SUVmax to tumor perimeter distance: a robust radiomics prognostic biomarker in resectable non-small cell lung cancer patients

Londoño, G.A. Jiménez; Vicente, Ana María García; Bosque, Jesús J.; Amo-Salas, Mariano; Pérez-Beteta, Julián; Honguero-Martínez, Antonio Francisco; Pérez-Garcı́a, Vı́ctor M.; Castrejón, Á.M. Soriano

doi:10.1007/s00330-021-08523-3

Cited by 7 publications

(13 citation statements)

References 54 publications

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“…Prostate axial slices of 18F-DCFPyL PET/CT were visually assessed independently by two experienced observers belonging to two investigational groups. In PSMA-positive studies, automated prostate tumor segmentation was performed using aPROMISE software 13 and compared to the scientific software package Matlab (R2021b, MathWorks, Natick, Mass) using an in-house semiautomatic-manual guided segmentation procedure developed by the Mathematical Oncology Laboratory group (MOLab) based on a gradient algorithm detailed in previous publications 14 , 15 . Two nuclear medicine physicians revised all the procedures.…”

Section: Methodsmentioning

confidence: 99%

Analytical performance validation of aPROMISE platform for prostate tumor burden, index and dominant tumor assessment with 18F-DCFPyL PET/CT. A pilot study

García Vicente,

Lucas Lucas,

Pérez-Beteta

et al. 2024

Sci Rep

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To validate the performance of automated Prostate Cancer Molecular Imaging Standardized Evaluation (aPROMISE) in quantifying total prostate disease burden with 18F-DCFPyL PET/CT and to evaluate the interobserver and histopathologic concordance in the establishment of dominant and index tumor. Patients with a recent diagnosis of intermediate/high-risk prostate cancer underwent 18F-DCFPyL-PET/CT for staging purpose. In positive-18F-DCFPyL-PET/CT scans, automated prostate tumor segmentation was performed using aPROMISE software and compared to an in-house semiautomatic-manual guided segmentation procedure. SUV and volume related variables were obtained with two softwares. A blinded evaluation of dominant tumor (DT) and index tumor (IT) location was assessed by both groups of observers. In histopathological analysis, Gleason, International Society of Urological Pathology (ISUP) group, DT and IT location were obtained. We compared all the obtained variables by both software packages using intraclass correlation coefficient (ICC) and Cohen’s kappa coefficient (k) for the concordance analysis. Fifty-four patients with a positive 18F-DCFPyL PET/CT were evaluated. The ICC for the SUVmax, SUVpeak, SUVmean, tumor volume (TV) and total lesion activity (TLA) was: 1, 0.833, 0.615, 0.494 and 0.950, respectively (p < 0.001 in all cases). For DT and IT detection, a high agreement was observed between both softwares (k = 0.733; p < 0.001 and k = 0.812; p < 0.001, respectively) although the concordances with histopathology were moderate (p < 0001). The analytical validation of aPROMISE showed a good performance for the SUVmax, TLA, DT and IT definition in comparison to our in-house method, although the concordance was moderate with histopathology for DT and IT.

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Section: Methodsmentioning

confidence: 99%

Analytical performance validation of aPROMISE platform for prostate tumor burden, index and dominant tumor assessment with 18F-DCFPyL PET/CT. A pilot study

García Vicente,

Lucas Lucas,

Pérez-Beteta

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

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“…Prostate axial slices of 18 F-DCFPyL PET/CT were visually assessed independently by two experienced observers belonging to two investigational groups. In PSMA-positive studies, automated prostate tumor segmentation was performed using aPROMISE software [13] and compared to the scienti c software package Matlab (R2021b, MathWorks, Natick, Mass) using an in-house semiautomatic-manual guided segmentation procedure developed by the Mathematical Oncology group (MOLab) based on a gradient algorithm and detailed in previous publications [14,15]. Two nuclear medicine physicians revised all the procedures.…”

Section: Patientsmentioning

confidence: 99%

Analytical performance validation of aPROMISE platform for prostate tumor burden, index and dominant tumor assessment with 18F-DCFPyL PET/CT. A pilot study

