Resources Anytime, Anywhere

Litsey, Ryan

doi:10.1016/b978-0-08-101989-4.00003-1

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…The contrast-enhanced computed tomography (CT) scan dataset was originally published in 2019 as part of the KiTS19 grand challenge [66]. The KiTS21 dataset includes subjects from M Health Fairview [67] and Cleveland Clinic [68] medical centers who underwent either partial or radical nephrectomy between 2010 and 2018. Each of the 544 original cases was reviewed retrospectively to include only those patients who had also undergone a contrast-enhanced CT scan of the entire kidney(s) and corresponding renal tumors.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata

Mahmud,

Abbas,

Mushtak

et al. 2023

Cancers

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Kidney cancers are one of the most common malignancies worldwide. Accurate diagnosis is a critical step in the management of kidney cancer patients and is influenced by multiple factors including tumor size or volume, cancer types and stages, etc. For malignant tumors, partial or radical surgery of the kidney might be required, but for clinicians, the basis for making this decision is often unclear. Partial nephrectomy could result in patient death due to cancer if kidney removal was necessary, whereas radical nephrectomy in less severe cases could resign patients to lifelong dialysis or need for future transplantation without sufficient cause. Using machine learning to consider clinical data alongside computed tomography images could potentially help resolve some of these surgical ambiguities, by enabling a more robust classification of kidney cancers and selection of optimal surgical approaches. In this study, we used the publicly available KiTS dataset of contrast-enhanced CT images and corresponding patient metadata to differentiate four major classes of kidney cancer: clear cell (ccRCC), chromophobe (chRCC), papillary (pRCC) renal cell carcinoma, and oncocytoma (ONC). We rationalized these data to overcome the high field of view (FoV), extract tumor regions of interest (ROIs), classify patients using deep machine-learning models, and extract/post-process CT image features for combination with clinical data. Regardless of marked data imbalance, our combined approach achieved a high level of performance (85.66% accuracy, 84.18% precision, 85.66% recall, and 84.92% F1-score). When selecting surgical procedures for malignant tumors (RCC), our method proved even more reliable (90.63% accuracy, 90.83% precision, 90.61% recall, and 90.50% F1-score). Using feature ranking, we confirmed that tumor volume and cancer stage are the most relevant clinical features for predicting surgical procedures. Once fully mature, the approach we propose could be used to assist surgeons in performing nephrectomies by guiding the choices of optimal procedures in individual patients with kidney cancer.

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Section: Dataset Descriptionmentioning

confidence: 99%

Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata

Mahmud,

Abbas,

Mushtak

et al. 2023

Cancers

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Becoming Trusted Research Partners Through InfoExpress at The University of Toronto

Vrkljan

Findley-Jones

2020

Advances in Library and Information Science

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This case study discusses the importance of building initial trust in the relationship between researcher and academic library. Primary coverage serves the experience of two small humanities-based colleges serving approximately 125 faculty members within a larger university campus by providing the personal document delivery service of InfoExpress. The trust built through this initial research support service creates avenues for further support from the library and the wider university library system. As every relationship has challenges, the ones occurring here are opportunities to improve the relationship in favour of the researcher and library. If the researcher is unaware of what support the library provides, establishing a personal relationship will immediately provide productive research time and create an opportunity for future support through additional personalized services. The researcher, their research, and their library benefit by this trusted partnership.

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Resources Anytime, Anywhere

Cited by 2 publications

References 10 publications

Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata

Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata

Becoming Trusted Research Partners Through InfoExpress at The University of Toronto

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