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Book ; Thesis: Quantitative Biomarker zur bildgebenden Prädiktion von Krankheitsprogression und Therapiestratifizierung

Schulze-Hagen, Maximilian

2023

Author's details	vorgelegt von Dr. med. Maximilian Franz Schulze-Hagen, M.Sc
Language	English ; German
Size	119 Blätter, Illustrationen, Diagramme
Publishing place	Aachen
Publishing country	Germany
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Habilitationsschrift, Rheinisch-Westfälische Technische Hochschule Aachen, 2023
Note	Aufsätze in englisch, Text auf deutsch ; Die Habilitationsschrift besteht aus einem Text und 6 Aufsätzen, die zuvor in verschiedenen Zeitschriften/Publikationen veröffentlicht wurden
HBZ-ID	HT021734874
Database	Catalogue ZB MED Medicine, Health

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2023 E 1116	order for loan	Magazin

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Book ; Thesis: Primary anastomosis with defunctioning stoma versus Hartmann's procedure for perforated diverticulitis

Schulze-Hagen, Maximilian

a comparison of stoma reversal rates

2014

Author's details	vorgelegt von Maximilian Franz Schulze-Hagen
Language	English
Size	8 S. : graph. Darst.
Publishing country	Germany
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Aachen, Techn. Hochsch., Diss., 2014
HBZ-ID	HT018286766
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Article ; Online: Targeted multiparametric magnetic resonance imaging/transrectal ultrasound-guided (mpMRI/TRUS) fusion prostate biopsy versus systematic random prostate biopsy: A comparative real-life study.

Pham, Trang H N / Schulze-Hagen, Maximilian F / Rahnama'i, Mohammad S

Cancer reports (Hoboken, N.J.)

2024 Volume 7, Issue 2, Page(s) e1962

Abstract: Background: Patients with suspected prostate cancer usually undergo transrectal ultrasound-guided (TRUS) systematic biopsy, which can miss relevant prostate cancers and lead to overtreatment.: Aims: The aim of this study was to evaluate the detection ...

Abstract	Background: Patients with suspected prostate cancer usually undergo transrectal ultrasound-guided (TRUS) systematic biopsy, which can miss relevant prostate cancers and lead to overtreatment. Aims: The aim of this study was to evaluate the detection rate for prostate cancer in MR-guided targeted biopsy (TB) and systematic biopsy (SB) in comparison with mpMRI of the prostate. Methods and results: Three hundred and eight men who underwent mpMRI due to elevated PSA values between 2015 and 2020 were studied at university hospital Aachen, Germany. MRI-images were divided into cohorts with suspicious findings (PI-RADS ≥ 3) and negative findings (PI-RADS < 3). In patients with PI-RADS ≥ 3 TB combined with SB was performed. A part of this group underwent RP subsequently. In patients with PI-RADS < 3 and clinical suspicion SB was performed. In the PI-RADS ≥ 3 group (n = 197), TB combined with SB was performed in 194 cases. Three cases were lost to follow-up. Biopsy yielded 143 positive biopsies and 51 cases without carcinoma. TB detected 71% (102/143) and SB 98% (140/143) of the overall 143 carcinoma. Overall, 102 carcinomas were detected by TB, hereof 66% (67/102) clinically significant (Gleason ≥ 3+4) and 34% (35/102) clinically insignificant carcinoma (Gleason 3+3). SB detected 140 carcinomas, hereof 64% (90/140) csPCA and 36% (50/140) nsPCA. Forty-one of the overall 143 detected carcinoma were only found by SB, hereof 46% (19/41) csPCA and 54% (22/41) nsPCA. Tumor locations overlapped in 44% (63/143) between TB and SB. In 25% (36/143), SB detected additional tumor foci outside the target lesions. 70/143 patients subsequently underwent RP. The detection of tumor foci was congruent between mpMRI and prostatectomy specimen in 79% (55/70) of cases. Tumor foci were mpMRI occult in 21% (15/70) of cases. In the group with negative mpMRI (n = 111), biopsy was performed in 81 cases. Gleason ≥ 3+4 carcinoma was detected in 7% and Gleason 3+3 in 24% cases. Conclusion: There was a notable number of cases in which SB detected tumor foci that were mpMRI occult and could have been missed by TB alone. Therefore, additional systematic random biopsy is still required. A supplemental random biopsy should be considered depending on the overall clinical suspicion in negative mpMRI.
MeSH term(s)	Male ; Humans ; Prostate/pathology ; Multiparametric Magnetic Resonance Imaging ; Prostatic Neoplasms/diagnostic imaging ; Prostatic Neoplasms/pathology ; Magnetic Resonance Imaging/methods ; Prospective Studies ; Image-Guided Biopsy/methods ; Ultrasonography, Interventional/methods ; Carcinoma/pathology
Language	English
Publishing date	2024-01-12
Publishing country	United States
Document type	Journal Article
ISSN	2573-8348
ISSN (online)	2573-8348
DOI	10.1002/cnr2.1962
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: In Reply.

