The value of dynamic subtraction MRI technique in the assessment of treatment response of hepatocellular carcinoma to transcatheter arterial chemoembolization

DECIDE: A decoupled semantic and boundary learning network for precise osteosarcoma segmentation by integrating multi-modality MRI

Automated Osteosarcoma Segmentation in Multi-modality MRI (AOSMM) holds clinical significance for effective tumor evaluation and treatment planning. However, the precision of AOSMM is challenged by the diverse characteristics of multi-modality MRI and the inherent heterogeneity and boundary ambiguity of osteosarcoma. While numerous methods have made significant strides in automated osteosarcoma segmentation, they primarily focused on the use of a single MRI modality and overlooked the potential benefits of integrating complementary information from other MRI modalities. Furthermore, they did not adequately model the long-range dependencies of complex tumor features, which may lead to insufficiently discriminative feature representations. To this end, we propose a decoupled semantic and boundary learning network (DECIDE) to achieve precise AOSMM with three functional modules. The Multi-modality Feature Fusion and Recalibration (MFR) module adaptively fuses and recalibrates multi-modality features by exploiting their channel-wise dependencies to compute low-rank attention weights for effectively aggregating useful information from different MRI modalities, which promotes complementary learning between multi-modality MRI and enables a more comprehensive tumor characterization. The Lesion Attention Enhancement (LAE) module employs spatial and channel attention mechanisms to capture global contextual dependencies over local features, significantly enhancing the discriminability and representational capacity of intricate tumor features. The Boundary Context Aggregation (BCA) module further enhances semantic representations by utilizing boundary information for effective context aggregation while also ensuring intra-class consistency in cases of boundary ambiguity. Substantial experiments demonstrate that DECIDE achieves exceptional performance in osteosarcoma segmentation, surpassing state-of-the-art methods in terms of accuracy and stability.

Role of multislice computed tomography 3D volumetric analysis in the assessment of the therapeutic response of hepatocellular carcinoma after transarterial chemoembolization

Background

Volumetric analysis is a novel radiological technique used in the measurement of target lesions in three dimensions in order to estimate the lesion’s volume. Recently, it has been used for evaluation of the remaining liver volume after partial hepatectomy and also for evaluation of the response of tumours to treatment. It has been proven to be more accurate than the standard one or two-dimensional measurements, and it is especially useful for the evaluation of complex tumours after radiological interventional methods when the use of standard methods is limited. In the current study, our aim was to evaluate the value of the three-dimensional (3D) volumetric method “Response Evaluation Criteria in Solid Tumours (vRECIST)” and to compare it with the non–three-dimensional methods (RECIST) and modified RECIST (mRECIST) in the assessment of the therapeutic response of hepatocellular carcinoma (HCC) after transarterial chemoembolization (TACE).

Results

A retrospective study was conducted on 50 patients with confirmed radiological or pathological diagnosis of hepatocellular carcinoma (HCC) who underwent TACE as the only interventional procedure and follwed up by triphasic CT 1 and 4 months after treatment. The study revealed a significant difference between mRECIST and vRECIST in the assessment of the therapeutic response of HCC after TACE, a weak agreement was found between both methods in the detection of complete response (CR), partial response (PR), stable disease (SD) or progressive disease (PD). Also, there was no significant agreement between mRECIST and vRECIST regarding the assessment by classifying the patients into responders or nonresponders.

Conclusion

Volumetric analysis is an effective method for measuring the HCC lesions and evaluating its response to locoregional treatment with a significant difference between vRECIST and mRECIST in the assessment of therapeutic response, which in turn help the interventional radiologist to decide the future treatments and change the therapeutic plans. Based on these results, we recommend vRECIST to be an essential part of the assessment of therapeutic response after locoregional therapy.