Personalised surveillance after treatment for high-risk cancer

A cross-sectional survey assessing clinicians' perspectives towards redesigning the surveillance model for head and neck cancer: can we do better?

BACKGROUND

Service delivery of post-treatment surveillance in head and neck cancer (HNC) varies across institutions in Australia. To better understand current practices and develop protocols that maximize service capacity or incorporate emerging technologies, especially in under-resourced regional and remote communities, it is important to obtain the perspectives of clinicians that regularly manage patients with HNC.

DESIGN

This cross-sectional study utilized an online survey distributed via email to specialists recruited from HNC-associated networks across Australia. The survey captured information on current practices and explored clinician perspectives towards re-designing the current surveillance model to incorporate telehealth or patient-reported outcome measures (PROMs). Quantitative data was analyzed using descriptive statistics while open-ended survey comments were analyzed using a content analysis approach.

RESULTS

Forty participants completed the survey (25 surgeons, 9 medical oncologists, 5 radiation oncologists and 1 oral medicine specialist). Most clinicians used either institution-specific guidelines (44%) or National Comprehensive Cancer Network guidelines (39%), with the remaining 17% using surveillance intervals based on patient symptoms. Following treatment, 53% of participants imaged patients only when there was clinical suspicion of recurrence or new symptoms. Planned surveillance imaging was conducted at 6 or 12-monthly intervals based on the HNC subtype. Fifty-seven percent of clinicians were open to redesigning the surveillance model, specifically in low-risk patients who did not require nasoendoscopic examination. Seventy-one percent had concerns regarding the feasibility of telehealth appointments, citing disparities in digital health equity. Additionally, 61% felt PROMs are currently underutilized and were open to incorporating HNC-specific PROMS into surveillance. Open-ended responses indicated that within the current surveillance model, "fragmented service provision" and "administration issues" were significantly impacting on timing of care.

CONCLUSION

Surveyed HNC clinicians feel that current post-treatment surveillance can be fragmented and potentially lead to delayed care. They are open to incorporating PROMS to assist in surveillance scheduling, especially in low-risk patients.

Prediction of Early Recurrence Following CRS/HIPEC in Patients With Disseminated Appendiceal Cancer

INTRODUCTION

In patients with disseminated appendiceal cancer (dAC) who underwent cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC), characterizing and predicting those who will develop early recurrence could provide a framework for personalizing follow-up. This study aims to: (1) characterize patients with dAC that are at risk for recurrence within 2 y following of CRS ± HIPEC (early recurrence; ER), (2) utilize automated machine learning (AutoML) to predict at-risk patients, and (3) identifying factors that are influential for prediction.

METHODS

A 12-institution cohort of patients with dAC treated with CRS ± HIPEC between 2000 and 2017 was used to train predictive models using H2O.ai's AutoML. Patients with early recurrence (ER) were compared to those who did not have recurrence or presented with recurrence after 2 y (control; C). However, 75% of the data was used for training and 25% for validation, and models were 5-fold cross-validated.

RESULTS

A total of 949 patients were included, with 337 ER patients (35.5%). Patients with ER had higher markers of inflammation, worse disease burden with poor response, and received greater intraoperative fluids/blood products. The highest performing AutoML model was a Stacked Ensemble (area under the curve = 0.78, area under the curve precision recall = 0.66, positive predictive value = 85%, and negative predictive value = 63%). Prediction was influenced by blood markers, operative course, and factors associated with worse disease burden.

CONCLUSIONS

In this multi-institutional cohort of dAC patients that underwent CRS ± HIPEC, AutoML performed well in predicting patients with ER. Variables suggestive of poor tumor biology were the most influential for prediction. Our work provides a framework for identifying patients with ER that might benefit from shorter interval surveillance early after surgery.

Does Surveillance-Detected Disease Progression Yield Superior Patient Outcomes in High-Grade Glioma?

BACKGROUND

Standard follow-up care for patients with high-grade glioma (HGG) involves routine surveillance imaging to detect disease progression, assess treatment response, and monitor clinical symptoms. Although logical in nature, evidence supporting this practice is limited. We hypothesize patients with tumor recurrence detected on routine surveillance imaging will experience superior outcomes relative to symptomatic detection, using measures of survival and postrecurrence neurologic function.

METHODS

Adult patients receiving treatment for HGG at our institution from 2004 to 2018 were identified, and data including tumor characteristics, imaging results, neurologic status, and survival were extracted from the medical records of patients meeting inclusion criteria. All participants were followed for a minimum of 12 months, or for survival duration. Survival and neurologic function differences were assessed using log rank and 2-sample t tests with 2-sided 0.05 alpha level of significance.

RESULTS

Of the 74 patients meeting inclusion criteria, 47 (63.5%) had recurrence detected via routine surveillance imaging, and 27 (36.5%) had symptomatic detection outside of the surveillance schedule. Neither median overall survival (14.8 months for surveillance and 15.7 months for symptomatic; P = 0.600) nor postrecurrence neurologic function (assessed by Karnofsky Performance Scale Index and Eastern Cooperative Oncology Group) differed between the surveillance and symptomatic detection groups (P = 0.699 and P = 0.908, respectively).

CONCLUSIONS

Recurrence detection occurring via routine surveillance imaging did not yield superior patient outcomes relative to symptomatic detection occurring outside of the standard surveillance schedule in patients with HGG. Further evaluation of surveillance imaging and alternative follow-up methods for this patient population may be warranted.