One key advantage of MRI is to better discriminate soft tissues, added to its ability to capture tumor changes and heterogene-ity . The Cancer Imaging Archive 1 (TCIA)* database, publically available, is retrospectively retrieved. The ap- proved dataset is composed of MR/PET/CT scans of 51 patients with histologically proven STS of the extremities [1]. A dataset 2 of 21 patients is analyzed, 11 with pathologically confirmed Liposarcomas (LPS) which arise in deep soft tissue fat cells (age 29–82 years) and 10 with Leiomyosarcomas (LMS) affecting muscle cells (age 24–83 years). The cohort includes 12 males and 9 females scanned during a median follow-up period of 31 months. For each patient, the ground truth differentiating histopathological subtypes is confirmed by surgery. Tumors were localized in primary sites comprehending the thigh, the biceps and the pelvis. Three types of MRI sequences are selected for research purpose , namely T1-weighted (T1), T2-weighted fat- saturated (T2FS) and short tau inversion recovery (STIR). For all T1 sequences, the acquisition was performed in the axial plane, while T2FS and STIR sequences were acquired in different orien- tations (axial, sagittal and coronal). Additionally, we note that MR scans slice thickness was 5.5 mm for T1 and 5 mm for both T2- weighted fat-saturated and STIR. The in-plane resolution was 0.63 mm 2 , 0.74 mm 2 and 0.86 mm 2 for T2FS, T1 and STIR scans, respectively [1].
[1] Vallières, M., Freeman, C. R., Skamene, S. R., & El Naqa, I. (2015). A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metas- tases in soft-tissue sarcomas of the extremities. Physics in Medicine and Biology, 60 (14), 5471–5496.
- https://wiki.cancerimagingarchive.net/display/Public/Soft- tissue- Sarcoma
The dataset contains:
T1 | T2FS | STIR | Total
LPS 676 | 457 | 219 | 1352
LMS 472 | 409 | 63 | 944
Download link: https://drive.google.com/open?id=120dgtM6L3e9Ttlhcr5_LLQrlOiuHw-gt
Cite as: Hermessi,H., Mourali, O., Zagrouba, E. (2019), Deep feature learning for soft tissue sarcoma classification in MR images via transfer learning. Expert Systems With Applications, 120, 116–127.
https://doi.org/10.1016/j.eswa.2018.11.025