Diagnostics Using AI to predict cancer


Sepsis isn’t the only disease state that’s predictable with AI. Researchers are coming up with different algorithms all the time to try and get a head start on one of the world’s biggest killers – cancer. The below are just two examples of how AI is being applied in the field of oncology.


Lung cancer – Researchers from the Diagnostic Image Analysis Group at Radboud (DIAG) University Medical Centre (RUMC) in Nijmegen developed a deep learning algorithm for malignancy risk estimation of pulmonary nodules detected at low-dose screening CT scan. When they evaluated it by pitting its predictive capabilities against experts in the field of radiology and pulmonology, they found it performed with equal accuracy, meaning it could be a valuable way to triage patients by risk. The researchers plan to continue improving the algorithm by incorporating clinical parameters like age, sex and smoking history.


Pancreatic cancer – AP-HP Greater Paris University Hospitals, the European clinical trial centre with the largest amount of healthcare research data in France, and Owkin, a start-up harnessing AI for medical research and clinical development, created a tool that uses deep learning models to identify pancreatic adenocarcinoma molecular subtypes from histology slides. Pancreatic adenocarcinoma is complex due to its heterogeneity and tumour plasticity – the ability of cancer cells to adapt to gain therapeutic resistance.


The tool, HE2RNA, connects information at the genomic, cellular and tissue levels to determine whether tumours are basal-like or classical – the two transcriptomic subtypes proposed to be predictive of patient response to chemotherapy. The tool is currently marketed for use in clinical trials for pancreatic cancer treatments.


be a common condition, it’s notoriously difficult to diagnose. Onset and progression are rapid and there is no single symptom or diagnostic marker that raises a red flag. In the US, sepsis has an average hospital mortality rate of 17%, reflecting both the speed at which the condition advances and frequent delays in diagnosis. Back in 2018, Augusta Health, a community hospital in Virginia, recognised that there was opportunity for improvement. Sepsis was leading to unnecessary deaths and soaking up vital healthcare resources. It was time to act.


“White blood count is not included for inpatients because labs are only taken once a day and we didn’t want old lab values to impact the current results.”


A new level of support 4.76%


Average mortality rate of sepsis at Augusta Health, compared with the Virginia state average of 12.7%.


Augusta Health 24


Enter Penny Cooper and her small team of data scientists, who devised an artificial intelligence (AI)-based early alert system to identify high- risk patients. They started by taking the four main systemic inflammatory response syndrome (SIRS) indicators: high temperature (>38°C), heart rate (>90 beats per minute), respiratory rate (>20 breaths per minute) and an abnormal white blood cell count. They used these benchmarks as their key variables before adding mean arterial pressure and shock index to further increase accuracy.


Based on the results of their retrospective study, Cooper’s team then developed an algorithm to assess the key indicators every hour, automatically extracting data from the hospital’s Rauland bed system and combining it with clinical data from electronic medical records (EMRs). The system assesses the entire patient population – regardless of disease state – and assumes that every patient may be at risk of sepsis.


From there, the data is analysed and concurrent symptoms identified, with each patient allocated a ‘score’ based on the data. If a patient receives a score above a predetermined cut-off point, an alert is triggered and sent to nursing staff. Augusta Health uses Vocera communication devices, but Cooper says alerts could be routed to any similar system, or indeed to mobile phones or emails. The system – introduced into the emergency department in August 2018 and then to the wider patient population two years later – has cut the hospital’s sepsis mortality rate to 4.76% compared with the Virginia state average of 12.7%. Cooper estimates more than 300 lives have been saved when predicted mortality is compared with actual mortality.


Simplicity is the key


The beauty of the system lies in its simplicity – feeding the right data to the right algorithm and letting AI do the rest. Staff don’t need to spend any extra time collecting data – it is all already captured during assessment and treatment. Moreover, instead of clinicians analysing and comparing the data to identify which patients are at risk of sepsis, the system does it for them. Automating the process also helps eliminate concerns over privacy or consent. “The data is being collected in the EMR anyway and the nurses we’re alerting are the nurses who are directly attending that patient – we’re not sending that alert out to a different nurse,” says Cooper. “The alert is also only sharing basic information – a room and bed number – it doesn’t even include the patient’s name.” Cooper’s team has also adapted the system for different clinical settings, changing the parameters in line with available data. More variables are included for emergency department (ED) patients than for inpatients, for example. Some variables are also excluded to reduce error. “White blood count is not included for inpatients because labs are only taken once a day and we didn’t want old lab values to impact the current results,” explains Cooper. She insists the alert system was not difficult to set up, nor was it expensive. The algorithm was devised and programmed in-house, without the use of external consultants or software developers. “We’re a pretty small shop,” she says of Augusta Health, which has just 255 beds. The hospital’s data


Practical Patient Care / www.practical-patient-care.com


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