CAN ARTIFICIAL INTELLIGENCE PREDICT THE RISK OF PANCREATIC CANCER USING DISEASE TRAJECTORIES?

Researchers used artificial intelligence (AI) approaches on real-world longitudinal clinical data to build monitoring programmes for the early diagnosis of individuals at high risk of pancreatic cancer, one of the most aggressive illnesses.

CONCERNING THE RESEARCH

The current study employed real-world longitudinal health records from a large number of patients to identify several people at high risk of pancreatic cancer.

They used patient information from the Danish National Patient Registry (DNPR) and, later, the United States Veterans Affairs (US-VA) Corporate Data Warehouse (CDW) to apply freshly discovered machine learning (ML) algorithms.

 

The former included clinical data from 8.6 million patients between 1977 and 2018, equivalent to 24,000 pancreatic cancer cases, and the latter included clinical data from three million patients, corresponding to 3,900 pancreatic cancer cases.

The researchers developed and evaluated a wide range of machine learning models on the sequence of illness codes in the DNPR and US-VA clinical data, as well as the prediction of cancer incidence.

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BACKGROUND

Pancreatic cancer is becoming more common, making it the top cause of cancer-related fatalities globally. Pancreatic cancer is difficult to detect due to a lack of awareness of its risk factors.

Late identification at advanced or remote metastatic stages complicates therapy, making patient survival exceedingly rare. At five years, just two to nine per cent of such patients survive.

Although age is a known risk factor for pancreatic cancer, age-based population-wide screening is impracticable due to the high expense of clinical testing, which also produces false-positive findings.

Furthermore, data on family history or genetic risk factors for the general population are frequently lacking. As a result, there is an urgent need to create low-cost monitoring programmes for early diagnosis of pancreatic cancer. In developing prediction models, the researchers employed three-character International Classification of Diseases (ICD) diagnostic codes and classified 'pancreatic cancer patients as those who had at least one code under C25, signifying malignant neoplasm of the pancreas.

Cancer diagnostic illness codes were 98% accurate. Finally, the researchers identified which diagnoses in a patient's history of diagnostic codes were most revealing of cancer risk to recommend an optimal monitoring program.

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Furthermore, the researchers used the area under the receiver operating characteristic (AUROC) and relative risk (RR) curves to assess the prediction performance of the various models trained in the DNPR. Furthermore, they revealed ML-derived RR ratings of cancer patients in the high-risk category.

OUTPUT

All prior research that used real-world clinical records to predict pancreatic cancer risk yielded good findings, but they did not extract time-sequential longitudinal characteristics from illness histories. They tested non-time-sequential models on the DNPR dataset in this work.

While clinicians may have discovered some instances based on known pancreatic cancer risk factors, such as chronic pancreatitis, a significant portion of them, approximately 70, would still be newly diagnosed based on a conservative estimate. 

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Despite the use of common ICD illness codes and similar cancer survival, cross-applying the DNPR data to the US-VA data reduced the performance of ML models, emphasizing the requirement for independent model training across geographical areas to achieve regionally optimal model performance.

However, an ideal environment for multi-institutional collaboration to achieve globally relevant results

CONCLUSIONS 

The accuracy of the ML-based models reported in this paper might increase with the availability of data other than illness codes, such as observations documented in clinical notes, test findings, genetic profiles of additional people, or health-related information from their wearable devices. 

Then, clinical adoption of early pancreatic cancer detection would necessitate the identification of high-risk individuals.

Because individuals at the highest risk are a smaller subset of a huge population computationally assessed, costly and improved clinical screening and intervention programs will be restricted to a few people.

Nonetheless, AI on real-world clinical records has the potential to change the focus from late-stage cancer treatment to early-stage cancer treatment, which would significantly enhance the quality of life for all patients while raising the benefit-to-cost ratio. Finally, utilizing disease trajectories, artificial intelligence (AI) has the potential to be a helpful tool in forecasting the risk of pancreatic cancer. Large volumes of data, such as a patient's medical history and imaging findings, may be analyzed by AI algorithms to detect trends and make predictions about a patient's future health outcomes.

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Healthcare practitioners may be able to identify individuals who are at a higher risk of getting pancreatic cancer early by utilizing AI to analyze disease trajectories, allowing for earlier intervention and maybe improved treatment results. Furthermore, AI may be able to assist in the personalization of treatment strategies for specific patients based on their distinct disease trajectories.

While more study is needed in this area, the prospective benefits of employing AI in pancreatic cancer are promising. Risk prediction appears to be promising. As AI technology advances and more data becomes accessible, AI is anticipated to play a growing role in improving patient outcomes in the battle against pancreatic cancer.

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