MRI Tumor Research Articles

Total bile acid as a preoperative risk factor for post-hepatectomy liver failure in patients with hepatocellular carcinoma and normal bilirubin

Background and aimsTotal bile acid (TBA) is associated with portal hypertension, a risk factor for post-hepatectomy liver failure (PHLF). We conducted this study to clarify whether TBA is also associated with PHLF in patients with hepatocellular carcinoma (HCC). MethodsWe recruited patients with HCC and Child-Pugh class A, who underwent liver resection, and applied multivariate analyses to identify risk factors for PHLF. ResultsWe analyzed data from 154 patients. The prevalence of PHLF was 14.3%. The median maximum tumor diameter was 5.1 cm (2.9–6.9 cm). The proportions of patients with elevated TBA levels (P = 0.001), severe albumin-bilirubin (AIBL) grades (P = 0.033), and low platelet counts (P = 0.031) were significantly higher within the subgroup of patients with PHLF than in the subgroup without PHLF. The multivariate analysis results suggest that TBA level (OR, 1.08; 951.03–1.14; P = 0.003) and MRI tumor diameter (OR, 1.17; 95% CI, 1.01–1.35; P = 0.038) are independent preoperative risk factors for PHLF. The TBA levels correlated with the indocyanine green retention rate at 15 minutes (P = 0.001) and the effective hepatic blood flow (P < 0.001), two markers of portal hypertension. However, TBA levels did not correlate with tumor diameter (P = 0.536). ConclusionsCompared to ICG R15 and AIBL score, preoperative TBA was risk factor for PHLF in Chinese patients with HCC, and it may impact PHLF through its potential role as a marker of portal hypertension.

Gallium-67 SPECT/CT and diffusion-weighted MRI in the palatal tumor: report of a rare case with adenocarcinoma not otherwise specified

BackgroundAdenocarcinoma not otherwise specified (NOS) is an uncommon malignant minor salivary gland tumor. We report gallium-67 SPECT/CT and diffusion-weighted MRI (DWI) in the palatal tumor, especially a rare case with adenocarcinoma NOS.Case presentationAn 85-year-old female patient presented with swelling on the palate within 3 months. DWI and apparent diffusion coefficient (ADC) maps indicated high and low signal intensities on the lesion, respectively. ADC value of the lesion was 0.58 × 10–3 mm2s−1. Gallium-67 SPECT/CT showed increased uptake on the lesion and no metastasis. The maximum SUV for the palatal lesion was 7.36. Histopathological diagnosis by a biopsy was adenocarcinoma NOS.ConclusionThe gallium-67 SPECT/CT and DWI should be effective for assessment of the palatal tumor.

Enhancing brain tumor MRI classification with an ensemble of deep learning models and transformer integration.

Brain tumors are widely recognized as the primary cause of cancer-related mortality globally, necessitating precise detection to enhance patient survival rates. The early identification of brain tumor is presented with significant challenges in the healthcare domain, necessitating the implementation of precise and efficient diagnostic methodologies. The manual identification and analysis of extensive MRI data are presented as a challenging and laborious task, compounded by the importance of early tumor detection in reducing mortality rates. Prompt initiation of treatment hinges upon identifying the specific tumor type in patients, emphasizing the urgency for a dependable deep learning methodology for precise diagnosis. In this research, a hybrid model is presented which integrates the strengths of both transfer learning and the transformer encoder mechanism. After the performance evaluation of the efficacy of six pre-existing deep learning model, both individually and in combination, it was determined that an ensemble of three pretrained models achieved the highest accuracy. This ensemble, comprising DenseNet201, GoogleNet (InceptionV3), and InceptionResNetV2, is selected as the feature extraction framework for the transformer encoder network. The transformer encoder module integrates a Shifted Window-based Self-Attention mechanism, sequential Self-Attention, with a multilayer perceptron layer (MLP). These experiments were conducted on three publicly available research datasets for evaluation purposes. The Cheng dataset, BT-large-2c, and BT-large-4c dataset, each designed for various classification tasks with differences in sample number, planes, and contrast. The model gives consistent results on all three datasets and reaches an accuracy of 99.34%, 99.16%, and 98.62%, respectively, which are improved compared to other techniques.

