Artificial Intelligence-Assisted Peer Review in Radiation Oncology

Peer review in radiation oncology is essential for the safe and efficient delivery of radiation treatments; ¹ however, little guidance and limited research exist regarding the frequency, mechanisms, and metrics of peer review from professional organizations. ^1-4 Peer review addresses patient characteristics and clinical oncological information, planned radiotherapeutic parameters, and potential treatment-related variations. In a report series commissioned by the American Society for Radiation Oncology, seven main items are currently examined by peer reviewers. ¹ These are (1) the decision to include radiation as part of treatment, (2) the general radiation treatment approach, (3) the target definition, (4) normal tissue image segmentation, (5) the planning directive, (6) technical plan quality, and (7) treatment delivery.

Many of these factors are indicative of cases requiring re-plans or cases that may have treatment interruptions. Although peer reviews should be conducted prior to the start of treatment to ensure safety and quality, they are typically performed either immediately before or after treatment has begun. Thus, there is minimal time to adjust therapy based on the recommendations of peer reviewers. Plan changes based on peer review discussions are not uncommon. Hoopes et al reported that 90% of physicians have changed their radiation plans because of peer review. ³ Other studies have shown that up to 10% of cases have been recommended for plan modifications based on peer review. ^1,3 Studies have also demonstrated that prolonged radiation treatment times, for various reasons, correlated with reduced local disease control and worse overall survival. ^5-7 Therefore, timely, efficient peer review may greatly improve treatment quality and, ultimately, enhance patient safety.

However, peer review has some practical difficulties, especially when the process must be coordinated among multiple facilities. While a fully integrated approach holds promise for improving the quality, safety, and value of cancer care, in reality, the process is often disjointed owing to differing provider schedules, caseloads, and expertise. These opportunities to optimize peer review are further highlighted by the fact that different approaches are taken by academic centers and community cancer centers operating within the same network. ⁸ Practically speaking, however, caseload, time, department resources, staffing, and increasing complexity in treatment techniques limit thorough peer review. ⁹ Thus, detecting cases that may experience treatment interruptions due to undesirable effects is not easy.

A pan-Canadian survey by Caissie et al demonstrated that barriers to peer review, including time constraints (27%) and radiation oncologist availability (34%), caused half of all programs surveyed to conduct peer review after the start of treatment. ¹⁰ Therefore, automating peer review, even if only partially, may significantly streamline the process.

Artificial intelligence (AI) tools can potentially be used to assist in peer review. Studies have used machine learning (ML) techniques to identify high-risk patients for detailed clinical evaluation during radiation and chemoradiation. ¹¹ To identify potential treatment interruptions or unusual side effects resulting from suboptimal treatment plans, we hypothesize that complex, nonlinear relationships exist between different variables, including clinical characteristics and plan parameters. Our approach was to use AI and ML to uncover complex interactions among variables and to supplement clinical knowledge with treatment and plan parameters. By using AI, our intent was to screen treatment plans for clinical and radiotherapeutic factors that could lead to treatment interruptions or toxicity and submit the plans for more detailed inspection to help expedite peer review.

These study results provide proof of concept that AI can be a reliable screening tool in the peer review process to help identify cases early on that may cause treatment interruption or major changes in treatment course. We found that logistic regression and NN-based models had some predictive power in recognizing cases that would experience treatment interruptions. This could be useful in identifying cases that will require a replan for various reasons, such as a patient who experiences toxicity or has a dramatic change in target volume. Cases identified as “high risk” for interruptions could be highlighted in peer review for a more in-depth evaluation.

Our study demonstrates the potential of AI in radiation oncology peer review to prospectively identify treatment interruption. A simple example of one potential software display in a peer-review setting is shown in Figure 3. Each patient could be assigned a “score” associated with treatment interruption risk that could help prioritize cases for discussion. The software might also display the clinical or plan features flagged for that specific case. Links directly to the plan and other relevant clinical documents could facilitate quick reference during discussions.

