Developing a Machine Learning-Based Clinical Decision Support Tool for
  Uterine Tumor Imaging
Darryl E. Wright Adriana V. Gregory Deema Anaam Sepideh Yadollahi Sumana Ramanathan Kafayat A. Oyemade Reem Alsibai Heather Holmes Harrison Gottlich Cherie-Akilah G. Browne Sarah L. Cohen Rassier Isabel Green Elizabeth A. Stewart Hiroaki Takahashi Bohyun Kim Shannon Laughlin-Tommaso Timothy L. Kline
Uterine leiomyosarcoma (LMS) is a rare but aggressive malignancy. On imaging,
it is difficult to differentiate LMS from, for example, degenerated leiomyoma
(LM), a prevalent but benign condition. We curated a data set of 115 axial
T2-weighted MRI images from 110 patients (mean [range] age=45 [17-81] years)
with UTs that included five different tumor types. These data were randomly
split stratifying on tumor volume into training (n=85) and test sets (n=30). An
independent second reader (reader 2) provided manual segmentations for all test
set images. To automate segmentation, we applied nnU-Net and explored the
effect of training set size on performance by randomly generating subsets with
25, 45, 65 and 85 training set images. We evaluated the ability of radiomic
features to distinguish between types of UT individually and when combined
through feature selection and machine learning. Using the entire training set
the mean [95% CI] fibroid DSC was measured as 0.87 [0.59-1.00] and the
agreement between the two readers was 0.89 [0.77-1.0] on the test set. When
classifying degenerated LM from LMS we achieve a test set F1-score of 0.80.
Classifying UTs based on radiomic features we identify classifiers achieving
F1-scores of 0.53 [0.45, 0.61] and 0.80 [0.80, 0.80] on the test set for the
benign versus malignant, and degenerated LM versus LMS tasks. We show that it
is possible to develop an automated method for 3D segmentation of the uterus
and UT that is close to human-level performance with fewer than 150 annotated
images. For distinguishing UT types, while we train models that merit further
investigation with additional data, reliable automatic differentiation of UTs
remains a challenge.