Kuhl, Christiane / Schulze-Hagen, Maximilian / Bieling, Heribert

Deutsches Arzteblatt international

2021 Volume 118, Issue 5, Page(s) 66

MeSH term(s)	COVID-19 ; Humans ; Probability ; SARS-CoV-2 ; Tomography, X-Ray Computed
Language	English
Publishing date	2021-03-11
Publishing country	Germany
Document type	Letter ; Comment
ZDB-ID	2406159-1
ISSN	1866-0452 ; 1866-0452
ISSN (online)	1866-0452
ISSN	1866-0452
DOI	10.3238/arztebl.m2021.0037
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Article ; Online: Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T.

Lemainque, Teresa / Yoneyama, Masami / Morsch, Chiara / Iordanishvili, Elene / Barabasch, Alexandra / Schulze-Hagen, Maximilian / Peeters, Johannes M / Kuhl, Christiane / Zhang, Shuo

Magnetic resonance imaging

2024 Volume 110, Page(s) 96–103

Abstract: Purpose: Further acceleration of DWI in diagnostic radiology is desired but challenging mainly due to low SNR in high b-value images and associated bias in quantitative ADC values. Deep learning-based reconstruction and denoising may provide a solution ... ...

Abstract	Purpose: Further acceleration of DWI in diagnostic radiology is desired but challenging mainly due to low SNR in high b-value images and associated bias in quantitative ADC values. Deep learning-based reconstruction and denoising may provide a solution to address this challenge. Methods: The effects of SNR reduction on ADC bias and variability were investigated using a commercial diffusion phantom and numerical simulations. In the phantom, performance of different reconstruction methods, including conventional parallel (SENSE) imaging, compressed sensing (C-SENSE), and compressed SENSE acceleration with an artificial intelligence deep learning-based technique (C-SENSE AI), was compared at different acceleration factors and flip angles using ROI-based analysis. ADC bias was assessed by Lin's Concordance correlation coefficient (CCC) followed by bootstrapping to calculate confidence intervals (CI). ADC random measurement error (RME) was assessed by the mean coefficient of variation (CV¯) and non-parametric statistical tests. Results: The simulations predicted increasingly negative bias and loss of precision towards lower SNR. These effects were confirmed in phantom measurements of increasing acceleration, for which CCC decreased from 0.947 to 0.279 and CV¯ increased from 0.043 to 0.439, and of decreasing flip angle, for which CCC decreased from 0.990 to 0.063 and CV¯ increased from 0.037 to 0.508. At high acceleration and low flip angle, C-SENSE AI reconstruction yielded best denoised ADC maps. For the lowest investigated flip angle, CCC = {0.630, 0.771 and 0.987} and CV¯={0.508, 0.426 and 0.254} were obtained for {SENSE, C-SENSE, C-SENSE AI}, the improvement by C-SENSE AI being significant as compared to the other methods (CV: p = 0.033 for C-SENSE AI vs. C-SENSE and p < 0.001 for C-SENSE AI vs. SENSE; CCC: non-overlapping CI between reconstruction methods). For the highest investigated acceleration factor, CCC = {0.479,0.926,0.960} and CV¯={0.519,0.119,0.118} were found, confirming the reduction of bias and RME by C-SENSE AI as compared to C-SENSE (by trend) and to SENSE (CV: p < 0.001; CCC: non-overlapping CI). Conclusion: ADC bias and random measurement error in DWI at low SNR, typically associated with scan acceleration, can be effectively reduced by deep-learning based C-SENSE AI reconstruction.
Language	English
Publishing date	2024-04-15
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	604885-7
ISSN	1873-5894 ; 0730-725X
ISSN (online)	1873-5894
ISSN	0730-725X
DOI	10.1016/j.mri.2024.04.018
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Article ; Online: Can the predictive value of multiparametric MRI for prostate cancer be improved by a liquid biopsy with SelectMDx?

