First systematic clinical study on detection of anatomical changes in PT using prompt-gamma imaging


First systematic clinical study on detection of anatomical changes in PT using prompt-gamma imaging

Berthold, J.; Jost, A.; Khamfongkhruea, C.; Petzoldt, J.; Thiele, J.; Hölscher, T.; Wohlfahrt, P.; Pausch, G.; Janssens, G.; Smeets, J.; Richter, C.

Purpose & Objective
Anatomical changes during the course of proton therapy treatment can result in relevant changes in proton range, potentially causing severe under- or overdosage. Verifying the proton treatment, ideally in real-time, is thus highly desirable. Here, we present the first systematic evaluation of the sensitivity of a prompt-gamma-imaging (PGI) based range verification system to detect anatomical changes in prostate-cancer treatments.

Materials & Methods
A PGI slit-camera system was clinically applied to monitor spot-wise proton range deviations during 7 and 9 fractions of hypo-fractionated pencil beam scanning (PBS) treatment for 2 prostate-cancer patients, respectively (2 opposing fields, 1.5 GyE each). For all monitored fractions, in-room control CT scans (cCT) were acquired in treatment position, serving as ground-truth reference. Based on the evaluation of planning CT (pCT) and cCT data on the level of CT images, dose distributions and derived line-dose profiles, anatomical changes were identified and scored concerning cause and magnitude. The detectability of these changes with PGI was determined by manually comparing expected range shifts from line-dose profiles (pCT vs. cCT) with PGI-derived spot-wise range shifts for distal PBS spots (Fig.1). This evaluation was performed for both, measured as well as simulated PGI data based on cCT (no statistical uncertainty). Furthermore, the sensitivity for a binary differentiation between relevant (strong/moderate) and no relevant anatomical changes within a fraction was determined. Working towards an automated classification of treatment deviations for real-time treatment verification, a simple two-parametric model was established to classify each monitored field into global, local and not clinically relevant anatomical changes.

Results
From 64 detected anatomical changes in 32 monitored treatment fields, in total 66% (84%) were also identified by measured (simulated) PGI data (Fig.2a). All strong changes (14/64) were identified correctly. For the differentiation between relevant from non-relevant changes, a sensitivity of 69% (95%) was achieved for measured (simulated) PGI data. The first attempt for automated classification was able to reliably differentiate global from local changes (Fig.2b). However, it was more difficult to distinguish treatments with no relevant from local anatomical changes. Also in the ground-truth classification, this decision was sometimes also borderline.

Conclusion
In the first systematic investigation of the sensitivity of PGI-based treatment verification in clinical prostate-cancer treatments, its capability to detect strong anatomical changes has been clearly demonstrated. In clinical PGI application the sensitivity is a bit smaller than for idealized PGI simulations, still severe changes were detected for all cases. The next step is to establish a reliable automated interpretation of PGI data. In a first trial, we established a two-parametric prediction model with already encouraging results.

  • Lecture (Conference) (Online presentation)
    ESTRO 2020, 28.11.-01.12.2020, Wien, Österreich
  • Abstract in refereed journal
    Radiotherapy and Oncology 152(2020)Supplement, S244-S245
    DOI: 10.1016/S0167-8140(21)00465-5

Permalink: https://www.hzdr.de/publications/Publ-29807
Publ.-Id: 29807