Tumor microenvironment(TME) and single-source dual-energy CT(ssDECT) on assessment of inconformity between RECIST1.1 and pathological remission in neoadjuvant immunotherapy of NSCLC.

BACKGROUND: The inconformity (IC) between pathological and imaging remissions after neoadjuvant immunotherapy in patients with NSCLC can affect the evaluation of curative effect of neoadjuvant therapy and the decision regarding the chance of surgery. MATERIALS AND METHODS: Patients who achieved disease control(CR/PR/SD) after neoadjuvant chemoimmunotherapy from a clinical trial (NCT04326153) and after neoadjuvant chemotherapy during the same period were enrolled in this study. All patients underwent radical resection and systematic mediastinal lymphadenectomy after neoadjuvant treatments. The pathological remission, immunohistochemistry (CD4, CD8, CD20, CD56, FoxP3, CD68, CD163, CD11b tumor-infiltrating lymphocytes, or macrophages), and single-source dual-energy computed tomography (ssDECT) scans were assessed. The IC between imaging remission by CT and pathological remission was investigated. The underlying cause of IC, the correlation between IC and DFS, and prognostic biomarkers were explored. RESULTS: After neoadjuvant immunotherapy, enhanced immune killing and reduced immunosuppressive performance were observed. 70 % of neoadjuvant chemoimmunotherapy patients were in high/medium IC level. Massive necrosis and repair around and inside the cancer nest were the main pathological changes observed 30-45 days post-treatment with PD1/PD-L1 antibody and were the main causes of IC between the pathology and imaging responses after neoadjuvant immunotherapy. High IC and preoperative CD8 expression (H score ≥ 3) indicate a high pathological response rate and prolonged DFS. Iodine material density ssDECT images showed that the iodine content in the lesion causes hyperattenuation in post-neoadjuvant lesion in PCR patient. CONCLUSIONS: Compared to chemotherapy and targeted therapy, the efficacy of neoadjuvant immunotherapy was underestimated based on the RECIST criteria due to the unique antitumor therapeutic mechanism. Preoperative CD8+ expression and ssDECT predict this IC and evaluate the residual tumor cells. This is of great significance for screening immune beneficiaries and making more accurate judgments about the timing of surgery.

Visualizing the Habenula Using 3T High-Resolution MP2RAGE and QSM: A Preliminary Study.

BACKGROUND AND PURPOSE: The habenula is a key node in the regulation of emotion-related behavior. Accurate visualization of the habenula and its reliable quantitative analysis is vital for the assessment of psychiatric disorders. To obtain high-contrast habenula images and allow them to be compatible with clinical applications, this preliminary study compared 3T MP2RAGE and quantitative susceptibility mapping with MPRAGE by evaluating the habenula segmentation performance. MATERIALS AND METHODS: Ten healthy volunteers were scanned twice with 3T MPRAGE and MP2RAGE and once with quantitative susceptibility mapping. Image quality and visibility of habenula anatomic features were analyzed by 3 radiologists using a 5-point scale. Contrast assessments of the habenula and thalamus were also performed. The reproducibility of the habenula volume from MPRAGE and MP2RAGE was evaluated by manual segmentation and the Multiple Automatically Generated Template brain segmentation algorithm (MAGeTbrain). T1 values and susceptibility were measured in the whole habenula and habenula geometric subregion using MP2RAGE T1-mapping and quantitative susceptibility mapping. RESULTS: The 3T MP2RAGE and quantitative susceptibility mapping demonstrated clear boundaries and anatomic features of the habenula compared with MPRAGE, with a higher SNR and contrast-to-noise ratio (all P < .05). Additionally, 3T MP2RAGE provided reliable habenula manual and MAGeTbrain segmentation volume estimates with greater reproducibility. T1-mapping derived from MP2RAGE was highly reliable, and susceptibility contrast was highly nonuniform within the habenula. CONCLUSIONS: We identified an optimized sequence combination (3T MP2RAGE combined with quantitative susceptibility mapping) that may be useful for enhancing habenula visualization and yielding more reliable quantitative data.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.

Objective.In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of 'human-signal-image' using the workflow 'CT-simulated data- reconstructed CT,' and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data.Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866).Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of 'signal-to-image' can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of 'signal-image-knowledge', opening up new avenues for more accurate medical diagnostics.