In radiation oncology, every patient receives a personalized plan chosen by their physician that best fits their characteristics, such as their diagnosis and performance status. However, for reasons that are sometimes unknown, a patient may require a change or break in treatment, which can be detrimental to their oncologic outcome. In patients with head-and-neck cancer, the hazard rate of death increased 4.2% for each additional day needed to finish radiation therapy beyond 8 weeks. ¹³ Even small disruptions in radiation therapy can have negative consequences in gynecologic patients. Lanciano et al reported a 7.7% reduction in 4-year survival when the radiation therapy course was >10 weeks compared with 8.0-9.9 weeks. ⁷

The outcome analyzed in our study was based on whether or not a plan had remaining fractions. Although remaining fractions are not always indicative of treatment toxicity, they could be a major contributor. Several studies have looked at the ability of ML models to predict toxicity using clinical and dose factors and/or radiomic features. ^14,15 Reddy et al used random forest, gradient-boosted decision tree, and logistic regression models with input of clinical and treatment variables to predict breast cancer treatment toxicity and achieved an area under the curve (AUC) ranging from 0.56 to 0.85. ¹⁶ Das et al created a fusion of different non-linear multivariate models (decision trees, NNs, support vector machines, and self-organizing maps) with input of dose and non-dose patient variables to predict radiation-induced pneumonitis in lung cancer patients with an AUC of 0.79. ¹⁷

The test set accuracy in our study ranged from 57% to 71% for the lung/breast/chest subset. We grouped lung and breast into the same subset due to data size limitations; thus, the predictive accuracy of our models may be improved by further separation.

Similar to their study on breast cancer patients, Reddy et al used random forest, gradient-boosted decision tree, and logistic regression models with clinical and treatment parameters to predict head-and-neck cancer treatment toxicity and achieved an AUC of 0.64-0.76 in their validation set. ¹⁸ Jiang et al used three supervised learning methods (ridge logistic regression, lasso logistic regression, and random forest) to predict xerostomia, resulting in an AUC of 0.7. ¹⁹

Our head-and-neck subset came in lower at 53%-61% test set accuracy. We did not directly analyze toxicity because we predicted whether there were remaining fractions in each plan. Remaining fractions could also be caused by re-planning owing to tumor shrinkage; this may explain the reduced predictive value of our study compared to others looking solely at toxicity. Carrara et al applied the artificial NN approach with five input variables relating to patient dose, history, and therapy to predict toxicity after high-dose prostate cancer radiation therapy, achieving an AUC of 0.78. ²⁰ Pella et al used support vector machines and neural network-based algorithms to predict acute toxicity of the bladder and rectum due to prostate irradiation, resulting in overall accuracy similar in both models at an AUC of 0.7. ²¹ The accuracy of our testing set in the prostate/pelvis subset, at 71%-75%, is similar.

Although toxicity is one cause of treatment interruption, physician input/suggestions can also cause interruptions if the case is not presented before treatment and/or some information is not assessed during peer review. The traditional approach to peer review is based on “chart rounds,” where the physicians, physicists, dosimetrists, and therapists review details of each case (eg, clinical history, treatment technique, prescription dose, treatment plan, and patient setup). The average amount of time spent on each patient during peer review was reported to range between 1 and 4 minutes. ^2,22

Therefore, reviewing the more technical aspects of treatment delivery (eg, monitor units and couch/collimator/gantry parameters), which may provide additional information, may not be feasible owing to time constraints. As demonstrated by our study, subtle, complex relationships within/between clinical data and plan parameters may influence successful treatment delivery. AI and ML have the potential to identify and analyze these relationships for peer review. ²³ Similar to the goals of many AI-driven studies, ours is not to replace humans with AI but instead to offer providers additional tools to enhance the process. These can supplement clinical intuition and deepen our understanding of factors that previously might go unanticipated.