Rahnama'i, Mohammad Sajjad / Bach, Christian / Schulze-Hagen, Maximilian / Kuhl, Christiane K / Vögeli, Thomas Alexander

Cancer reports (Hoboken, N.J.)

2021 Volume 4, Issue 6, Page(s) e1396

Abstract: Background: SelectMDx is a urinary biomarker test for determining prostate cancer risk.: Aim: In a group of patients with a biopsy proven prostate cancer (PCa) who had undergone a multi parametric Magnetic Resonance Imaging (mpMRI) and urinary ... ...

Abstract	Background: SelectMDx is a urinary biomarker test for determining prostate cancer risk. Aim: In a group of patients with a biopsy proven prostate cancer (PCa) who had undergone a multi parametric Magnetic Resonance Imaging (mpMRI) and urinary biomarker test with SelectMDx, we studied the additive value of SelectMDx to mpMRI and correlated that to the radical prostatectomy histology. Methods and results: Thirty-nine consecutive patients with a positive prostate biopsy were included in the study. They all had mpMRI and SelectMDx and underwent a radical prostatectomy. Overall, the mpMRI showed a PIRADS ≤3 lesion in seven cases out of the 39 patients. Significant lesions (PIRADS ≥4) were found in 32 cases (82%), that is, in 17 cases a PIRADS 5 lesion and in 15 cases a PIRADS 4 lesion. The mpMRI missed significant PCa in seven cases (18%) who had a PIRADS ≤3 lesion but had a significant PCa on final histology after RP. In our study, the positive predictive values of mpMRI were 97% and that of the SelectMDx was 100%. Conclusion: In this real-life selected group of consecutive patients with a confirmed positive PCa biopsy and available mpMRI, the liquid biopsy test with SelectMDx, did not provide an additional information about the PCa clinical significance. The addition of SelectMDx was only found valuable in those patients who had a very high-risk PCa (ie, GS ≥8) who had a positive SelectMDx test outcome despite of a negative mpMRI outcome.
MeSH term(s)	Aged ; Biomarkers, Tumor/genetics ; Biomarkers, Tumor/urine ; Follow-Up Studies ; Humans ; Liquid Biopsy/methods ; Male ; Middle Aged ; Multiparametric Magnetic Resonance Imaging/methods ; Prognosis ; Prostatectomy/methods ; Prostatic Neoplasms/diagnosis ; Prostatic Neoplasms/genetics ; Prostatic Neoplasms/urine
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2021-05-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2573-8348
ISSN (online)	2573-8348
DOI	10.1002/cnr2.1396
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Reliability as a Precondition for Trust-Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction.

Müller-Franzes, Gustav / Nebelung, Sven / Schock, Justus / Haarburger, Christoph / Khader, Firas / Pedersoli, Federico / Schulze-Hagen, Maximilian / Kuhl, Christiane / Truhn, Daniel

Diagnostics (Basel, Switzerland)

2022 Volume 12, Issue 2

Abstract: Machine learning results based on radiomic analysis are often not transferrable. A potential reason for this is the variability of radiomic features due to varying human made segmentations. Therefore, the aim of this study was to provide comprehensive ... ...

Abstract	Machine learning results based on radiomic analysis are often not transferrable. A potential reason for this is the variability of radiomic features due to varying human made segmentations. Therefore, the aim of this study was to provide comprehensive inter-reader reliability analysis of radiomic features in five clinical image datasets and to assess the association of inter-reader reliability and survival prediction. In this study, we analyzed 4598 tumor segmentations in both computed tomography and magnetic resonance imaging data. We used a neural network to generate 100 additional segmentation outlines for each tumor and performed a reliability analysis of radiomic features. To prove clinical utility, we predicted patient survival based on all features and on the most reliable features. Survival prediction models for both computed tomography and magnetic resonance imaging datasets demonstrated less statistical spread and superior survival prediction when based on the most reliable features. Mean concordance indices were C
Language	English
Publishing date	2022-01-19
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics12020247
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Conference proceedings: Ein retrospektiver Vergleich der gezielten MRT/TRUS Fusionsbiopsie mit systematischer Biopsie mit der Detektionsrate von Prostatakarzinomen in der mpMRT

