PlatiPy: Processing Library and Analysis Toolkit for
Medical Imaging in Python

Chlap, Phillip; Finnegan, Robert

doi:10.21105/joss.05374

Cited by 14 publications

(2 citation statements)

References 12 publications

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“…Preprocessing parameters are described in detail in Supplement S 1 . Standard Python libraries, including SimpleITK [ 15 ], NiBabel [ 16 ], and PlatiPy [ 17 ], were used for processing volumetric medical imaging data. Multichannel images were split into separate volumetric images, and segmentations with multiple labels (e.g., for multiple ROIs, organs, or readers) were split into separate segmentations.…”

Section: Methodsmentioning

confidence: 99%

Addressing challenges in radiomics research: systematic review and repository of open-access cancer imaging datasets

Woznicki,

Laqua,

Al-Haj

et al. 2023

Insights Imaging

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Objectives Open-access cancer imaging datasets have become integral for evaluating novel AI approaches in radiology. However, their use in quantitative analysis with radiomics features presents unique challenges, such as incomplete documentation, low visibility, non-uniform data formats, data inhomogeneity, and complex preprocessing. These issues may cause problems with reproducibility and standardization in radiomics studies. Methods We systematically reviewed imaging datasets with public copyright licenses, published up to March 2023 across four large online cancer imaging archives. We included only datasets with tomographic images (CT, MRI, or PET), segmentations, and clinical annotations, specifically identifying those suitable for radiomics research. Reproducible preprocessing and feature extraction were performed for each dataset to enable their easy reuse. Results We discovered 29 datasets with corresponding segmentations and labels in the form of health outcomes, tumor pathology, staging, imaging-based scores, genetic markers, or repeated imaging. We compiled a repository encompassing 10,354 patients and 49,515 scans. Of the 29 datasets, 15 were licensed under Creative Commons licenses, allowing both non-commercial and commercial usage and redistribution, while others featured custom or restricted licenses. Studies spanned from the early 1990s to 2021, with the majority concluding after 2013. Seven different formats were used for the imaging data. Preprocessing and feature extraction were successfully performed for each dataset. Conclusion RadiomicsHub is a comprehensive public repository with radiomics features derived from a systematic review of public cancer imaging datasets. By converting all datasets to a standardized format and ensuring reproducible and traceable processing, RadiomicsHub addresses key reproducibility and standardization challenges in radiomics. Critical relevance statement This study critically addresses the challenges associated with locating, preprocessing, and extracting quantitative features from open-access datasets, to facilitate more robust and reliable evaluations of radiomics models. Key points - Through a systematic review, we identified 29 cancer imaging datasets suitable for radiomics research. - A public repository with collection overview and radiomics features, encompassing 10,354 patients and 49,515 scans, was compiled. - Most datasets can be shared, used, and built upon freely under a Creative Commons license. - All 29 identified datasets have been converted into a common format to enable reproducible radiomics feature extraction. Graphical Abstract

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Section: Methodsmentioning

confidence: 99%

Addressing challenges in radiomics research: systematic review and repository of open-access cancer imaging datasets

Woznicki,

Laqua,

Al-Haj

et al. 2023

Insights Imaging

View full text Add to dashboard Cite

show abstract

“…During training, a 3D block measuring 50 mm around the mediastinal tumour was cropped and augmented in random intensity shift (prob=0.5, offsets=2), random rotation (prob=0.5, rotation=90°), random flip (prob=0.5), random zoom (prob=0.5, min_ zoom=0.95, max_zoom=1.05) and random affine transform (prob=0.5) using Project MONAI. 10 The feature maps of the last hidden layer of the U-net were global average-pooled, max-pooled and concatenated into one vector with dimensions of 64. A linear projection was performed to obtain embeddings with dimensions of 128.…”

Section: Implementation Detailsmentioning

confidence: 99%

Multimodal modeling with low-dose CT and clinical information for diagnostic artificial intelligence on mediastinal tumors: a preliminary study