Explainable ensemble deep learning-based model for brain tumor detection and classification

AbstractBrain tumors are very dangerous as they cause death. A lot of people die every year because of brain tumors. Therefore, accurate classification and detection in the early stages can help in recovery. Various deep learning techniques have achieved good results in brain tumor classification. The traditional deep learning methods and training the neural network from scratch are time-consuming and can last for weeks of training. Therefore, in this work, we proposed an ensemble approach depending on transfer learning that utilizes pre-trained models of DenseNet121 and InceptionV3 to detect three forms of brain tumors: meningioma, glioma, and pituitary. While developing the ensemble model, some changes were made to the architecture of pre-trained models by replacing their classifiers (fully connected and SoftMax layers) with a new classifier to adopt the recent task. In addition, gradient-weighted class activation maps (Grad-CAM) are an explainable model to verify results and achieve high confidence. The suggested model was validated using a publicly available dataset and achieved 99.02% accuracy, 98.75% precision, 98.98% recall, and a 98.86% F1 score. The suggested approach outperformed others in detecting and classifying brain tumor MRI data, and verifying results using the explainable model achieved a high degree of trust.

Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.

To develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patients treated at a high-volume brain tumor center. We retrospectively analyzed data from 634 patients treated for glioblastoma at a single brain tumor center over a 5-year period (2017-2021). The mean age was 56 +/-13 years. 372/634 (59%) patients were male, and 262/634 (41%) patients were female. Study data consisted of 3,403 brain MRI exams and corresponding standardized, radiologist-based brain tumor response assessments (BT-RADS). An artificial intelligence (AI)-based brain tumor response assessment algorithm was developed using automated, volumetric tumor segmentation. AI-based response assessments were evaluated for agreement with radiologist-based response assessments and ability to stratify patients by overall survival. Metrics were computed to assess the agreement using BTRADS as the ground-truth, fixed-time point survival analysis was conducted to evaluate the survival stratification, and associated P-values were calculated. For all BT-RADS categories, AI-based response assessments showed moderate agreement with radiologists' response assessments (F1 = 0.587-0.755). Kaplan-Meier survival analysis revealed statistically worse overall fixed time point survival for patients assessed as image worsening equivalent to RANO progression by human alone compared to by AI alone (log-rank P=0.007). Cox proportional hazard model analysis showed a disadvantage to AI-based assessments for overall survival prediction (P=0.012). AI-based volumetric glioblastoma MRI response assessment following BT-RADS criteria yielded moderate agreement for replicating human response assessments and slightly worse stratification by overall survival. GBM＝ Glioblastoma; RANO＝ Response Assessment in Neuro-Oncology; BTRADS＝ Brain Tumor Reporting and Data System; NLP = Natural Language Processing.

NIMG-43. INFARCTION RADIOLOGICALLY MIMICKING INTRA-AXIAL BRAIN TUMORS

Abstract There are several reports of brain lesions that radiologically mimicking the brain tumors. The abscess is representative lesion of resembling the intra-axial tumors in brain MRI. However, cerebral infarction is not a common lesion that resembles a brain tumor and can be easily distinguished from a brain tumor in MRI. Here, we represented the two cases of pathologically diagnosed infarction that radiologically suspected gliomas. A 67-year-old man presented with left side weakness and memory impairment. Brain MRI revealed the rim enhancing lesion radiologically suspected glioma. Navigation guided biopsy was performed and final pathological diagnosis was subacute infarction. A-41-year-old man represented the right side weakness. MRI showed the enhancing nodular lesion with perilesional edema. Surgical excision was performed and pathological diagnosis was infarction. Although advancement of brain imaging techniques, it is difficult to accurately discriminate the intracerebral lesions utilizing the MRI. Further comprehensive study should be executed for more precise differentiation of infarction and brain tumors.

BIOM-18. MAPPING TUMOR HABITATS IN IDH-WILD TYPE GLIOBLASTOMA: INTEGRATING MR IMAGING, PATHOLOGIC, AND RNA DATA FROM IVY GLIOBLASTOMA ATLAS PROJECT

