Publications by authors named "Venkata Sk Manem"
Article Synopsis
- Deep learning methods show strong potential for predicting lung cancer risk from CT scans, but there's a need for more comprehensive comparisons and validations of these models in real-world settings.
- The study reviews 21 state-of-the-art deep learning models, analyzing their performance using CT scans from a subset of the National Lung Screening Trial, with a focus on malignant versus benign classification.
- Results reveal that 3D deep learning models generally outperformed 2D models, with the best 3D model achieving an AUROC of 0.86 compared to 0.79 for the best 2D model, emphasizing the need to choose appropriate pretrained datasets and model types for effective lung cancer risk prediction.
Radiotherapy is a mainstay of cancer treatment. The clinical response to radiotherapy is heterogeneous, from a complete response to early progression. Recent studies have explored the importance of patient characteristics in response to radiotherapy.
View Article and Find Full Text PDFBackground: Immune checkpoint inhibitors (ICIs) have emerged as one of the most promising first-line therapeutics in the management of non-small cell lung cancer (NSCLC). However, only a subset of these patients responds to ICIs, highlighting the clinical need to develop better predictive and prognostic biomarkers. This study will leverage pre-treatment imaging profiles to develop survival risk models for NSCLC patients treated with first-line immunotherapy.
View Article and Find Full Text PDFRadiotherapy is integral to the care of a majority of patients with cancer. Despite differences in tumor responses to radiation (radioresponse), dose prescriptions are not currently tailored to individual patients. Recent large-scale cancer cell line databases hold the promise of unravelling the complex molecular arrangements underlying cellular response to radiation, which is critical for novel predictive biomarker discovery.
View Article and Find Full Text PDFObjective: Radiation therapy is among the most effective and widely used modalities of cancer therapy in current clinical practice. In this era of personalized radiation medicine, high-throughput data now provide the means to investigate novel biomarkers of radiation response. Large-scale efforts have identified several radiation response signatures, which poses two challenges, namely, their analytical validity and redundancy of gene signatures.
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