A Survey on Deep Learning for Precision Oncology

Overview

Journal Diagnostics (Basel)

Specialty Radiology

Date 2022 Jun 24

PMID 35741298

Authors

Ching-Wei Wang

Muhammad-Adil Khalil

Nabila Puspita Firdi

Affiliations

Soon will be listed here.

Abstract

Precision oncology, which ensures optimized cancer treatment tailored to the unique biology of a patient's disease, has rapidly developed and is of great clinical importance. Deep learning has become the main method for precision oncology. This paper summarizes the recent deep-learning approaches relevant to precision oncology and reviews over 150 articles within the last six years. First, we survey the deep-learning approaches categorized by various precision oncology tasks, including the estimation of dose distribution for treatment planning, survival analysis and risk estimation after treatment, prediction of treatment response, and patient selection for treatment planning. Secondly, we provide an overview of the studies per anatomical area, including the brain, bladder, breast, bone, cervix, esophagus, gastric, head and neck, kidneys, liver, lung, pancreas, pelvis, prostate, and rectum. Finally, we highlight the challenges and discuss potential solutions for future research directions.

Citing Articles

Innovation and challenges of artificial intelligence technology in personalized healthcare.

Li Y, Li Y, Wei M, Li G Sci Rep. 2024; 14(1):18994.

PMID: 39152194 PMC: 11329630. DOI: 10.1038/s41598-024-70073-7.

The Feasibility and Performance of Total Hip Replacement Prediction Deep Learning Algorithm with Real World Data.

Chen C, Huang J, Lin W, Cheng C, Chen S, Fu C Bioengineering (Basel). 2023; 10(4).

PMID: 37106645 PMC: 10136253. DOI: 10.3390/bioengineering10040458.

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.

Zhao Y, Zhang J, Hu D, Qu H, Tian Y, Cui X Micromachines (Basel). 2022; 13(12).

PMID: 36557496 PMC: 9781697. DOI: 10.3390/mi13122197.

Artificial intelligence in pancreatic cancer.

Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J Theranostics. 2022; 12(16):6931-6954.

PMID: 36276650 PMC: 9576619. DOI: 10.7150/thno.77949.

References

Maspero M, Savenije M, Dinkla A, Seevinck P, Intven M, Jurgenliemk-Schulz I . Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy. Phys Med Biol. 2018; 63(18):185001. DOI: 10.1088/1361-6560/aada6d. View

Ding M, Chen L, Cooper G, Young J, Lu X . Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics. Mol Cancer Res. 2017; 16(2):269-278. PMC: 5821274. DOI: 10.1158/1541-7786.MCR-17-0378. View

Gronberg M, Gay S, Netherton T, Rhee D, Court L, Cardenas C . Technical Note: Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture. Med Phys. 2021; 48(9):5567-5573. DOI: 10.1002/mp.14827. View

Nikolov S, Blackwell S, Zverovitch A, Mendes R, Livne M, De Fauw J . Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study. J Med Internet Res. 2021; 23(7):e26151. PMC: 8314151. DOI: 10.2196/26151. View

Nguyen D, Long T, Jia X, Lu W, Gu X, Iqbal Z . A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning. Sci Rep. 2019; 9(1):1076. PMC: 6355802. DOI: 10.1038/s41598-018-37741-x. View

Li W, Li Y, Qin W, Liang X, Xu J, Xiong J . Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR)-guided radiotherapy. Quant Imaging Med Surg. 2020; 10(6):1223-1236. PMC: 7276358. DOI: 10.21037/qims-19-885. View

Yoon H, Cheon W, Jeong S, Kim H, Kim K, Nam H . Multi-Parametric Deep Learning Model for Prediction of Overall Survival after Postoperative Concurrent Chemoradiotherapy in Glioblastoma Patients. Cancers (Basel). 2020; 12(8). PMC: 7465791. DOI: 10.3390/cancers12082284. View

Wang C, Liou Y, Lin Y, Chang C, Chu P, Lee Y . Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning. Sci Rep. 2021; 11(1):16244. PMC: 8355253. DOI: 10.1038/s41598-021-95545-y. View

Maspero M, Bentvelzen L, Savenije M, Guerreiro F, Seravalli E, Janssens G . Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy. Radiother Oncol. 2020; 153:197-204. DOI: 10.1016/j.radonc.2020.09.029. View

10.

Lee J, An J, Choi M, Park S, Kim S, Lee J . Deep Learning-Based Survival Analysis Identified Associations Between Molecular Subtype and Optimal Adjuvant Treatment of Patients With Gastric Cancer. JCO Clin Cancer Inform. 2019; 2:1-14. PMC: 6873983. DOI: 10.1200/CCI.17.00065. View

11.

Hu Y, Modat M, Gibson E, Li W, Ghavami N, Bonmati E . Weakly-supervised convolutional neural networks for multimodal image registration. Med Image Anal. 2018; 49:1-13. PMC: 6742510. DOI: 10.1016/j.media.2018.07.002. View

12.

Schreier J, Attanasi F, Laaksonen H . A Full-Image Deep Segmenter for CT Images in Breast Cancer Radiotherapy Treatment. Front Oncol. 2019; 9:677. PMC: 6669791. DOI: 10.3389/fonc.2019.00677. View

13.

Nie D, Zhang H, Adeli E, Liu L, Shen D . 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients. Med Image Comput Comput Assist Interv. 2017; 9901:212-220. PMC: 5278791. DOI: 10.1007/978-3-319-46723-8_25. View

14.

Arabi H, Dowling J, Burgos N, Han X, Greer P, Koutsouvelis N . Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI-guided radiation planning in the pelvic region. Med Phys. 2018; 45(11):5218-5233. DOI: 10.1002/mp.13187. View

15.

Stathopoulos S, Kalamboukis T . Applying latent semantic analysis to large-scale medical image databases. Comput Med Imaging Graph. 2014; 39:27-34. DOI: 10.1016/j.compmedimag.2014.05.009. View

16.

Jiang D, Yan H, Chang N, Li T, Mao R, Du C . Convolutional neural network-based dosimetry evaluation of esophageal radiation treatment planning. Med Phys. 2020; 47(10):4735-4742. DOI: 10.1002/mp.14434. View

17.

Kearney V, Chan J, Wang T, Perry A, Descovich M, Morin O . DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation. Sci Rep. 2020; 10(1):11073. PMC: 7338467. DOI: 10.1038/s41598-020-68062-7. View

18.

van Rooij W, Dahele M, Ribeiro Brandao H, Delaney A, Slotman B, Verbakel W . Deep Learning-Based Delineation of Head and Neck Organs at Risk: Geometric and Dosimetric Evaluation. Int J Radiat Oncol Biol Phys. 2019; 104(3):677-684. DOI: 10.1016/j.ijrobp.2019.02.040. View

19.

Ma J, Nguyen D, Bai T, Folkerts M, Jia X, Lu W . A feasibility study on deep learning-based individualized 3D dose distribution prediction. Med Phys. 2021; 48(8):4438-4447. PMC: 8842508. DOI: 10.1002/mp.15025. View

20.

Neppl S, Landry G, Kurz C, Hansen D, Hoyle B, Stocklein S . Evaluation of proton and photon dose distributions recalculated on 2D and 3D Unet-generated pseudoCTs from T1-weighted MR head scans. Acta Oncol. 2019; 58(10):1429-1434. DOI: 10.1080/0284186X.2019.1630754. View