Garcia-Vicente,

Lucas-Lucas,

Pérez-Beteta

et al. 2023

Preprint

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Aim To validate the performance of automated Prostate Cancer Molecular Imaging Standardized Evaluation (aPROMISE) in quantifying total prostate disease burden with 18F-DCFPyL PET/CT and to evaluate the interobserver and histopathologic concordance in the establishment of dominant and index tumour. Material and methods Patients with a recent diagnosis of intermediate/high risk prostate cancer underwent 18F-DCFPyL-PET/CT for staging purpose. In positive-PSMA scans, automated prostate tumor segmentation was performed using aPROMISE software and compared to an in-house semiautomatic-manual guided segmentation procedure. SUV and volume related variables were obtained with both software. A blinded evaluation of dominant tumor (DT) and index tumor (IT) location was assessed by both groups of observers. In histopathological analysis, Gleason, International Society of Urological Pathology (ISUP) group, DT and IT location were obtained. We compared all the obtained variables by both software packages using intraclass correlation coefficient (ICC) and Cohen’s kappa coefficient (k) for the concordance analysis. Results Fifty-four patients with a positive 18F-DCFPyL PET/CT were evaluated. The ICC for the SUVmax, SUVpeak, SUVmean, metabolic tumor volume (MTV) and total lesion activity (TLA) was: 1, 0.833, 0.615, 0.494 and 0.950, respectively (p<0.001 in all cases). For DT and IT detection, a high agreement was observed between both softwares (k=0.733; p<0.001 and k=0.812; p<0.001, respectively) although the concordances with histopathology were moderate (p<0001). Conclusions The analytical validation of aPROMISE shows a good performance for the SUVmax, TLA, DT and IT definition in comparison to our in-house method, although the concordance was moderate with histopathology for DT and IT.

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“…Geometric features include size measurements (diameters, volumetry, etc) and shape descriptors (sphericity, compactness, etc). Image intensity is characterized by histogram features, like energy, entropy, mean, variance, kurtosis, and other similar statistics, which are sometimes specific to imaging modalities like SUVs in PET (Leijenaar et al 2015, Orlhac et al 2021, Jiménez Londoño et al 2022. These first-order intensity features are complemented by second-order features that characterize textures in the images, i.e.…”

Section: Handcrafted Features (Aka Feature Engineering)mentioning

confidence: 99%

Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency

Montero¹,

Bibal²,

Huet³

et al. 2022

Phys. Med. Biol.

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The interest for machine learning (ML) has grown tremendously in recent years, partly due to the performance leap that occurred with new techniques of deep learning, convolutional neural networks for images, increased computational power, and wider availability of large data sets. Most fields of medicine follow that popular trend and, notably, radiation oncology is one of those that are at the forefront, with already a long tradition in using digital images and fully computerized workflows. ML models are driven by data, and in contrast with many statistical or physical models, they can be very large and complex, with countless generic parameters. This inevitably raises two questions, namely, the tight dependence between the models and the data sets that feed them, and the interpretability of the models, which scales with its complexity. Any problems in the data used to train the model will be later reflected in their performance. This, together with the low interpretability of ML models, makes their implementation into the clinical workflow particularly difficult. Building tools for risk assessment and quality assurance of ML models must involve then two main points: interpretability and data-model dependency. After a joint introduction of both radiation oncology and ML, this paper reviews the main risks and current solutions when applying the latter to workflows in the former. Risks associated with data and models, as well as their interaction, are detailed. Next, the core concepts of interpretability, explainability, and data-model dependency are formally defined and illustrated with examples. Afterwards, a broad discussion goes through key applications of ML in workflows of radiation oncology as well as vendors’ perspectives for the clinical implementation of ML.

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SUVmax to tumor perimeter distance: a robust radiomics prognostic biomarker in resectable non-small cell lung cancer patients

Cited by 7 publications

References 54 publications

Analytical performance validation of aPROMISE platform for prostate tumor burden, index and dominant tumor assessment with 18F-DCFPyL PET/CT. A pilot study

Analytical performance validation of aPROMISE platform for prostate tumor burden, index and dominant tumor assessment with 18F-DCFPyL PET/CT. A pilot study

Analytical performance validation of aPROMISE platform for prostate tumor burden, index and dominant tumor assessment with 18F-DCFPyL PET/CT. A pilot study

Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency

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