Dosimetric impact of weight loss and anatomical changes at organs at risk during intensity-modulated radiotherapy for head-and-neck cancer

Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients

Purpose

This study aimed to evaluate the clinical need for an automated decision-support software platform for adaptive radiation therapy (ART) of head and neck cancer (HNC) patients.

Methods

We tested RTapp (SegAna), a new ART software platform for deciding when a treatment replan is needed, to investigate a set of 27 HNC patients’ data retrospectively. For each fraction, the software estimated key components of ART such as daily dose distribution and cumulative doses received by targets and organs at risk (OARs) from daily 3D imaging in real-time. RTapp also included a prediction algorithm that analyzed dosimetric parameter (DP) trends against user-specified thresholds to proactively trigger adaptive re-planning up to four fractions ahead. The DPs evaluated for ART were based on treatment planning dose constraints. Warning (V₉₅<95%) and adaptation (V₉₅<93%) thresholds were set for PTVs, while OAR adaptation dosimetric endpoints of +10% (DE₁₀) were set for all D_max and D_mean DPs. Any threshold violation at end of treatment (EOT) triggered a review of the DP trends to determine the threshold-crossing fraction Fx when the violations occurred. The prediction model accuracy was determined as the difference between calculated and predicted DP values with 95% confidence intervals (CI₉₅).

Results

RTapp was able to address the needs of treatment adaptation. Specifically, we identified 18/27 studies (67%) for violating PTV coverage or parotid D_mean at EOT. Twelve PTVs had V₉₅<95% (mean coverage decrease of −6.8 ± 2.9%) including six flagged for adaptation at median Fx= 6 (range, 1–16). Seventeen parotids were flagged for exceeding D_mean dose constraints with a median increase of +2.60 Gy (range, 0.99–6.31 Gy) at EOT, including nine with DP>DE₁₀. The differences between predicted and calculated PTV V₉₅ and parotid D_mean was up to 7.6% (mean ± CI₉₅, −2.7 ± 4.1%) and 5 Gy (mean ± CI₉₅, 0.3 ± 1.6 Gy), respectively. The most accurate predictions were obtained closest to the threshold-crossing fraction. For parotids, the results showed that Fx ranged between fractions 1 and 23, with a lack of specific trend demonstrating that the need for treatment adaptation may be verified for every fraction.

Conclusion

Integrated in an ART clinical workflow, RTapp aids in predicting whether specific treatment would require adaptation up to four fractions ahead of time.

The Impact of Weight Loss during Chemoradiotherapy for Unresectable Esophageal Cancer: Real-World Results

Weight loss is a common phenomenon presented in unresectable esophageal cancer (EC) patients during their definitive chemoradiotherapy (dCRT) treatment course. This study explored the prognostic value of weight changes during dCRT in unresectable EC patients. From 2009 to 2017, 69 cT4b thoracic EC patients undergoing complete curative dCRT without baseline malnutrition were included. Clinical factors were analyzed via the Cox proportional hazards model and survival was analyzed by the Kaplan−Meier method. During dCRT, the median weight loss percentage was 5.51% (IQR = 2.77−8.85%), and the lowest body weight was reached at 35 days (IQR = 23−43 days). Median OS of these patients was 13.5 months. Both univariate and multivariate analysis demonstrated that weight loss ≤ 4% during dCRT was significantly associated with superior OS with a hazard ratio of 2.61 (95% CI: 1.40−4.85, p = 0.002). The median OS for patients with weight loss ≤ 4% and >4% during dCRT was 59.6 months and 9.7 months, respectively (p = 0.001). Our study demonstrated that weight loss ≤ 4% during dCRT course is a favorable prognostic factor for cT4b EC patients. This index could serve as a nutrition support reference for unresectable EC patients receiving dCRT in the future.

Improved adaptive radiotherapy to adjust for anatomical alterations during curative treatment for locally advanced lung cancer

Anatomical changes during chemoradiation for lung cancer may decrease dose to the target or increase dose to organs at risk. To assess our ability to identify clinically significant anatomical alterations, we followed 67 lung cancer patients by daily cone-beam CT scans to ensure correct patient positioning and observe anatomical alterations. We also re-calculated the original dose distribution on a planned control CT scan obtained halfway during the treatment course to identify anatomical changes that potentially affected doses to the target or organs at risk. Of 66 patients who completed the treatment, 12 patients needed adaptation, two patients were adapted twice. We conclude that daily cone-beam CT and routines at the treatment machine discover relevant anatomical changes during curative radiotherapy for patients with lung cancer without additional imaging.