Quantitative evaluation of lesion response heterogeneity for superior prognostication of clinical outcome

Lokre, Ojaswita; Perk, Timothy G.; Weisman, Amy J.; Govindan, Rajkumar Munian; Chen, Song; Chen, Meijie; Eickhoff, Jens; Liu, Glenn; Jeraj, Robert

doi:10.1007/s00259-024-06764-0

Cited by 2 publications

(1 citation statement)

References 44 publications

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“…These models output a voxel-wise disease mask, which in turn, can be used for a variety of downstream clinical tasks. For instance, in (Weber et al 2021, Schott et al 2023, Lokre et al 2024 the deep learning-based disease delineations were used to extract image biomarkers, which were in turn used as inputs for a predictive model of patient outcome to treatment. The used delineation models, however, do not provide uncertainty information.…”

Section: Introduction 1overviewmentioning

confidence: 99%

Uncertainty quantification via localized gradients for deep learning-based medical image assessments

Schott,

Pinchuk,

Santoro-Fernandes

et al. 2024

Phys. Med. Biol.

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Objective. Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep learning models have been shown to be highly accurate across a variety of tasks, measures that indicate the reliability of these models are less established. Increasingly, uncertainty quantification (UQ) methods are being introduced to inform users on the reliability of model outputs. However, most existing methods cannot be augmented to previously validated models because they are not post hoc, and they change a model’s output. In this work, we overcome these limitations by introducing a novel post hoc UQ method, termed Local Gradients UQ, and demonstrate its utility for deep learning-based metastatic disease delineation. Approach. This method leverages a trained model’s localized gradient space to assess sensitivities to trained model parameters. We compared the Local Gradients UQ method to non-gradient measures defined using model probability outputs. The performance of each uncertainty measure was assessed in four clinically relevant experiments: (1) response to artificially degraded image quality, (2) comparison between matched high- and low-quality clinical images, (3) false positive (FP) filtering, and (4) correspondence with physician-rated disease likelihood. Main results. (1) Response to artificially degraded image quality was enhanced by the Local Gradients UQ method, where the median percent difference between matching lesions in non-degraded and most degraded images was consistently higher for the Local Gradients uncertainty measure than the non-gradient uncertainty measures (e.g., 62.35% vs. 2.16% for additive Gaussian noise). (2) The Local Gradients UQ measure responded better to high- and low-quality clinical images (p<0.05 vs p>0.1 for both non-gradient uncertainty measures). (3) FP filtering performance was enhanced by the Local Gradients UQ method when compared to the non-gradient methods, increasing the area under the receiver operating characteristic curve (ROC AUC) by 20.1% and decreasing the false positive rate by 26%. (4) The Local Gradients UQ method also showed more favorable correspondence with physician-rated likelihood for malignant lesions by increasing ROC AUC for correspondence with physician-rated disease likelihood by 16.2%. Significance. In summary, this work introduces and validates a novel gradient-based UQ method for deep learning-based medical image assessments to enhance user trust when using deployed clinical models.

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Section: Introduction 1overviewmentioning

confidence: 99%

Uncertainty quantification via localized gradients for deep learning-based medical image assessments

Schott,

Pinchuk,

Santoro-Fernandes

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

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Clinical Meaningfulness of an Algorithm-Based Service for Analyzing Treatment Response in Patients with Metastatic Cancer Using FDG PET/CT

Bupathi,

Garmezy,

Lattanzi

et al. 2024

JCM

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Background/Objectives: Determining how a patient with metastatic cancer is responding to therapy can be difficult for medical oncologists, especially with text-only radiology reports. In this investigation, we assess the clinical usefulness of a new algorithm-based analysis that provides spatial location and quantification for each detected lesion region of interest (ROI) and compare it to information included in radiology reports in the United States. Methods: Treatment response radiology reports for FDG PET/CT scans were retrospectively gathered from 228 patients with metastatic cancers. Each radiology report was assessed for the presence of both qualitative and quantitative information. A subset of patients (N = 103) was further analyzed using an algorithm-based service that provides the clinician with comprehensive quantitative information, including change over time, of all detected ROI with visualization of anatomical location. For each patient, three medical oncologists from different practices independently rated the usefulness of the additional analysis overall and in four subcategories. Results: In the 228 radiology reports, quantitative information of size and uptake was provided for at least one lesion at one time point in 78% (size) and 95% (uptake) of patients. This information was reported for both analyzed time points (current scan and previous comparator) in 52% (size) and 66% (uptake) of patients. Only 7% of reports quantified the total number of lesions, and none of the reports quantified changes in all lesions for patients with more than a few lesions. In the assessment of the augmentative algorithm-based analysis, the majority of oncologists rated it as overall useful for 98% of patients (101/103). Within specific categories of use, the majority of oncologists voted to use it for making decisions regarding systemic therapy in 97% of patients, for targeted therapy decisions in 72% of patients, for spatial location information in 96% of patients, and for patient education purposes in 93% of patients. Conclusions: For patients with metastatic cancer, the algorithm-based analysis of all ROI would allow oncologists to better understand treatment response and support their work to more precisely optimize the patient’s therapy.

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Quantitative evaluation of lesion response heterogeneity for superior prognostication of clinical outcome

Cited by 2 publications

References 44 publications

Uncertainty quantification via localized gradients for deep learning-based medical image assessments

Uncertainty quantification via localized gradients for deep learning-based medical image assessments

Clinical Meaningfulness of an Algorithm-Based Service for Analyzing Treatment Response in Patients with Metastatic Cancer Using FDG PET/CT

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