Abstract BACKGROUND To identify biologic correlates and spatially validate intratumoral subregions (tumor habitat) using diffusion-weighted and dynamic susceptibility contrast-imaging on a comprehensive pathology map of the isocitrate dehydrogenase (IDH)-wild type whole glioblastoma sample using the Ivy Glioblastoma Atlas Project (Ivy GAP). METHODS Complete data of 20 patients with 168 slides of IDH-wild type glioblastoma were obtained from the IvyGAP (http://glioblastoma.alleninstitute.org/). On MRI, tumor habitats were defined using voxel-wise clustering of apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps for contrast-enhancing lesion (CEL) and peritumoral non-enhancing lesion (NEL). On pathology slides, normalized areas of leading edge (LE), infiltrating tumor (IT), cellular tumor (CT), hypervascular lesion (CThypervascular), and perinecrotic lesion (CTperinecrotic) were obtained. Gross specimen pictures were co-registered on MRI images for co-localization and correlation between MRI tumor habitats. Pathologic areas were calculated using Pearson’s correlation coefficient. RNA sequencing of 67 RNA-seq samples was assessed using 4 Neftel subtypes and further correlated with pathology. RESULTS Six tumor habitats were correlated: hypervascular, hypovascular cellular, and hypovascular hypocellular habitats for CEL and NEL. CT was strongly correlated with hypovascular cellular habitat in CEL (C2, r = 0.238, p =.005). IT was strongly correlated with hypovascular cellular habitat in NEL (C5, r = 0.294, p =.017). CThypervascular was correlated with hypervascular habitat in NEL (r = 0.195, p =.023). CTperinecrotic was correlated with imaging necrosis (r = 0.199, p =.005). Astrocyte-like subtypes were positively correlated with IT (r = 0.256, p &amp;lt;.001), while mesenchymal-like subtypes were positively correlated with CTperinecrotic area (r = 0.246, p &amp;lt;.001). CONCLUSION Pathologically matched tumor subregions were cellular tumor with hypovascular cellular habitat in CEL and infiltrative tumor with hypovascular cellular habitat in NEL. Identification of the most aggressive tumor portion, and infiltrative tumor portion using non-invasive imaging can be achieved using MRI tumor habitats.

Factors of interobserver variability in prostate tumor MRI delineation: impact of PI-QUAL score.

Prostate cancer ranks as the second most common cancer in men worldwide. Dose escalation to the tumor and/or the prostate improves biochemical recurrence-free survival. However, interobserver variability in lesion contouring poses a significant limitation to such therapeutic approaches. Therefore, a study of factors influencing this variability is necessary. Three independent readers delineated the index prostate lesion (DIL) using T2w and ADC sequences for each patient. Clinical data were retrospectively collected for all participants. Logistic regression analysis was employed to examine the correlation between clinical features and a mean DICE coefficient > 0.7. Features exhibiting a p value < 0.05 on univariate analysis were subjected to multivariate analysis. The study comprised 68 patients, with a median DICE coefficient of 0.69 (95% CI 0.65-0.71), wherein 42.6% (29/68) attained a mean DICE > 0.7. Univariate analysis identified the PI-QUAL score, maximum diameter of DIL, and mean DIL volume as significant (p < 0.05) predictors. In multivariate analysis, only the PI-QUAL score (p = 0.008) remained statistically associated with a DICE coefficient > 0.7. The PI-QUAL score emerges as the primary predictive factor for minimizing inter-reader variability in intraprostatic dominant lesion segmentation. These findings underscore the importance of considering PI-QUAL scores when devising focal treatment plans. Adoption of a multi-reader approach involving diverse medical specialists (radiologists, radiotherapists, urologists) is advocated, particularly for MRIs with low PI-QUAL scores. Radiotherapy is a major treatment for patients with localized prostate cancer. Dose escalation to the tumor leads to improved cancer control. Precise delineation of the dominant intraprostatic lesion (DIL) remains a limitation to focal treatments. Features influencing inter-reader variability were never evaluated. In this study, we identified that the PI-QUAL score was the sole predictor of the inter-reader delineation variability of the DIL.

