a more refined model (CNN-SR+) where the pre-trained CNN-SR was further optimized by minimizing the difference between individual spectra and a common template with unsupervised learning. The performance on Off spectra was improved to 0.058 {+/-} 0.050 Hz for correcting frequency offsets and to 0.416 {+/-} 0.317{degrees} for correcting phase offsets. We further evaluated the ability of our model to process the published Big GABA in vivo dataset and CNN-SR+ achieved the best performance. Moreover, additional frequency and phase offsets (i.e., small, moderate, large) were applied to the in vivo dataset, and CNN-SR+ also demonstrated better performance for FPC when compared to the other deep learning models. These results indicate the utility of using deep learning for spectral registration and demonstrate the application of unsupervised learning in further improving the model to achieve state-of-the-art performance.

neuroscience↗

bioRxiv2021

Magnetic Resonance Spectroscopy Frequency and Phase Correction Using Convolutional Neural Networks

We introduce DeepSPEC, a novel convolutional neural network (CNN) -based approach for frequency-and-phase correction (FPC) of MRS spectra to achieve fast and accurate FPC of single-voxel PRESS MRS and MEGA-PRESS data. In DeepSPEC, two neural networks, including one for frequency correction and one for phase correction were trained and validated using published simulated and in vivo PRESS and MEGA-PRESS MRS dataset with wide-range artificial frequency and phase offsets applied. DeepSPEC was subsequently tested and compared to the current deep learning solution - a "vanilla" neural network approach using multilayer perceptrons (MLP). Furthermore, random noise was added to the original simulated dataset to further investigate the model performance with noise at varied signal-to-noise (SNR) levels (i.e., 6 dB, 3 dB, and 1.5 dB). The testing showed that DeepSPEC is more robust to noise compared to the MLP-based approach due to having a smaller absolute error in both frequency and phase offset prediction. The DeepSPEC framework was capable of correcting frequency offset with 0.01{+/-}0.01 Hz and phase offset with 0.12{+/-}0.09{degrees} absolute errors on average for unseen simulated data at a high SNR (12 dB) and correcting frequency offset with 0.01{+/-}0.02 Hz and phase offset within -0.07{+/-}0.44{degrees} absolute errors on average at very low SNR (1.5 dB). Furthermore, additional frequency and phase offsets (i.e., small, moderate, large) were applied to the in vivo dataset, and DeepSPEC demonstrated better performance for FPC when compared to the MLP-based approach. Results also show DeepSPEC has superior performance than the model-based SR implementation (mSR) in FPC by having higher accuracy in a wider range of additional offsets. These results represent a proof of concept for the use of CNNs for preprocessing MRS data and demonstrate that DeepSPEC accurately predicts frequency and phase offsets at varying noise levels with state-of-the-art performance.

neuroscience↗