An automated methodology for whole-body, multimodality tracking of individual cancer lesions.

Objective. Manual analysis of individual cancer lesions to assess disease response is clinically impractical and requires automated lesion tracking methodologies. However, no methodology has been developed for whole-body individual lesion tracking, across an arbitrary number of scans, and acquired with various imaging modalities.Approach. This study introduces a lesion tracking methodology and benchmarked it using 2368Ga-DOTATATE PET/CT and PET/MR images of eight neuroendocrine tumor patients. The methodology consists of six steps: (1) alignment of multiple scans via image registration, (2) body-part labeling, (3) automatic lesion-wise dilation, (4) clustering of lesions based on local lesion shape metrics, (5) assignment of lesion tracks, and (6) output of a lesion graph. Registration performance was evaluated via landmark distance, lesion matching accuracy was evaluated between each image pair, and lesion tracking accuracy was evaluated via identical track ratio. Sensitivity studies were performed to evaluate the impact of lesion dilation (fixed versus automatic dilation), anatomic location, image modalities (inter- versus intra-modality), registration mode (direct versus indirect registration), and track size (number of time-points and lesions) on lesion matching and tracking performance.Main results. Manual contouring yielded 956 lesions, 1570 lesion-matching decisions, and 493 lesion tracks. The median residual registration error was 2.5 mm. The automatic lesion dilation led to 0.90 overall lesion matching accuracy, and an 88% identical track ratio. The methodology is robust regarding anatomic locations, image modalities, and registration modes. The number of scans had a moderate negative impact on the identical track ratio (94% for 2 scans, 91% for 3 scans, and 81% for 4 scans). The number of lesions substantially impacted the identical track ratio (93% for 2 nodes versus 54% for ≥5 nodes).Significance. The developed methodology resulted in high lesion-matching accuracy and enables automated lesion tracking in PET/CT and PET/MR.

Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability.

Objective.Patients with metastatic disease are followed throughout treatment with medical imaging, and accurately assessing changes of individual lesions is critical to properly inform clinical decisions. The goal of this work was to assess the performance of an automated lesion-matching algorithm in comparison to inter-reader variability (IRV) of matching lesions between scans of metastatic cancer patients.Approach.Forty pairs of longitudinal PET/CT and CT scans were collected and organized into four cohorts: lung cancers, head and neck cancers, lymphomas, and advanced cancers. Cases were also divided by cancer burden: low-burden (<10 lesions), intermediate-burden (10-29), and high-burden (30+). Two nuclear medicine physicians conducted independent reviews of each scan-pair and manually matched lesions. Matching differences between readers were assessed to quantify the IRV of lesion matching. The two readers met to form a consensus, which was considered a gold standard and compared against the output of an automated lesion-matching algorithm. IRV and performance of the automated method were quantified using precision, recall, F1-score, and the number of differences.Main results.The performance of the automated method did not differ significantly from IRV for any metric in any cohort (p> 0.05, Wilcoxon paired test). In high-burden cases, the F1-score (median [range]) was 0.89 [0.63, 1.00] between the automated method and reader consensus and 0.93 [0.72, 1.00] between readers. In low-burden cases, F1-scores were 1.00 [0.40, 1.00] and 1.00 [0.40, 1.00], for the automated method and IRV, respectively. Automated matching was significantly more efficient than either reader (p< 0.001). In high-burden cases, median matching time for the readers was 60 and 30 min, respectively, while automated matching took a median of 3.9 minSignificance.The automated lesion-matching algorithm was successful in performing lesion matching, meeting the benchmark of IRV. Automated lesion matching can significantly expedite and improve the consistency of longitudinal lesion-matching.

Development and validation of a longitudinal soft-tissue metastatic lesion matching algorithm.

Metastatic cancer presents with many, sometimes hundreds of metastatic lesions through the body, which often respond heterogeneously to treatment. Therefore, lesion-level assessment is necessary for a complete understanding of disease response. Lesion-level assessment typically requires manual matching of corresponding lesions, which is a tedious, subjective, and error-prone task. This study introduces a fully automated algorithm for matching of metastatic lesions in longitudinal medical images. The algorithm entails four steps: (1) image registration, (2) lesion dilation, (3) lesion clustering, and (4) linear assignment. In step (1), 3D deformable registration is used to register the scans. In step (2), lesion contours are conformally dilated. In step (3), lesion clustering is evaluated based on local metrics. In step (4), matching is assigned based on non-greedy cost minimization. The algorithm was optimized (e.g. choice of deformable registration algorithm, dilatation size) and validated on 140 scan-pairs of 32 metastatic cancer patients from two independent clinical trials, who received longitudinal PET/CT scans as part of their treatment response assessment. Registration error was evaluated using landmark distance. A sensitivity study was performed to evaluate the optimal lesion dilation magnitude. Lesion matching performance accuracy was evaluated for all patients and for a subset with high disease burden. Two investigated deformable registration approaches (whole body deformable and articulated deformable registrations) led to similar performance with the overall registration accuracy between 2.3 and 2.6 mm. The optimal dilation magnitude of 25 mm yielded almost a perfect matching accuracy of 0.98. No significant matching accuracy decrease was observed in the subset of patients with high lesion disease burden. In summary, lesion matching using our new algorithm was highly accurate and a significant improvement, when compared to previously established methods. The proposed method enables accurate automated metastatic lesion matching in whole-body longitudinal scans.