Michael Golatta

Heidelberg University — Germany

Biochemistry, Genetics and Molecular Biology · Life Sciences

39h-index4.4kcitations330works7.62yr avg
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Research Topics

Breast Cancer Treatment Studies(157), Breast Lesions and Carcinomas(87), Breast Implant and Reconstruction(54), AI in cancer detection(32), Radiomics and Machine Learning in Medical Imaging(31)

Publications330 total

Deep Learning Model for Breast Shear Wave Elastography to Improve Breast Cancer Diagnosis (INSPiRED 006): An International, Multicenter Analysis
Journal of Clinical Oncology·2025· 2 cited
Abstract P3-10-22: Preoperative radiotherapy versus postoperative radiotherapy after neoadjuvant chemotherapy in high-risk breast cancer: a prospective, randomized, international multicentre Phase III trial—NeoRad
Clinical Cancer Research·2025
Abstract GS2-07: No axillary surgery versus axillary sentinel lymph node biopsy in patients with early invasive breast cancer and breast-conserving surgery: Final primary results of the Intergroup-Sentinel-Mamma (INSEMA) trial
Clinical Cancer Research·2025
ASO Visual Abstract: Multi-Institutional Study to Evaluate the Effectiveness and Safety of a Supine MRI-Based Guidance System, the Breast Cancer LocatorTM, for Breast-Conserving Surgery in Patients with Nonpalpable Breast Cancer
Annals of Surgical Oncology·2025· 1 cited
A Multi-institutional Study to Evaluate the Effectiveness and Safety of a Supine MRI-Based Guidance System, the Breast Cancer LocatorTM, for Breast Conserving Surgery in Patients with Nonpalpable Breast Cancer
Annals of Surgical Oncology·2025
A Deep Learning Model for Breast Shear Wave Elastography to Improve Breast Cancer Diagnosis (INSPiRED 006): An International, Multicenter Analysis
Senologie - Zeitschrift für Mammadiagnostik und -therapie·2025
Can the organic coating of breast tissue markers be sampled by an ultrasound-guided core cut biopsy without removing the clip marker? A proof-of-concept evaluation of two coated clip marker types in turkey breast tissue to improve representativity of ultrasound-guided core cut biopsies
Ultrasound·2025Open Access
267MO A deep learning model for breast shear wave elastography to improve breast cancer diagnosis (INSPiRED 006): An international, multicenter analysis
ESMO Open·2025Open Access
Correction: Best Practice Guidelines – DEGUM Recommendations on Breast Ultrasound
Ultraschall in der Medizin - European Journal of Ultrasound·2025Open Access
Correction: Best Practice Guidelines – DEGUM Recommendations on Breast Ultrasound
Ultraschall in der Medizin - European Journal of Ultrasound·2025Open Access
The impact of updated imaging software on the performance of machine learning models for breast cancer diagnosis: a multi-center, retrospective study
Archives of Gynecology and Obstetrics·2025Open Access
Best Practice Guidelines – DEGUM Recommendations on Breast Ultrasound
Ultraschall in der Medizin - European Journal of Ultrasound·2025· 4 citedOpen Access
Best Practice Guidelines – DEGUM Recommendations on Breast Ultrasound
Ultraschall in der Medizin - European Journal of Ultrasound·2025Open Access
Evaluation of Ex Vivo Shear Wave Elastography of Axillary Sentinel Lymph Nodes in Patients with Early Breast Cancer
Cancers·2024· 2 citedOpen Access
Axillary Surgery in Breast Cancer — Primary Results of the INSEMA Trial
New England Journal of Medicine·2024· 171 cited
Machine Learning to Predict the Individual Risk of Treatment-Relevant Toxicity for Patients With Breast Cancer Undergoing Neoadjuvant Systemic Treatment
JCO Clinical Cancer Informatics·2024· 3 citedOpen Access
Postoperative Bildgebung – was ist erforderlich, was überflüssig?
Senologie - Zeitschrift für Mammadiagnostik und -therapie·2024
Shear-wave elastography as a supplementary tool for axillary staging in patients undergoing breast cancer diagnosis
Insights into Imaging·2024· 7 citedOpen Access
Systematische Evaluation von ex vivo Shear-Wave-Elastographie in axillären Lymphknoten im Rahmen von axillären Operationen bei Patientinnen mit Brustkrebs
Senologie - Zeitschrift für Mammadiagnostik und -therapie·2024
Machine learning to predict the individual Risk of Treatment-relevant Toxicity for Breast Cancer Patients undergoing Neoadjuvant Systemic Treatment
Senologie - Zeitschrift für Mammadiagnostik und -therapie·2024
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Frequent Co-authors

Christof Sohn(33), Joerg Heil(31), Geraldine Rauch(25), André Hennigs(16), Andreas Schneeweiß(16), Sarah Schott(15), Hans‐Peter Sinn(14), Jörg Heil(13), Christoph Domschke(13), Florian Schuetz(11), Joachim Rom(11), Fabian Riedel(10), Aba Harcos(9), André Pfob(8), Frederik Marmé(8), H. Junkermann(8), Markus Wallwiener(8), Anne Stieber(7), Florian Schütz(7), Benedikt Schaefgen(7)