Have these advancements in oncology moved us closer to predicting the future?
Key Takeaways
The digital revolution is transforming oncology, enhancing personalized cancer care through forecasting, prediction, and risk stratification.
Machine-learning models have demonstrated proficiency in predicting especially challenging cancers, potentially improving clinical outcomes and patient quality of life in unprecedented ways.
Research has shown that newly developed algorithms may even be better than clinicians at predicting cancer.
We live in a time referred to as the “digital revolution.” AI, especially, has contributed to the rapid expansion of accessible health data, changing the way we practice medicine.
Advancements across the field of oncology have resulted in better personalization of care, as well as the potential to predict future outcomes and challenges.
Improving precision oncology
Although there have been numerous advances in the diagnosis, prognosis, and treatment of cancer, individualized and data-driven care is difficult. This challenge is rooted in the distinct molecular, genetic, and tumor-based characteristics of the disease.
“AI has become pivotal as it can provide patients with forecasting and prediction and improved risk stratification according to specific criteria, such as in the cases of some breast, colon, ovarian, lung, and skin cancers,” per the author of a review published in Frontiers in Public Health.[] “Further, it can detect hidden patterns from several sources such as molecular profiling, pathology, and medical imaging, and integration of -omics data to provide a more comprehensive understanding of cancer and improve precision oncology.” The author also notes that, during a surgical procedure, AI has been shown to detect and diagnose cancers in real time.
More recently, algorithms have utilized microRNAs (miRNA) as a biomarker for cancer classifications. These miRNAs are small, single-stranded, non-coding RNAs that play a pivotal role in cancer pathogenesis.
Cancer of unknown primary
Between 3% and 5% of cancers are classified as a cancer of unknown primary (CUP). The dearth of targeted therapies for CUP results in poor outcomes.
Researchers at Harvard, MIT, and other major centers developed a machine-learning classifier trained on next-generation sequencing (NGS) data called OncoNPC.[] The data represented 36,445 tumors spanning 22 types of cancer. In 971 CUP tumors, OncoNPC predicted primary cancer types in 41.2% of tumors.
The deep-learning tool was also able to identify CUP subgroups with significantly higher polygenic germline risk for the predicted cancer types, and with significantly different survival outcomes. Patients with CUP treated with first palliative intent treatments that were aligned with their OncoNPC-predicted cancers had better clinical outcomes (HR = 0.348).
Pancreatic cancer
Pancreatic cancer is usually caught late, thus resulting in poor outcomes. Dutch and US researchers trained machine-learning models on the sequence of disease codes in clinical histories of patients with pancreatic cancer and tested the prediction of cancer occurrence within incremental time windows.[]
“We present a framework for predicting the risk of a low-incidence but very aggressive cancer by applying deep learning to real-world longitudinal datasets of disease trajectories,” the investigators wrote. “This study was designed to make explicit use of the time sequence of disease events and to assess the ability to predict cancer risk for increasing intervals between the end of the disease trajectory used for risk prediction and cancer occurrence.”
“Our results indicate that using the time sequence in disease histories as input to the model, rather than just disease occurrence at any time, improves the ability of AI methods to predict pancreatic cancer occurrence, especially for the highest-risk group,” they added.
According to the investigators, their model enhanced the ability to monitor patients at higher risk, potentially benefiting their lifespan and quality of life via early detection of this aggressive cancer.
Intriguingly, machine learning may be better than clinicians at predicting cancer, per the authors of a review published in the Journal of Multidisciplinary Healthcare.[]
But its potential extends beyond oncology: “These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer,” said the authors.
What this means for you
Machine learning, which trains computers to learn from vast swathes of data, can help in clinical decision-making by predicting different types of cancer. Recent advancements have focused on cancer types that have traditionally been more difficult to diagnose and treat, such as CUP and pancreatic cancers.