Resident Doctor at Second Affiliated Hospital of Harbin Medical University, China
Apekshya Singh 🇳🇵 is a dedicated medical professional currently pursuing her Master’s in Medical Imaging and Nuclear Medicine 📸🧠 at Harbin Medical University, China 🇨🇳. With a background in Clinical Medicine from Changsha Medical University and a deep interest in AI-based diagnostics 🤖, she aims to transform radiology through machine learning and radiomics. Passionate about research and innovation, Apekshya is working on predictive modeling for hepatic metastasis in rectal cancer patients 🎯🧬. Her adaptability, collaborative spirit, and academic excellence 🌟 make her a promising contributor to future breakthroughs in imaging and precision medicine 🔍🧪.
Professional Profile:
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🔹 Education & Experience
📚 Education
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🎓 Master’s in Medical Imaging & Nuclear Medicine
Harbin Medical University (2023–present), China
🏥 Training at 2nd Affiliated Hospital
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🎓 MBBS in Clinical Medicine
Changsha Medical University (2013–2019), China
📖 Medium: English | 🩺 Internship: 1 year full-time rotatory
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🎓 HSEB in Science (Biology)
V.S. Niketan HSS, Kathmandu, Nepal (2009–2011)
💼 Experience
🔹 Professional Development
Apekshya Singh is committed to lifelong learning and innovation in medical imaging and diagnostics 🧠📊. Her journey from MBBS to pursuing a Master’s in Imaging Medicine showcases her strong foundation in both clinical and technological domains 🩺💻. She is currently developing expertise in radiomics and artificial intelligence to enhance cancer diagnostics, specifically focusing on hepatic metastasis prediction using machine learning models 🤖🧬. With hands-on experience in hospital rotations and research labs 🧫🏥, she thrives in interdisciplinary environments and seeks to align her work with global medical challenges 🌍. Apekshya is open to collaborative, multicultural research initiatives 🤝🌐.
🔹 Research Focus
Apekshya Singh’s research lies at the intersection of medical imaging, computational diagnostics, and cancer prediction 🎯📸🧬. Her current focus is on using radiomics and machine learning to predict hepatic metastases in rectal cancer patients, enabling early and accurate diagnoses 🧠🔍. She combines imaging data with algorithmic analysis, working on feature extraction and predictive model development 💻📊. Her broader interests include applying AI and deep learning in radiology to address diverse diagnostic challenges across diseases 🦠🩻. This interdisciplinary focus positions her in the fields of computational imaging, radiological AI, and clinical oncology research 🧪🤖🧬.
🔹 Awards & Honors
🏅 Heilongjiang Provincial Government Scholarship
– For academic excellence during Master’s program at Harbin Medical University
🏅 International Outstanding Student Scholarship
– Awarded during MBBS studies at Changsha Medical University
🏅 Full Entrance Scholarship
– Granted during enrollment at V.S. Niketan HSS, Nepal
Publication Top Notes
1. Habitat Radiomics Based on MRI for Predicting Metachronous Liver Metastasis in Locally Advanced Rectal Cancer: a Two‑center Study
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Authors: S. Shi, T. Jiang, H. Liu, Y. Wu, A. Singh, Y. Wang, J. Xie, X. Li
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Journal: Academic Radiology
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Year: 2025
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Overview: This study investigates the use of habitat radiomics features derived from pre-treatment MRI to predict the development of metachronous liver metastases in patients with locally advanced rectal cancer (LARC). It is a two-center retrospective analysis and leverages advanced image segmentation and machine learning techniques.
2. Emerging MRI Biomarkers for Prognostication in Rectal Cancer
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Authors: A. Singh, X.F. Li, S.M. Shi, H. Liu, Y. Wu, S. Nirala
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Journal: Current Cancer Therapy Reviews
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Year: 2024
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Overview: This review paper focuses on novel and emerging MRI biomarkers for prognosis and therapy response assessment in rectal cancer. It discusses conventional and radiomic features, with a focus on translating imaging biomarkers into clinical decision-making tools.
3. Maternal High Fat Diet and its Expressions in the Heart and Liver in the Mice Embryogenesis
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Authors: S. Nirala, X.R. Tan, M. Shafiq, R. Basnet, A. Singh
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Journal: Current Molecular Medicine
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Volume: 24 (7), Pages 889–898
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Year: 2024
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Overview: This experimental study investigates how a maternal high-fat diet (HFD) affects gene expression and molecular signatures in the heart and liver of mouse embryos, providing insights into early developmental metabolic programming.
4. Development and Validation of a Multi-parametric MRI Deep-learning Model for Preoperative Lymphovascular Invasion Evaluation in Rectal Cancer
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Authors: X.L. Shi Shengming, Apekshya Singh, Jiaqi Ma, Xinsheng Nie, Xiangjiang Kong, et al.
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Journal: Quantitative Imaging in Medicine and Surgery
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Year: 2024
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Overview: This paper proposes a deep learning model trained on multi-parametric MRI data to preoperatively predict lymphovascular invasion (LVI) status in rectal cancer patients. It offers a non-invasive tool for treatment planning.
5. Lipid and High Resolution Surface Chemical Patterning
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Authors: J. Moran-Mirabal, A. Singh, B. Baird, H. Craighead
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Journal: Biophysical Journal
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Volume: 86 (1), Page 34A
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Year: 2004
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Overview: This abstract/short communication from a conference issue describes techniques for surface patterning at high resolution involving lipid domains—relevant in the context of membrane biophysics and biosensor development.
Conclusion
Apekshya Singh is a very promising early-career researcher who has already made significant strides in a complex, interdisciplinary area combining medical imaging, artificial intelligence, and oncology. While she may not yet have a long list of publications (due to being early in her research career), her clear focus, strong academic record, and impactful ongoing research make her highly suitable for a Best Researcher Award (Young Researcher or Early Career category). Recognizing her now would encourage further contributions and innovation in a highly impactful field.