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Tumor habitat characteristics derived from intravoxel incoherent motion for early response assessment in soft tissue
Xin Wen1, Jiuming Jiang1, Lei Miao2
1Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background:
Soft tissue sarcomas (STSs) are heterogeneous malignancies with limited reliable criteria for early response assessment. Commonly used criteria, such as Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, often underperform because treatment-related changes may not translate into tumor shrinkage. This study aimed to assess the potential of intravoxel incoherent motion (IVIM)-derived tumor habitat characteristics as biomarkers for assessing the treatment responses of STS patients who received neoadjuvant radiotherapy (neo-RT) along with targeted therapy.
Methods:
Patients with STS of the limbs or trunk from a prospective phase II trial were selected. All patients received magnetic resonance imaging (MRI) examinations pre- and post-neo-RT and targeted therapy. K-means clustering was employed to classify lesions into subregional habitats based on IVIM parameters. Postoperative pathology classified patients into good- and poor-response groups, defined as <50% and ≥50% residual tumor cells, respectively. IVIM parameters, including mean true-diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) value in the whole tumor and subregions, were extracted and compared between groups. Individual parameter models and combined model were constructed, and receiver operating characteristic (ROC) analysis was utilized to evaluate the diagnostic efficacy.
Results:
Twenty-one participants (11 males, 10 females; mean age 52.1±17.4 years) were enrolled, including 13 individuals in the good-response group and 8 in the poor-response group. K-means clustering identified three clusters based on pre- and post-treatment IVIM features. Five features that significantly differed between the good-response and poor-response groups were incorporated into individual parameter models and combined model. The area under the ROC curve of the combined model was 0.904, greatly outperforming that of RECIST 1.1 [area under curve (AUC) 0.567] and individual parameter models.
Conclusions:
Tumor subregional IVIM parameters can well assess the pathological response degree in STS patients undergoing neo-RT and targeted therapy.
Insights
Intravoxel incoherent motion (IVIM) parameters show promise in assessing soft tissue sarcoma (STS) treatment response. These advanced MRI biomarkers outperform traditional methods for predicting pathological outcomes after neoadjuvant radiotherapy and targeted therapy.
Area of Science:
- Oncology
- Radiology
- Medical Imaging
Background:
- Soft tissue sarcomas (STSs) present challenges in early response assessment due to limitations of current criteria like RECIST 1.1.
- Treatment-induced changes in STSs may not always correlate with tumor shrinkage, complicating response evaluation.
- This study investigates intravoxel incoherent motion (IVIM)-derived tumor habitat characteristics as potential biomarkers for STS treatment response.
Purpose of the Study:
- To evaluate the efficacy of IVIM parameters in assessing treatment response in STS patients undergoing neoadjuvant radiotherapy (neo-RT) and targeted therapy.
- To compare the diagnostic performance of IVIM-derived tumor habitat characteristics against established criteria (RECIST 1.1).
- To identify specific IVIM parameters and their subregional distributions that correlate with pathological response.
Main Methods:
- Prospective phase II trial data from STS patients receiving neo-RT and targeted therapy were analyzed.
- Magnetic resonance imaging (MRI) was performed pre- and post-treatment.
- K-means clustering was used to classify tumor subregions based on IVIM parameters (D, D*, f); pathological response was determined post-surgery.
Main Results:
- Twenty-one STS patients were analyzed, categorized into good- (13) and poor-response (8) groups based on pathology.
- K-means clustering identified distinct tumor habitats using pre- and post-treatment IVIM features.
- A combined IVIM model demonstrated high diagnostic efficacy (AUC 0.904), significantly outperforming RECIST 1.1 (AUC 0.567).
Conclusions:
- Subregional IVIM parameters effectively assess pathological response in STS patients receiving neo-RT and targeted therapy.
- IVIM analysis offers a superior method for early treatment response assessment in STSs compared to RECIST 1.1.
- This approach holds potential for optimizing treatment strategies in soft tissue sarcoma management.
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