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A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients

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A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients. / Peeken, Jan C; Shouman, Mohamed A; Kroenke, Markus; Rauscher, Isabel; Maurer, Tobias; Gschwend, Jürgen E; Eiber, Matthias; Combs, Stephanie E.

In: EUR J NUCL MED MOL I, Vol. 47, No. 13, 12.2020, p. 2968-2977.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Peeken, JC, Shouman, MA, Kroenke, M, Rauscher, I, Maurer, T, Gschwend, JE, Eiber, M & Combs, SE 2020, 'A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients', EUR J NUCL MED MOL I, vol. 47, no. 13, pp. 2968-2977. https://doi.org/10.1007/s00259-020-04864-1

APA

Peeken, J. C., Shouman, M. A., Kroenke, M., Rauscher, I., Maurer, T., Gschwend, J. E., Eiber, M., & Combs, S. E. (2020). A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients. EUR J NUCL MED MOL I, 47(13), 2968-2977. https://doi.org/10.1007/s00259-020-04864-1

Vancouver

Peeken JC, Shouman MA, Kroenke M, Rauscher I, Maurer T, Gschwend JE et al. A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients. EUR J NUCL MED MOL I. 2020 Dec;47(13):2968-2977. https://doi.org/10.1007/s00259-020-04864-1

Bibtex

@article{eb288b4ed454432ba5aa2d5e3f78839b,
title = "A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients",
abstract = "PURPOSE: In recurrent prostate carcinoma, determination of the site of recurrence is crucial to guide personalized therapy. In contrast to prostate-specific membrane antigen (PSMA)-positron emission tomography (PET) imaging, computed tomography (CT) has only limited capacity to detect lymph node metastases (LNM). We sought to develop a CT-based radiomic model to predict LNM status using a PSMA radioguided surgery (RGS) cohort with histological confirmation of all suspected lymph nodes (LNs).METHODS: Eighty patients that received RGS for resection of PSMA PET/CT-positive LNMs were analyzed. Forty-seven patients (87 LNs) that received inhouse imaging were used as training cohort. Thirty-three patients (62 LNs) that received external imaging were used as testing cohort. As gold standard, histological confirmation was available for all LNs. After preprocessing, 156 radiomic features analyzing texture, shape, intensity, and local binary patterns (LBP) were extracted. The least absolute shrinkage and selection operator (radiomic models) and logistic regression (conventional parameters) were used for modeling.RESULTS: Texture and shape features were largely correlated to LN volume. A combined radiomic model achieved the best predictive performance with a testing-AUC of 0.95. LBP features showed the highest contribution to model performance. This model significantly outperformed all conventional CT parameters including LN short diameter (AUC 0.84), LN volume (AUC 0.80), and an expert rating (AUC 0.67). In lymph node-specific decision curve analysis, there was a clinical net benefit above LN short diameter.CONCLUSION: The best radiomic model outperformed conventional measures for detection of LNM demonstrating an incremental value of radiomic features.",
author = "Peeken, {Jan C} and Shouman, {Mohamed A} and Markus Kroenke and Isabel Rauscher and Tobias Maurer and Gschwend, {J{\"u}rgen E} and Matthias Eiber and Combs, {Stephanie E}",
year = "2020",
month = dec,
doi = "10.1007/s00259-020-04864-1",
language = "English",
volume = "47",
pages = "2968--2977",
journal = "EUR J NUCL MED MOL I",
issn = "1619-7070",
publisher = "Springer",
number = "13",

}

RIS

TY - JOUR

T1 - A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients

AU - Peeken, Jan C

AU - Shouman, Mohamed A

AU - Kroenke, Markus

AU - Rauscher, Isabel

AU - Maurer, Tobias

AU - Gschwend, Jürgen E

AU - Eiber, Matthias

AU - Combs, Stephanie E

PY - 2020/12

Y1 - 2020/12

N2 - PURPOSE: In recurrent prostate carcinoma, determination of the site of recurrence is crucial to guide personalized therapy. In contrast to prostate-specific membrane antigen (PSMA)-positron emission tomography (PET) imaging, computed tomography (CT) has only limited capacity to detect lymph node metastases (LNM). We sought to develop a CT-based radiomic model to predict LNM status using a PSMA radioguided surgery (RGS) cohort with histological confirmation of all suspected lymph nodes (LNs).METHODS: Eighty patients that received RGS for resection of PSMA PET/CT-positive LNMs were analyzed. Forty-seven patients (87 LNs) that received inhouse imaging were used as training cohort. Thirty-three patients (62 LNs) that received external imaging were used as testing cohort. As gold standard, histological confirmation was available for all LNs. After preprocessing, 156 radiomic features analyzing texture, shape, intensity, and local binary patterns (LBP) were extracted. The least absolute shrinkage and selection operator (radiomic models) and logistic regression (conventional parameters) were used for modeling.RESULTS: Texture and shape features were largely correlated to LN volume. A combined radiomic model achieved the best predictive performance with a testing-AUC of 0.95. LBP features showed the highest contribution to model performance. This model significantly outperformed all conventional CT parameters including LN short diameter (AUC 0.84), LN volume (AUC 0.80), and an expert rating (AUC 0.67). In lymph node-specific decision curve analysis, there was a clinical net benefit above LN short diameter.CONCLUSION: The best radiomic model outperformed conventional measures for detection of LNM demonstrating an incremental value of radiomic features.

AB - PURPOSE: In recurrent prostate carcinoma, determination of the site of recurrence is crucial to guide personalized therapy. In contrast to prostate-specific membrane antigen (PSMA)-positron emission tomography (PET) imaging, computed tomography (CT) has only limited capacity to detect lymph node metastases (LNM). We sought to develop a CT-based radiomic model to predict LNM status using a PSMA radioguided surgery (RGS) cohort with histological confirmation of all suspected lymph nodes (LNs).METHODS: Eighty patients that received RGS for resection of PSMA PET/CT-positive LNMs were analyzed. Forty-seven patients (87 LNs) that received inhouse imaging were used as training cohort. Thirty-three patients (62 LNs) that received external imaging were used as testing cohort. As gold standard, histological confirmation was available for all LNs. After preprocessing, 156 radiomic features analyzing texture, shape, intensity, and local binary patterns (LBP) were extracted. The least absolute shrinkage and selection operator (radiomic models) and logistic regression (conventional parameters) were used for modeling.RESULTS: Texture and shape features were largely correlated to LN volume. A combined radiomic model achieved the best predictive performance with a testing-AUC of 0.95. LBP features showed the highest contribution to model performance. This model significantly outperformed all conventional CT parameters including LN short diameter (AUC 0.84), LN volume (AUC 0.80), and an expert rating (AUC 0.67). In lymph node-specific decision curve analysis, there was a clinical net benefit above LN short diameter.CONCLUSION: The best radiomic model outperformed conventional measures for detection of LNM demonstrating an incremental value of radiomic features.

U2 - 10.1007/s00259-020-04864-1

DO - 10.1007/s00259-020-04864-1

M3 - SCORING: Journal article

C2 - 32468251

VL - 47

SP - 2968

EP - 2977

JO - EUR J NUCL MED MOL I

JF - EUR J NUCL MED MOL I

SN - 1619-7070

IS - 13

ER -