M.P.A. Starmans

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Automated Assessment of T2-Weighted MRI to Differentiate Malignant and Benign Primary Solid Liver Lesions in Noncirrhotic Livers Using Radiomics
Starmans, M. P. A., Miclea, R. L., Vilgrain, V., Ronot, M., Purcell, Y., Verbeek, J., Niessen, W. J., Klein, S., Thomeer, M. G. & More Authors, 2024, In: Academic Radiology. 31, 3, p. 870-879 10 p.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
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8 Link opens in a new tab Citations (Scopus)

36 Downloads (Pure)
Classification of clinically significant prostate cancer on multi-parametric MRI: A validation study comparing deep learning and radiomics
Castillo T., J. M., Arif, M., Starmans, M. P. A., Niessen, W. J., Bangma, C. H., Schoots, I. G. & Veenland, J. F., 2022, In: Cancers. 14, 1, 15 p., 12.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
38 Link opens in a new tab Citations (Scopus)

217 Downloads (Pure)
A multi-center, multi-vendor study to evaluate the generalizability of a radiomics model for classifying prostate cancer: High grade vs. low grade
Castillo T., J. M., Starmans, M. P. A., Arif, M., Niessen, W. J., Klein, S., Bangma, C. H., Schoots, I. G. & Veenland, J. F., 2021, In: Diagnostics. 11, 2, 14 p., 369.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
50 Link opens in a new tab Citations (Scopus)

179 Downloads (Pure)
The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning
Angus, L., Starmans, M. P. A., Rajicic, A., Odink, A. E., Jalving, M., Niessen, W. J., Visser, J. J., Sleijfer, S., Klein, S. & van der Veldt, A. A. M., 2021, In: Journal of Personalized Medicine. 11, 4, 12 p., 257.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
3 Link opens in a new tab Citations (Scopus)

67 Downloads (Pure)

M.P.A. Starmans

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Collaborations and top research areas from the last five years

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Automated Assessment of T2-Weighted MRI to Differentiate Malignant and Benign Primary Solid Liver Lesions in Noncirrhotic Livers Using Radiomics

Classification of clinically significant prostate cancer on multi-parametric MRI: A validation study comparing deep learning and radiomics

A multi-center, multi-vendor study to evaluate the generalizability of a radiomics model for classifying prostate cancer: High grade vs. low grade

The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning

M.P.A. Starmans

Fingerprint

Collaborations and top research areas from the last five years

Research output

Automated Assessment of T2-Weighted MRI to Differentiate Malignant and Benign Primary Solid Liver Lesions in Noncirrhotic Livers Using Radiomics

Classification of clinically significant prostate cancer on multi-parametric MRI: A validation study comparing deep learning and radiomics

A multi-center, multi-vendor study to evaluate the generalizability of a radiomics model for classifying prostate cancer: High grade vs. low grade

The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning