PT  - JOURNAL ARTICLE
AU  - Janna Morawitz
AU  - Benjamin Sigl
AU  - Christian Rubbert
AU  - Nils-Martin Bruckmann
AU  - Frederic Dietzel
AU  - Lena J. Häberle
AU  - Saskia Ting
AU  - Svjetlana Mohrmann
AU  - Eugen Ruckhäberle
AU  - Ann-Kathrin Bittner
AU  - Oliver Hoffmann
AU  - Pascal Baltzer
AU  - Panagiotis Kapetas
AU  - Thomas Helbich
AU  - Paola Clauser
AU  - Wolfgang Peter Fendler
AU  - Christoph Rischpler
AU  - Ken Herrmann
AU  - Benedikt M. Schaarschmidt
AU  - Andreas Stang
AU  - Lale Umutlu
AU  - Gerald Antoch
AU  - Julian Caspers
AU  - Julian Kirchner
TI  - Clinical decision support for axillary lymph node staging in newly diagnosed breast cancer patients based on <sup>18</sup>F-FDG PET/MRI and machine-learning
AID  - 10.2967/jnumed.122.264138
DP  - 2022 Sep 01
TA  - Journal of Nuclear Medicine
PG  - jnumed.122.264138
4099  - http://jnm.snmjournals.org/content/early/2022/09/22/jnumed.122.264138.short
4100  - http://jnm.snmjournals.org/content/early/2022/09/22/jnumed.122.264138.full
AB  - Background: In addition to its high prognostic value, the involvement of axillary lymph nodes in breast cancer patients also plays an important role in therapy planning. Therefore, an imaging modality that can determine nodal status with high accuracy in primary breast cancer patients is desirable. Purpose: To investigate if machine-learning prediction models based on simple assessable imaging features in MRI (magnetic resonance imaging) or PET (positron emission tomography)/MRI are able to determine nodal status in newly diagnosed breast cancer patients with comparable performance as experienced radiologists, if such models can be adjusted to achieve low rates of false negatives such that invasive procedures could potentially be omitted, and if a clinical framework for decision-support based on simple imaging features can be derived from these models. Methods: 303 participants from three centres prospectively underwent dedicated whole-body 18F-FDG (18F-fluorodeoxyglucose) PET/MRI between August 2017 and September 2020. Imaging datasets were evaluated regarding axillary lymph node metastases based on morphologic and metabolic features. Predictive models were developed for MRI and PET/MRI separately using random forest classifiers on data of two centers and were tested on data of the third center. Results: The diagnostic accuracy for MRI features was 87.5% both for radiologists and for machine learning algorithm. For PET/MRI the diagnostic accuracy was 89.3% for the radiologists and 91.2% for the machine learning algorithm with no significant differences in diagnostic performance of radiologists and the machine learning algorithm in MRI (P = 0.671) and PET/MRI (P = 0.683). Most important lymph node feature was tracer uptake, followed by lymph node size. With an adjusted threshold, a sensitivity of 96.2% was achieved by the random forest classifier, whereas specificity, positive predictive value, negative predictive value and accuracy were 68.2%, 78.1%, 93.8% and 83.3%. A decision tree based on three simple imaging features could be established for MRI and PET/MRI. Conclusion: Applying a high sensitivity threshold to the random forest results could potentially avoid invasive procedures such as sentinel lymph node biopsy in 68.2% of the patients.