Yamada,

Kojima,

Otsuka

et al. 2024

BMJ Open Resp Res

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BackgroundDiagnosing mediastinal tumours, including incidental lesions, using low-dose CT (LDCT) performed for lung cancer screening, is challenging. It often requires additional invasive and costly tests for proper characterisation and surgical planning. This indicates the need for a more efficient and patient-centred approach, suggesting a gap in the existing diagnostic methods and the potential for artificial intelligence technologies to address this gap. This study aimed to create a multimodal hybrid transformer model using the Vision Transformer that leverages LDCT features and clinical data to improve surgical decision-making for patients with incidentally detected mediastinal tumours.MethodsThis retrospective study analysed patients with mediastinal tumours between 2010 and 2021. Patients eligible for surgery (n=30) were considered ‘positive,’ whereas those without tumour enlargement (n=32) were considered ‘negative.’ We developed a hybrid model combining a convolutional neural network with a transformer to integrate imaging and clinical data. The dataset was split in a 5:3:2 ratio for training, validation and testing. The model’s efficacy was evaluated using a receiver operating characteristic (ROC) analysis across 25 iterations of random assignments and compared against conventional radiomics models and models excluding clinical data.ResultsThe multimodal hybrid model demonstrated a mean area under the curve (AUC) of 0.90, significantly outperforming the non-clinical data model (AUC=0.86, p=0.04) and radiomics models (random forest AUC=0.81, p=0.008; logistic regression AUC=0.77, p=0.004).ConclusionIntegrating clinical and LDCT data using a hybrid transformer model can improve surgical decision-making for mediastinal tumours, showing superiority over models lacking clinical data integration.

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Dosimetric impact of variable air cavity within PTV for rectum cancer

Chan,

Goodall,

Finnegan

et al. 2024

J Applied Clin Med Phys

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PurposeThe aim of this study is to determine the impact of rectal air volume changes on treatment plan quality, and subsequently inform daily cone‐beam computed tomography (CBCT) evaluation constraints, in terms of acceptable rectal air volume during treatment.MethodsTwelve rectal cancer patients who exhibited rectal air within the PTV on their planning CT were selected. A study was conducted to evaluate the deterioration in plan quality due to expanding air volume. For each case, the air cavity volume was isotropically expanded in three dimensions using predefined margins of 3, 5, 7, and 10 mm, while deforming bladder and rectum contours. A constraint was applied to the bony anatomy to restrict the deformation. Treatment plans were then generated for all twelve patients by recalculating the reference plan with the expanded air cavity volume.ResultsAs the air cavity expanded, the maximum relative change in D98% coverage, compared to the reference plan, decreased by 10.8% ± 3.5%, while the D2% increased by 3.5% ± 0.9%. The positioning of the air cavity notably influenced the D98% variability with the 3 mm expansion. D98% coverage falls below 95% when the air cavity volume exceeds 17 cm3. On average, D2% coverage increased by 0.5% with each expansion. At the largest expansion, extensive coverage of 102% and 105% isodoses was observed compared to the reference plan.ConclusionAir cavity volumes above 17 cm3 can potentially degrade the high‐dose PTV coverage while increasing the regions covered by the 102% and 105% isodoses. Clinical CBCT guidelines were deduced, recommending a maximum threshold of 3.2 cm in diameter in any direction.

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PlatiPy: Processing Library and Analysis Toolkit for Medical Imaging in Python

Cited by 14 publications

References 12 publications

Addressing challenges in radiomics research: systematic review and repository of open-access cancer imaging datasets

Addressing challenges in radiomics research: systematic review and repository of open-access cancer imaging datasets

Multimodal modeling with low-dose CT and clinical information for diagnostic artificial intelligence on mediastinal tumors: a preliminary study

Dosimetric impact of variable air cavity within PTV for rectum cancer

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