Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy. (July 2021)

• Record Type: Journal Article
• Authors: Thor, Maria; Iyer, Aditi; Jiang, Jue; Apte, Aditya; Veeraraghavan, Harini; Allgood, Natasha B.; Kouri, Jennifer A.; Zhou, Ying; LoCastro, Eve; Elguindi, Sharif; Hong, Linda; Hunt, Margie; Cerviño, Laura; Aristophanous, Michalis; Zarepisheh, Masoud; Deasy, Joseph O.
• Is Part Of: Physics and imaging in radiation oncology. Volume 19(2021)
• Page Start: 96
• Page End: 101
• Publication Date: 2021-07
• Subjects: Cancer -- Radiation -- Head neck -- Deep learning -- Masseter -- Medial pterygoid -- Mastication -- Chewing -- Trismus; Radiotherapy -- Periodicals; Radiation dosimetry -- Periodicals; Cancer -- Imaging -- Periodicals; Oncology -- Periodicals; 615.842
• DOI: 10.1016/j.phro.2021.07.009
• Languages: English
• ISSNs: 2405-6316

Abstract

Abstract: Background and Purpose: Reducing trismus in radiotherapy for head and neck cancer (HNC) is important. Automated deep learning (DL) segmentation and automated planning was used to introduce new and rarely segmented masticatory structures to study if trismus risk could be decreased. Materials and Methods: Auto-segmentation was based on purpose-built DL, and automated planning used our in-house system, ECHO. Treatment plans for ten HNC patients, treated with 2 Gy × 35 fractions, were optimized (ECHO0 ). Six manually segmented OARs were replaced with DL auto-segmentations and the plans re-optimized (ECHO1 ). In a third set of plans, mean doses for auto-segmented ipsilateral masseter and medial pterygoid (MIMean, MPIMean ), derived from a trismus risk model, were implemented as dose-volume objectives (ECHO2 ). Clinical dose-volume criteria were compared between the two scenarios (ECHO0 vs . ECHO1 ; ECHO1 vs . ECHO2 ; Wilcoxon signed-rank test; significance: p < 0.01). Results: Small systematic differences were observed between the doses to the six auto-segmented OARs and their manual counterparts (median: ECHO1 = 6.2 (range: 0.4, 21) Gy vs. ECHO0 = 6.6 (range: 0.3, 22) Gy; p = 0.007), and the ECHO1 plans provided improved normal tissue sparing across a larger dose-volume range. Only in the ECHO2 plans, all patients fulfilled both MIMean and MPIMean criteria. The population median MIMean and MPIMean were considerably lower than those suggested by the trismus model (ECHO0 : MIMean = 13 Gy vs. ≤42 Gy; MPIMean = 29 Gy vs. ≤68 Gy). Conclusions: Automated treatment planning can efficiently incorporate new structures from DL auto-segmentation, which results in trismus risk sparing without deteriorating treatment plan quality. Auto-planning and deep learning auto-segmentation together provide a powerful platform to further improve treatment planning.