Clinical Acceptability of Automatically Generated Elective Lymph Node Volumes for Head and Neck Cancer Patients

PURPOSE/OBJECTIVE(S)

Manual contouring of head and neck lymph node levels is a time-intensive process prone to provider-specific variation. The purpose of this work is to generate a clinical segmentation tool while minimizing the amount of manual effort required by physicians to develop training datasets and review contours. Here we investigate an approach to curate, develop, and clinically validate an auto-contouring model for standard cervical lymph node volumes in the head and neck using a publicly available deep learning architecture. This model updates our previously validated tool to reflect modern practices in lymph node segmentation.

MATERIALS/METHODS

With the assistance of a resident physician, five radiation oncologists manually contoured individual lymph node levels on CT scans for three separate patients treated definitively with radiation or chemoradiation for oropharynx cancer, resulting in 15 unique ground truth cases. These cases were then used to train an nnUnet deep-learning model to generate automated contours for 32 additional cases. These 32 cases were reviewed, manually edited, and used to create the final model. Finally, the model was used to generate contours on the original 15 CT scans (testing cohort), and providers compared these automated contours with the ground-truth (manual) contours. Two blinded studies were performed. In a double-blinded fashion, providers were first asked to select which set of contours they would prefer to use in clinical practice as a starting point for actual cases. Second, they scored each contour on a Likert scale (1-5) to indicate clinical acceptability, ranging from completely unusable to usable without modification.

RESULTS

Across all lymph node levels (IA, IB, II, III, IV, V, RP), average Dice Similarity Coefficient ranged from 0.77 to 0.89 for AI vs manual contours in the testing cohort. These AI and manual lymph node contours were reviewed by 5 physicians each, resulting in 525 preference scores. Across all lymph nodes, the AI contour was superior to or equally preferred to the manual contours at rates ranging from 75% to 91% in the first blinded study. In the second blinded study, physician preference for the manual vs AI contour was statistically different for only the RP contours (p < 0.01). Thus, there was not a significant difference in clinical acceptability for nodal levels I-V for manual versus AI contours. Across all physician-generated contours, 82% were rated as usable with stylistic to no edits, and across all AI-generated contours, 92% were rated as usable with stylistic to no edits.

CONCLUSION

An approach to generate clinically acceptable automated contours for cervical lymph node levels in the head and neck was demonstrated. Furthermore, for nodal levels I-V, there was no significant difference in clinical acceptability in manual vs AI contours. Because we were able to generate and validate a model for each lymph node level individually, the output is applicable to a complete range of disease in which cervical lymph nodes are treated.

Cobalt Compensator-Based IMRT for Gynecologic Cancer Treatment in Low- and Middle-Income Countries: Equivalence to LINAC-Based IMRT

PURPOSE/OBJECTIVE(S)

Cobalt intensity modulated radiation therapy (IMRT) has the potential to impact global oncology by improving access to precision radiation therapy, particularly in low- and middle-income countries, and a novel compensator-based machine has been developed. Previously, cobalt compensator-based IMRT and LINAC-based IMRT were shown to achieve similar target goals and dose constraints for head and neck clinical cases. Additionally, the fidelity of those calculations was supported by Monte Carlo simulations and gamma analysis. The current study investigates the use of cobalt compensator IMRT for another application - gynecologic cancer patients.

MATERIALS/METHODS

A commercial treatment planning system was previously commissioned for the cobalt compensator-based device using Monte Carlo simulations from a simulation toolkit. Patient-specific compensators were modeled. Clinical gynecologic cancer cases were planned and compared to clinical plans with a 6MV LINAC using IMRT. The prescribed dose was 45 Gy in 25 fractions. Dosimetric plan quality endpoints for the planning target volume (PTV) and organs-at-risk (OARs) were compared. Parametric t-testing was used for statistical analysis, and criteria for significance was p<0.05.

RESULTS

Dose statistics of 10 cases showed similar target coverage and normal tissue sparing between IMRT plans using a 6 MV LINAC and using a cobalt compensator-based device. Greater than 95% of the dose to greater than 95% of the PTV was achieved for all 10 cases using both the LINAC-based and cobalt compensator-based plans. There was no significant difference in the mean percentage of the PTV receiving 95% of the dose between LINAC-based and cobalt compensator-based plans (98.0 ± 1.88% vs 97.1 ± 1.90%, respectively, p = 0.313). Additionally, both the LINAC-based and cobalt compensator-based plans, for all 10 cases, met dose constraints for the OARs, including Spinal Cord Dmax < 4500 cGy, Kidney Dmean < 1800 cGy, Bladder Dmax < 5750 cGy, Rectum Dmax < 5500 cGy, Small Bowel Dmax < 5400 cGy, Bone Marrow V4000 < 37%, and Femoral Heads Dmax < 5000 cGy.

CONCLUSION

Cobalt compensator-based IMRT may provide comparable quality treatment plans to LINAC-based IMRT for gynecologic cancer patients. Physical development of the cobalt compensator device has been completed and will be commissioned for further research to assess clinical outcomes.

A multiparameter optimization of digital mammography

Multiparameter optimizations have been carried out for a wide range of digital mammography system configurations and requirements, with the aim of optimizing the image quality for a given patient dose. These conditions include a range of slot widths for scanning mammography systems, exposure times from 1 to 10 s, focal spot sizes from 80 to 800 microns, a range of detector resolutions and noise levels, dose restrictions, patient thicknesses and targets, and x-ray tube targets. The influences of these on the optimum system configuration in terms of tube potential, filtration, source to patient distance and target magnification are discussed. It is demonstrated that x-ray tube power constraints can significantly restrict the optimum magnification for slot scanning systems, with the result that poor-resolution detectors are not suited for use in a scanning configuration, and that large-focal-spot-good-detector resolution combinations are more suitable. The use of a detector with increased width, raised tube potential and reduced amount of added filtration is shown to be helpful in reducing x-ray tube power limitations. It is shown that, in many cases, correct optimization can bring the detail SNR for an examination using a given detector-x-ray tube configuration to within 10-15% of the SNR achieved with the optimum combination. This gives the designer some scope to consider other factors such as cost and the implications of image size on storage space.