Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions
2. Cancer Statistics
3. Conventional Practices for Cancer Diagnosis and Treatment
4. AI for Cancer Research
4.1. Cancer Data Repositories
- Be the Match: Jason Carter Clinical Trial Search and Support Program
- Bladder Cancer Advocacy Network
- Center for Information and Study on Clinical Research Participation (CISCRP)
- EmergingMed Clinical Trial Navigator Service
- Lazarex Cancer Foundation
- Melanoma Research Alliance
- Metastatic Breast Cancer Project
- Metastatic Prostate Cancer Project
- National Brain Tumor Society Clinical Trial Finder
- National Cancer Institute (NCI) Clinical Trials
- Pancreatic Cancer Action Network Clinical Trial Finder
- SPOHNC Clinical Trial Navigation Service
- Targeted Agent and Profiling Utilization Registry (TAPUR) Study
- The Leukemia & Lymphoma Society Clinical Trial Support Center
- Us TOO Prostate Cancer Clinical Trial Finder
- World Health Organization (WHO) International Clinical Trials Registry Platform.
4.1.1. Types of Cancer Data Repositories
4.1.2. Radiographic Images
- Cancer Imaging Program (CIP) : The National Cancer Institute’s (NCI) CIP supports and promotes basic, translational, and clinical imaging research related to cancer, as well as the integration and application of these imaging advancements to the study of cancer biology and the treatment of cancer and cancer risk.
- Cancercentre.ai : It contains data like screenshots from the radiology platform that depict MRI of the prostate (T2-weighted images in axial plane). It provides trained radiologists with pre-screened images and identified features.
- The Cancer Imaging Archive (TCIA) : TCIA has a sizable collection of medical photographs of cancer that are available for download by the general public. The image modality, or image type supplied are MRI, CT, digital histopathology, etc.
4.1.3. Genomic and Molecular Data
- The Cancer Genome Atlas (TCGA): Over 20,000 cancer samples from 33 different cancer types are characterised as TCGA, a popularly utilised public database for cancer research that produces numerous types of data and tools .
- International Cancer Genome Consortium (ICGC) : In 50 of the most significant cancer forms, it maps the genetic flaws. Worldwide cancer researchers are given free access to all the data from the 25,000 cancer samples that were examined.
- cBioPortalData : It allows visualization, analysis, and import of cBioPortal datasets as MultiAssayExperiment objects in Bioconductor.
- Genomics of Drug Sensitivity in Cancer : It characterizes over a thousand human cancer cell lines, and hundreds of chemicals are tested on them. It also offers information on drug response as well as genomic-sensitivity markers.
- Cancer Cell Line Encyclopedia (CCLE) : This is useful for investigating cancer biology, identifying cancer targets, and determining treatment efficacy.
- LinkedOmics : It offers a platform for accessing, analysing, and comparing cancer multi-omics data inside and across tumour types.
4.1.4. Pathological Images
- Whole Slide Imaging Repository : Pathology departments use scanned photos of traditional glass slides to create digital slides as their imaging method.
- Cancer Digital Slide Archive : It hosts pathological images maintained by TCGA.
4.1.5. Clinical Data and Blood Profiling
- Data-CAN : It is a cancer-data-knowledge network that was co-created in order to produce better results and greater social benefits for cancer research.
- Optum Oncology EHR data : Optum data include vital signs, symptoms, problem descriptions, clinical evaluations, lab results, techniques, diagnoses, surgeries, and therapies. Additionally, it contains information about the cancer’s stage, grade, histology, genetic mutations, other blood biomarkers, lines of therapy, and assessments of the disease’s development and drug response to further increase its usefulness to oncology.
4.2. Using Cloud AI Platforms
4.3. AI for Cancer Prediction
4.4. AI for Cancer Diagnosis
4.5. AI for Cancer Treatment
5.1. Technical Challenges
5.2. Ethical Challenges
6. Future Directions
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
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|Colon and Rectum||916,000|
|Cancer Type||New Cases|
|Colon and Rectum||1,930,000|
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Sebastian, A.M.; Peter, D. Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions. Life 2022, 12, 1991. https://doi.org/10.3390/life12121991
Sebastian AM, Peter D. Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions. Life. 2022; 12(12):1991. https://doi.org/10.3390/life12121991Chicago/Turabian Style
Sebastian, Anu Maria, and David Peter. 2022. "Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions" Life 12, no. 12: 1991. https://doi.org/10.3390/life12121991