News | Artificial Intelligence | September 26, 2019

AI Accurately Predicts Radiotherapy Side Effects for Head and Neck Cancer Patients

Machine learning model identified patients most likely to experience significant weight loss or need for a feeding tube

AI Accurately Predicts Radiotherapy Side Effects for Head and Neck Cancer Patients

September 26, 2019 — For the first time, a sophisticated computer model has been shown to accurately predict two of the most challenging side effects associated with radiation therapy for head and neck cancer. This precision oncology approach has the potential to better identify patients who might benefit from early interventions that may help to prevent significant weight loss after treatment or reduce the need for feeding tube placement. Findings were presented at the 61st Annual Meeting of the American Society for Radiation Oncology (ASTRO), Sept. 15-18, 2019, in Chicago.

“In the past, it has been hard to predict which patients might experience these side effects,” said Jay Reddy, M.D., Ph.D., an assistant professor of radiation oncology at The University of Texas MD Anderson Cancer Center and lead author on the study. “Now we have a reliable machine learning model, using a high volume of internal institutional data, that allows us to do so.”

Machine learning is a branch of artificial intelligence (AI) that uses statistical models to analyze large quantities of data, uncovering patterns that can predict outcomes with a high degree of accuracy. Used by the tech industry to allow speech and facial recognition, “spam” filtering and targeted advertising, machine learning has been an emerging topic of interest for medical researchers seeking to translate large amounts of data into knowledge that can support clinical decision making. 

Reddy and his team developed models to analyze large sets of data merged from three sources: electronic health records (Epic), an internal web-based charting tool (Brocade) and the record/verify system (Mosaiq). The data included more than 700 clinical and treatment variables for patients with head and neck cancer (75 percent male/25 percent female, with a median age of 62 years) who received more than 2,000 courses of radiation therapy (median dose 60 Gy) across five practice sites at MD Anderson from 2016 to 2018. 

Researchers used the models to predict three endpoints: significant weight loss, feeding tube placement and unplanned hospitalizations. Results from the best-performing model were then validated against 225 subsequent consecutive radiation therapy treatments. Models with a performance rate that met a pre-specified threshold of area under the curve (AUC) of 0.70 or higher were considered clinically valid. (An AUC score of 1.0 would mean the model’s predictions were 100 percent accurate, while a score of 0.0 would mean the predictions were never accurate.)

Approximately 53,000 people are diagnosed with head and neck (oral cavity or oropharyngeal) cancers each year in the United States. These cancers are more than twice as common in men as in women, and typically diagnosed later in life (with an average age of diagnosis of 62 years). Head and neck cancers, when diagnosed early, are typically treated with radiation therapy or surgery. Later-stage cancers are treated with a combination of radiation therapy and chemotherapy. A patient may also be treated first with surgery, followed by radiation therapy alone, or by a combination of radiation and chemotherapy.

Radiation therapy is effective at treating head and neck cancer by slowing or stopping the growth of new cancer cells. However, it may also damage oral tissue and upset the balance of bacteria in the mouth, causing adverse side effects such as a sore throat, mouth sores, loss of taste and dry mouth. When sore throats are severe, they can make it difficult for the patient to eat and may lead to weight loss or require the temporary insertion of a feeding tube. Nearly all patients with head and neck cancer experience some negative effects of treatment.

“Being able to identify which patients are at greatest risk would allow radiation oncologists to take steps to prevent or mitigate these possible side effects,” said Reddy. “If the patient has an intermediate risk, and they might get through treatment without needing a feeding tube, we could take precautions such as setting them up with a nutritionist and providing them with nutritional supplements. If we know their risk for feeding tube placement is extremely high – a better than 50 percent chance they would need one – we could place it ahead of time so they wouldn’t have to be admitted to the hospital after treatment. We’d know to keep a closer eye on that patient.”

The models predicted the likelihood of significant weight loss (AUC = 0.751) and need for feeding tube placement (AUC = 0.755) with a high degree of accuracy. 

“The models used in this study were consistently good at predicting those two outcomes,” said Reddy. “You could rerun those models with a new patient or series of patients and get a number saying this adverse effect is likely to happen or not to happen.”

For example, said Reddy, using their model, clinicians could potentially plug in information related to a specific patient – such as age, gender, type of cancer and other distinct variables – and the model might tell them, “Eighty percent of people like you with this clinical profile get through treatment without a feeding tube. It may not be perfect, but it’s better than having no understanding at all.”

The model fell short of predicting unplanned hospitalizations with sufficient clinical validity (AUC = 0.64). Redoing the analyses with more “training” data for unplanned hospitalizations could improve accuracy in predicting this side effect as well, said Reddy. “As we treat more and more patients, the sample size gets bigger, so every data point should get better. It’s possible we just didn’t have enough information accumulated for this aspect of the model.”

