Advanced Imaging in Musculoskeletal Oncology: Moving Away From RECIST and Embracing Advanced Bone and Soft Tissue Tumor Imaging (ABASTI) - Part I - Tumor Response Criteria and Established Functional Imaging Techniques

Novel Use and Value of Contrast-Enhanced Susceptibility-Weighted Imaging Morphologic and Radiomic Features in Predicting Extremity Soft Tissue Undifferentiated Pleomorphic Sarcoma Treatment Response

PURPOSE

Undifferentiated pleomorphic sarcomas (UPSs) demonstrate therapy-induced hemosiderin deposition, granulation tissue formation, fibrosis, and calcification. We aimed to determine the treatment-assessment value of morphologic tumoral hemorrhage patterns and first- and high-order radiomic features extracted from contrast-enhanced susceptibility-weighted imaging (CE-SWI).

MATERIALS AND METHODS

This retrospective institutional review board-authorized study included 33 patients with extremity UPS with magnetic resonance imaging and resection performed from February 2021 to May 2023. Volumetric tumor segmentation was obtained at baseline, postsystemic chemotherapy (PC), and postradiation therapy (PRT). The pathology-assessed treatment effect (PATE) in surgical specimens separated patients into responders (R; ≥90%, n = 16), partial responders (PR; 89%-31%, n = 10), and nonresponders (NR; ≤30%, n = 7). RECIST, WHO, and volume were assessed for all time points. CE-SWI T2* morphologic patterns and 107 radiomic features were analyzed.

RESULTS

A Complete-Ring (CR) pattern was observed in PRT in 71.4% of R (P = 7.71 × 10-6), an Incomplete-Ring pattern in 33.3% of PR (P = .2751), and a Globular pattern in 50% of NR (P = .1562). The first-order radiomic analysis from the CE-SWI intensity histogram outlined the values of the 10th and 90th percentiles and their skewness. R showed a 280% increase in 10th percentile voxels (P = .061) and a 241% increase in skewness (P = .0449) at PC. PR/NR showed a 690% increase in the 90th percentile voxels (P = .03) at PC. Multiple high-order radiomic texture features observed at PRT discriminated better R versus PR/NR than the first-order features.

CONCLUSION

CE-SWI morphologic patterns strongly correlate with PATE. The CR morphology pattern was the most frequent in R and had the highest statistical association predicting response at PRT, easily recognized by a radiologist not requiring postprocessing software. It can potentially outperform size-based metrics, such as RECIST. The first- and high-order radiomic analysis found several features separating R versus PR/NR.

The Role of CT and MR Imaging in Stereotactic Body Radiotherapy of the Spine: From Patient Selection and Treatment Planning to Post-Treatment Monitoring

Spine metastases (SMs) are common, arising in 70% of the cases of the most prevalent malignancies in males (prostate cancer) and females (breast cancer). Stereotactic body radiotherapy, or SBRT, has been incorporated into clinical treatment algorithms over the past decade. SBRT has shown promising rates of local control for oligometastatic spinal lesions with low radiation dose to adjacent critical tissues, particularly the spinal cord. Imaging is critically important in SBRT planning, guidance, and response monitoring. This paper reviews the roles of imaging in spine SBRT, including conventional and advanced imaging approaches for SM detection, treatment planning, and post-SBRT follow-up.

Perfusion-weighted imaging with dynamic contrast enhancement (PWI/DCE) morphologic, qualitative, semiquantitative, and radiomics features predicting undifferentiated pleomorphic sarcoma (UPS) treatment response

Undifferentiated pleomorphic sarcoma (UPS) is the largest subgroup of soft tissue sarcomas. This study determined the value of perfusion-weighted imaging with dynamic-contrast-enhancement (PWI/DCE) morphologic, qualitative, and semiquantitative features for predicting UPS pathology-assessed treatment effect (PATE). This retrospective study included 33 surgically excised extremity UPS patients with pre-surgical MRI. Volumetric tumor segmentation from PWI/DCE was obtained at Baseline (BL), Post-Chemotherapy (PC), and Post-Radiation Therapy (PRT). The surgical specimens' PATE separated cases into Responders (R) (≥ 90%, 16 patients), Partial-Responders (PR) (89 - 31%, 10 patients), and Non-Responders (NR) (≤ 30%, seven patients). Seven semiquantitative kinetic parameters and maps were extracted from time-intensity curves (TICs), and 107 radiomic features were derived. Statistical analyses compared R vs. PR/NR. At PRT, 79% of R displayed a "Capsular" morphology (P = 1.49 × 10-7), and 100% demonstrated a TIC-type II (P = 8.32 × 10-7). 80% of PR showed "Unipolar" morphology (P = 1.03 × 10-5), and 60% expressed a TIC-type V (P = 0.06). Semiquantitative wash-in rate (WiR) was able to separate R vs. PR/NR (P = 0.0078). The WiR radiomics displayed significant differences in the first_order_10 percentile (P = 0.0178) comparing R vs. PR/NR at PRT. The PWI/DCE TIC-type II curve, low WiR, and "Capsular" enhancement represent PRT patterns typically observed in successfully treated UPS and demonstrate potential for UPS treatment response assessment.

Cancer Radiomic and Perfusion Imaging Automated Framework: Validation on Musculoskeletal Tumors

PURPOSE

Limitations from commercial software applications prevent the implementation of a robust and cost-efficient high-throughput cancer imaging radiomic feature extraction and perfusion analysis workflow. This study aimed to develop and validate a cancer research computational solution using open-source software for vendor- and sequence-neutral high-throughput image processing and feature extraction.

METHODS

The Cancer Radiomic and Perfusion Imaging (CARPI) automated framework is a Python-based software application that is vendor- and sequence-neutral. CARPI uses contour files generated using an application of the user's choice and performs automated radiomic feature extraction and perfusion analysis. This workflow solution was validated using two clinical data sets, one consisted of 40 pelvic chondrosarcomas and 42 sacral chordomas with a total of 82 patients, and a second data set consisted of 26 patients with undifferentiated pleomorphic sarcoma (UPS) imaged at multiple points during presurgical treatment.

RESULTS

Three hundred sixteen volumetric contour files were processed using CARPI. The application automatically extracted 107 radiomic features from multiple magnetic resonance imaging sequences and seven semiquantitative perfusion parameters from time-intensity curves. Statistically significant differences (P < .00047) were found in 18 of 107 radiomic features in chordoma versus chondrosarcoma, including six first-order and 12 high-order features. In UPS postradiation, the apparent diffusion coefficient mean increased 41% in good responders (P = .0017), while firstorder_10Percentile (P = .0312) was statistically significant between good and partial/nonresponders.

CONCLUSION

The CARPI processing of two clinical validation data sets confirmed the software application's ability to differentiate between different types of tumors and help predict patient response to treatment on the basis of radiomic features. Benchmark comparison with five similar open-source solutions demonstrated the advantages of CARPI in the automated perfusion feature extraction, relational database generation, and graphic report export features, although lacking a user-friendly graphical user interface and predictive model building.

Advancements of molecular imaging and radiomics in pancreatic carcinoma

Despite the recent progress of medical technology in the diagnosis and treatment of tumors, pancreatic carcinoma remains one of the most malignant tumors, with extremely poor prognosis partly due to the difficulty in early and accurate imaging evaluation. This paper focuses on the research progress of magnetic resonance imaging, nuclear medicine molecular imaging and radiomics in the diagnosis of pancreatic carcinoma. We also briefly described the achievements of our team in this field, to facilitate future research and explore new technologies to optimize diagnosis of pancreatic carcinoma.