Pham, Trang / Schulze-Hagen, Maximilian / Kuhl, Christiane / Saar, Matthias / Rahnama'i, Sajjad

2022 , Page(s) P 1.3

Event/congress	67. Kongress der Nordrhein-Westfälischen Gesellschaft für Urologie; Münster; Nordrhein-Westfälische Gesellschaft für Urologie; 2022
Keywords	Medizin, Gesundheit
Publishing date	2022-03-01
Publisher	German Medical Science GMS Publishing House; Düsseldorf
Document type	Conference proceedings
DOI	10.3205/22nrwgu67
Database	German Medical Science

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Article ; Online: Automated major psoas muscle volumetry in computed tomography using machine learning algorithms.

Duong, Felix / Gadermayr, Michael / Merhof, Dorit / Kuhl, Christiane / Bruners, Philipp / Loosen, Sven H / Roderburg, Christoph / Truhn, Daniel / Schulze-Hagen, Maximilian F

International journal of computer assisted radiology and surgery

2021 Volume 17, Issue 2, Page(s) 355–361

Abstract: Purpose: The psoas major muscle (PMM) volume serves as an opportunistic imaging marker in cross-sectional imaging datasets for various clinical applications. Since manual segmentation is time consuming, two different automated segmentation methods, a ... ...

Abstract	Purpose: The psoas major muscle (PMM) volume serves as an opportunistic imaging marker in cross-sectional imaging datasets for various clinical applications. Since manual segmentation is time consuming, two different automated segmentation methods, a generative adversarial network architecture (GAN) and a multi-atlas segmentation (MAS), as well as a combined approach of both, were investigated in terms of accuracy of automated volumetrics in given CT datasets. Materials and methods: The bilateral PMM was manually segmented by a radiologist in 34 abdominal CT scans, resulting in 68 single 3D muscle segmentations as training data. Three different methods were tested for their ability to generate automated image segmentations: a GAN- and MAS-based approach and a combined approach of both methods (COM). Bilateral PMM volume (PMMV) was calculated in cm Results: Mean PMMV was 239 ± 7.0 cm Conclusion: The combined approach was able to efficiently exploit the advantages of both methods (GAN and MAS), resulting in a significantly higher accuracy in PMMV predictions compared to the isolated implementations of both methods. Even with the relatively small set of training data, the segmentation accuracy of this hybrid approach was relatively close to that of the radiologist.
MeSH term(s)	Algorithms ; Humans ; Image Processing, Computer-Assisted ; Machine Learning ; Psoas Muscles/diagnostic imaging ; Tomography, X-Ray Computed
Language	English
Publishing date	2021-12-20
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2365628-1
ISSN	1861-6429 ; 1861-6410
ISSN (online)	1861-6429
ISSN	1861-6410
DOI	10.1007/s11548-021-02539-2
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Article ; Online: Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction

Gustav Müller-Franzes / Sven Nebelung / Justus Schock / Christoph Haarburger / Firas Khader / Federico Pedersoli / Maximilian Schulze-Hagen / Christiane Kuhl / Daniel Truhn

Diagnostics, Vol 12, Iss 247, p

2022 Volume 247

Abstract	Machine learning results based on radiomic analysis are often not transferrable. A potential reason for this is the variability of radiomic features due to varying human made segmentations. Therefore, the aim of this study was to provide comprehensive inter-reader reliability analysis of radiomic features in five clinical image datasets and to assess the association of inter-reader reliability and survival prediction. In this study, we analyzed 4598 tumor segmentations in both computed tomography and magnetic resonance imaging data. We used a neural network to generate 100 additional segmentation outlines for each tumor and performed a reliability analysis of radiomic features. To prove clinical utility, we predicted patient survival based on all features and on the most reliable features. Survival prediction models for both computed tomography and magnetic resonance imaging datasets demonstrated less statistical spread and superior survival prediction when based on the most reliable features. Mean concordance indices were C mean = 0.58 [most reliable] vs. C mean = 0.56 [all] ( p < 0.001, CT) and C mean = 0.58 vs. C mean = 0.57 ( p = 0.23, MRI). Thus, preceding reliability analyses and selection of the most reliable radiomic features improves the underlying model’s ability to predict patient survival across clinical imaging modalities and tumor entities.
Keywords	radiomic features ; overall survival ; segmentation variability ; inter-rater reliability ; neural network ; robustness ; Medicine (General) ; R5-920
Subject code	519
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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