Fully automated longitudinal tracking and in-depth analysis of the entire tumor burden: unlocking the complexity

Kuckertz, Sven; Klein, Jan; Engel, Christiane; Geisler, Benjamin; Kraß, Stefan; Heldmann, Stefan

doi:10.1117/12.2613080

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…A fourth advantage is performing whole-body lesion tracking. Previous works have been limited to brain (Metcalf et al 1992, Kikinis et al 1999, Gerig et al 2000, Shahar and Greenspan 2005, Köhler et al 2019, Kuckertz et al 2021, liver (Kuckertz et al 2022, Szeskin et al 2023, or bone tissue . The fifth advantage is performing lesion tracking with multiple image modalities (PET/CT and PET/MR), whereas previous works that developed whole-body lesion matching were limited to PET/CT (Hering et al 2021, Santoro-Fernandes et al 2021 or CT images (Yan et al 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Subsequent developments investigated individual tracking of brain lesions identified in MR images (Kikinis et al 1999, Gerig et al 2000, Bosc et al 2003, Köhler et al 2019, Kuckertz et al 2021. Methodologies for tracking of cancer lesions have also been investigated for lung, liver, and lymphatic lesions using CT images (Moltz et al 2009, Xu et al 2011, Kuckertz et al 2022, Szeskin et al 2023. Methodologies to track lesions spread through the whole-body were previously developed using PET/CT images, however these were constrained to bone or soft tissue lesions (Santoro-Fernandes et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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

Santoro-Fernandes,

Huff,

Rivetti

et al. 2024

Phys. Med. Biol.

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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 23 68Ga-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 vs. automatic dilation), anatomic location, image modalities (inter- vs. intra-modality), registration mode (direct vs. indirect registration), and track size (number of time-points and lesions) on lesion matching and tracking performance. Main Results: Manual contouring yielded 956 lesions, 1,570 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 vs. 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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Santoro-Fernandes,

Huff,

Rivetti

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

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“…Their dataset contained 1313 lesions (mean of 12.7 lesions/subject), which is most similar to the intermediate-burden cohort in our study. Finally, ‘tumor trees’, which are graph structures, were used in Kuckertz et al ( 2022 ) to describe progression of tumor burden in longitudinally imaged cancer patients. The investigators use a spatial overlap criterion to determine matches, but do not evaluate the accuracy of their method.…”

Section: Discussionmentioning

confidence: 99%

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

Huff,

Santoro-Fernandes,

Chen

et al. 2023

Phys. Med. Biol.

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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, Jaccard Index, 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 minutes, respectively, while automated matching took a median of 3.9 minutes. Significance: The automated lesion-matching algorithm was successful in performing lesion matching, meeting the benchmark of inter-reader variability. Automated lesion matching can significantly expedite and improve the consistency of longitudinal lesion-matching.

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Fully automated longitudinal tracking and in-depth analysis of the entire tumor burden: unlocking the complexity

Cited by 2 publications

References 7 publications

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

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

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

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