While the machine learning approach cannot isolate the single-most predictive factor or combination of factors that lead to negative side effects, it can provide patients and their clinicians with a better understanding of what to expect during the course of treatment, said Reddy. In addition to predicting the likelihood of side effects, machine learning models could potentially predict which treatment plans would be most effective for different types of patients and allow for more personalized approaches to radiation oncology, he explained.

“Machine learning can make doctors more efficient and treatment safer by reducing the risk of error,” said Reddy. “It has the potential for influencing all aspects of radiation oncology today – anything where a computer can look at data and recognize a pattern.”

For more information: www.astro.org

Additional coverage of ASTRO 2019

Related Content

#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2

Getty Images

Feature | Coronavirus (COVID-19) | April 03, 2020 | By Melinda Taschetta-Millane and Dave Fornell
In an effort to keep the imaging field updated on the latest information being released on coronavirus (COVID-19), th
Varian received FDA clearance for its Ethos therapy in February 2020. It is an adaptive intelligence solution that uses onboard AI in the treatment system to take the cone beam CT imaging on the system, compare it to the treatment plan and deliver an entire adaptive treatment plan in a typical 15-minute treatment time slot, from patient setup through treatment delivery.

Varian received FDA clearance for its Ethos therapy in February 2020, shown here displayed for the first time at ASTRO 2019. It is an adaptive intelligence solution that uses onboard AI in the treatment system to take the cone beam CT imaging on the system, compare it to the treatment plan and deliver an entire adaptive treatment plan in a typical 15-minute treatment time slot, from patient setup through treatment delivery.

Feature | Treatment Planning | April 03, 2020 | Dave Fornell, Editor
The traditional treatment planning process takes days to create an optimized radiation therapy delivery plan, but new
An example of Philips’ TrueVue technology, which offers photo-realistic rendering and the ability to change the location of the lighting source on 3-D ultrasound images. In this example of two Amplazer transcatheter septal occluder devices in the heart, the operator demonstrating the product was able to push the lighting source behind the devices into the other chamber of the heart. This illuminated a hole that was still present that the occluders did not seal.

An example of Philips’ TrueVue technology, which offers photo-realistic rendering and the ability to change the location of the lighting source on 3-D ultrasound images. In this example of two Amplazer transcatheter septal occluder devices in the heart, the operator demonstrating the product was able to push the lighting source behind the devices into the other chamber of the heart. This illuminated a hole that was still present that the occluders did not seal. Photo by Dave Fornell

Feature | Radiology Imaging | April 02, 2020 | By Katie Caron
A new year — and decade — offers the opportunity to reflect on the advancements and challenges of years gone by and p
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus

Getty Images

Feature | Coronavirus (COVID-19) | April 02, 2020 | Jilan Liu and HIMSS Greater China Team
Information technologies have played a pivotal role in China’s response to the novel coronavirus...
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 the company is now offering a suite of AI solutions Vuno Med-LungQuant and Vuno Med-Chest X-ray for COVID-19, encompassing both lung X-ray and computed tomography (CT) modalities respectively all at once
News | Artificial Intelligence | April 02, 2020
April 2, 2020 — In the face of the COVID-19 pand
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 New studies use SIRD model to forecast COVID-19 spread; examine patient CT scans to correlate clinical features with mortality

Fig 1. A sample scoring on CT images of a 63-year-old woman from mortality group demonstrated a total score of 63. It was calculated as: for upper zone (A), 3 (consolidation) × 3 (50–75% distribution) × 2 (both right and left lungs) + 2 (ground glass opacity) ×1 (< 25% distribution) × 2 (both right and left lungs); for middle zone (B), 3 (consolidation) × 2 (25–50% distribution) × 2 (both right and left lungs) + 2 (ground glass opacity) × 2 (25–50% distribution) × 2 (both right and left lungs); for lower zone (C), 3 (consolidation) × (2 (25–50% distribution of the right lung) + 3 (50–75% distribution of the left lung)) + 2 (ground glass opacity) × (2 (25–50% distribution of the right lung) + 1 (< 25% distribution of the left lung)) Yuan et al, 2020 (CC BY 4.0)

News | Coronavirus (COVID-19) | April 01, 2020
April 1, 2020 — A new study, ...
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 The Chinese start-up company Infervision launches its AI-based solution InferRead CT Lung Covid-19 also in Europe
News | Artificial Intelligence | March 31, 2020
March 31, 2020 — Lung infections generated by the coronavirus can be detected in...
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 Doctor in our hospital is using this intelligent system for accurate diagnosis

Doctor in our hospital is using this intelligent system for accurate diagnosis. (Photo: Business Wire)

News | Artificial Intelligence | March 31, 2020
March 31, 2020 — The Intelligent Evalua...