# From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities

arXiv: https://arxiv.org/abs/1808.07954 · HTML: https://mlsanity.com/paper/1808.07954/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2018.

## Built on by (most cited first, 18 of 18)

- [DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT](https://mlsanity.com/paper/2109.07711/) (2021)
- [Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer](https://mlsanity.com/paper/2307.03427/) (2023)
- [Prediction of 5-year Progression-Free Survival in Advanced Nasopharyngeal Carcinoma with Pretreatment PET/CT using Multi-Modality Deep Learning-based Radiomics](https://mlsanity.com/paper/2103.05220/) (2021)
- [Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attention](https://mlsanity.com/paper/2111.03848/) (2021)
- [Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics](https://mlsanity.com/paper/2104.14655/) (2021)
- [AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics](https://mlsanity.com/paper/2110.10332/) (2021)
- [A Novel Collaborative Self-Supervised Learning Method for Radiomic Data](https://mlsanity.com/paper/2302.09807/) (2023)
- [AutoRad-Lung: A Radiomic-Guided Prompting Autoregressive Vision-Language Model for Lung Nodule Malignancy Prediction](https://mlsanity.com/paper/2503.20662/) (2025)
- [Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning](https://mlsanity.com/paper/2104.11416/) (2021)
- [An automated machine learning framework to optimize radiomics model construction validated on twelve clinical applications](https://mlsanity.com/paper/2108.08618/) (2021)
- [Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling](https://mlsanity.com/paper/2203.04964/) (2022)
- [Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer](https://mlsanity.com/paper/2308.11389/) (2023)
- [Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation](https://mlsanity.com/paper/2507.08189/) (2025)
- [NYCTALE: Neuro-Evidence Transformer for Adaptive and Personalized Lung Nodule Invasiveness Prediction](https://mlsanity.com/paper/2402.10066/) (2024)
- [Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis](https://mlsanity.com/paper/2411.02426/) (2024)
- [An Explainable Neural Radiomic Sequence Model with Spatiotemporal Continuity for Quantifying 4DCT-based Pulmonary Ventilation](https://mlsanity.com/paper/2503.23898/) (2025)
- [Precise Liver Tumor Segmentation in CT Using a Hybrid Deep Learning-Radiomics Framework](https://mlsanity.com/paper/2512.07574/) (2025)
- [An Interpretable Deep Learning Framework for Discovery and Clinical Validation of Deep Radiomic Signatures in Tumor Classification](https://mlsanity.com/paper/2607.03593/) (2026)

## Abstract (arXiv)

Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of medical images through internal/external communication systems, have resulted in a recent surge of significant interest in "Radiomics". Radiomics is an emerging and relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic models, and is expected to become a critical component for integration of image-derived information for personalized treatment in the near future. The conventional Radiomics workflow is typically based on extracting pre-designed features (also referred to as hand-crafted or engineered features) from a segmented region of interest. Nevertheless, recent advancements in deep learning have caused trends towards deep learning-based Radiomics (also referred to as discovery Radiomics). Considering the advantages of these two approaches, there are also hybrid solutions developed to exploit the potentials of multiple data sources. Considering the variety of approaches to Radiomics, further improvements require a comprehensive and integrated sketch, which is the goal of this article. This manuscript provides a unique interdisciplinary perspective on Radiomics by discussing state-of-the-art signal processing solutions in the context of Radiomics.