Since time is a limited resource, AI/ML can also help to identify and prioritize cases that require more time for discussion. Ultimately, these technologies could improve patient safety and treatment outcomes.

Our study offers a concept that can be used to better identify charts requiring more in-depth review. Many software tools for peer review or chart checking take “rule-based” approaches, meaning that the parameter being flagged will need to be predefined with a range or value. ²⁴ Azmandian et al used clustering techniques for outlier detection based on treatment parameters in four-field box prostate plans; this study helped to detect plan abnormalities without using the rule-based approach. ²⁵ Kalet et al developed a Bayesian network model using clinical and plan parameters to detect errors in radiation therapy plans. Their model utilized a clinical layer (eg, morphology or tumor type), a prescription layer (eg, total dose, dose per fraction, technique), and a treatment layer (eg, monitor unit per fraction, number of beams, beam energy). Their study achieved an AUC of 0.88, 0.98, and 0.89 for the lung, brain, and breast cancer error detection networks, respectively. ²⁶ Similar to our study, Kalet et al found the highest testing set accuracy in the brain subset.

Luk et al also used a Bayesian model incorporating prescription, plan, setup, and diagnostic parameters to detect chart review errors. The AUC for this study ranged from 0.82 to 0.89. ²⁷ Our testing set accuracy is lower than these other studies, possibly because our cohort included not only “abnormal” cases from a chart-checking perspective but also cases with toxicity-related interruptions.

Although the sensitivity and specificity of our models are relatively low, there are multiple ways in which we can improve these metrics of our study. First, while our study specified remaining fractions as a surrogate for cases likely to experience treatment interruption or complications, remaining fractions can result from reasons unrelated to treatment. Our definition of treatment interruptions as remaining fractions includes patients whose treatments were re-planned and those who discontinued treatment. Separating these cases into two cohorts could improve model predictability.

Second, we grouped subsets based on treatment site, but further dividing them into more focused groups may improve model predictability, eg, separating pelvic plans based on whether they include pelvic lymph nodes or separating spine plans based on vertebral level. Overall, model performance and rigor are expected to improve with increased curation of the dataset and additional clinical factors, including dosimetric plan and structure set data. Additionally, introducing socioeconomic and personal factors, which are commonly seen as causes of treatment interruptions, could improve our models.

Future study directions can also include optimizing hyperparameters and layer structures for the NN. Alternative techniques for data re-balancing or using convolutional NNs for deep learning could also be investigated. A weakness of this study is that it does not include brachytherapy, electron therapy, other treatment sites, or pediatric populations. To address this issue, we are continuing the study with brachytherapy, electrons, and pediatric populations, as well as including additional, more robust parameters.

A known limitation of NNs is their difficulty in identifying the single feature that contributes most to the model, given the complexity of their relationships and the numerous weights/biases assigned to them. Future studies can explore a technique to uncover those features selected by the NN deemed to be most important to the predictive task. At the current stage of our model, which demonstrates moderate predictive performance, making conclusions about or connecting a specific result and/or feature to a reason for a predicted interruption is difficult. Improvements in model performance should enable exploration of more advanced, explainable-AI techniques.

Another current focus of AI and ML is on treatment planning. ²³ For example, AI is being used to adapt treatment plans in real time to match the day-to-day variations in patient anatomy, thereby reducing interruptions caused by re-simulation and/or discussion manual re-planning. ²⁸ As the technology for adaptive planning becomes more widely available, our model will likely need additional training to keep up with technological advancements. Future studies may focus on sites where adaptive planning is often beneficial or necessary, such as the head and neck owing to tumor progression or treatment response. The ability to predict cases requiring adaptive planning due to tumor change can allow the care team to anticipate and minimize treatment breaks.

Our study demonstrated the ability of AI and ML models to predict major changes in patient treatment, including re-planning and radiation therapy cessation. The findings point to the promising capability of AI and ML to augment peer review and encourage further studies in this aspect of radiation oncology.