Efficient brain tumor grade classification using ensemble deep learning models

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural views of the brain, which is vital in identifying any of its abnormalities. The other option of performing an invasive biopsy is very painful and uncomfortable, which is not the case with MRI as it is free from surgically invasive margins and pieces of equipment. This helps patients to feel more at ease and hasten the diagnostic procedure, allowing physicians to formulate and practice action plans quicker. It is very difficult to locate a human brain tumor by manual because MRI scans produce large numbers of three-dimensional images. Complete applicability of pre-written computerized diagnostics, affords high possibilities in providing areas of interest earlier through the application of machine learning techniques and algorithms. The proposed work in the present study was to develop a deep learning model which will classify brain tumor grade images (BTGC), and hence enhance accuracy in diagnosing patients with different grades of brain tumors using MRI. A MobileNetV2 model, was used to extract the features from the images. This model increases the efficiency and generalizability of the model further. In this study, six standard Kaggle brain tumor MRI datasets were used to train and validate the developed and tested model of a brain tumor detection and classification algorithm into several types. This work consists of two key components: (i) brain tumor detection and (ii) classification of the tumor. The tumor classifications are conducted in both three classes (Meningioma, Pituitary, and glioma) and two classes (malignant, benign). The model has been reported to detect brain tumors with 99.85% accuracy, to distinguish benign and malignant tumors with 99.87% accuracy, and to type meningioma, pituitary, and glioma tumors with 99.38% accuracy. The results of this study indicate that the described technique is useful in the detection and classification of brain tumors.

Fractional order calculus model-derived histogram metrics for assessing pathological complete response to neoadjuvant chemotherapy in locally advanced rectal cancer

AimThis study evaluates the value of diffusion fractional order calculus (FROC) model for the assessment of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer (LARC) by using histogram analysis derived from whole-tumor volumes. Materials and methodsNinety-eight patients were prospectively included. Every patient received MRI scans before and after nCRT using a 3.0-Tesla MRI machine. Parameters of the FROC model, including the anomalous diffusion coefficient (D), intravoxel diffusion heterogeneity (β), spatial parameter (μ), and the standard apparent diffusion coefficient (ADC), were calculated. Changes in median values (ΔX-median) and ratio (rΔX-median) were calculated. Receiver operating characteristic (ROC) curves were used for evaluating the diagnostic performance. ResultsPre-treatmentβ-10th percentile values were significantly lower in the pCR group compared to the non-pCR group (p < 0.001). The Δβ-median showed higher diagnostic accuracy (AUC = 0.870) and sensitivity (76.67 %) for predicting tumor response compared to MRI tumor regression grading (mrTRG) scores (AUC = 0.722; sensitivity = 90.0 %). DiscussionThe use of FROC alongside comprehensive tumor histogram analysis was found to be practical and effective in evaluating the tumor response to nCRT in LARC patients.

Enhancing Brain Tumor Detection Through Custom Convolutional Neural Networks and Interpretability-Driven Analysis

Brain tumor detection is crucial for effective treatment planning and improved patient outcomes. However, existing methods often face challenges, such as limited interpretability and class imbalance in medical-imaging data. This study presents a novel, custom Convolutional Neural Network (CNN) architecture, specifically designed to address these issues by incorporating interpretability techniques and strategies to mitigate class imbalance. We trained and evaluated four CNN models (proposed CNN, ResNetV2, DenseNet201, and VGG16) using a brain tumor MRI dataset, with oversampling techniques and class weighting employed during training. Our proposed CNN achieved an accuracy of 94.51%, outperforming other models in regard to precision, recall, and F1-Score. Furthermore, interpretability was enhanced through gradient-based attribution methods and saliency maps, providing valuable insights into the model’s decision-making process and fostering collaboration between AI systems and clinicians. This approach contributes a highly accurate and interpretable framework for brain tumor detection, with the potential to significantly enhance diagnostic accuracy and personalized treatment planning in neuro-oncology.

Atlas-Based Structural Disconnectomes Are Associated with Cognitive Performance in Brain Tumors.

Background: Brain tumors are associated with impaired cognitive functioning, which may result from disruptions in brain structural connectivity. Estimating structural disconnections is a more advantageous representation of tumor impact and can be performed indirectly through normative brain atlases. Materials and Methods: Using a publicly available dataset of glioma and meningioma patient MRI scans and tumor masks, latent correlations were estimated between measures of structural disconnection and attention-based cognitive functioning. These measures included gray matter (GM) parcel damage, white matter tract damage, GM parcel-to-parcel disconnections, and reaction time (RTI) as part of the Cambridge Neuropsychological Test Automated Battery to assess attention. Results: Preprocessing pipelines with two different methods of minimizing the pathology impact on MRI normalization were utilized: cost-function masking and lesion filling. The results across both pipelines were nearly consistent, with significant correlations mainly found between RTI measures and the damage to the left inferior fronto-occipital and uncinate fasciculus, as well as the left prefrontal-visual disconnections. Conclusions: This alludes to the importance of left-hemispheric prefrontal-visual coupling in attention-based tasks, particularly those involving object- and feature-based attention.

CapeOX as neoadjuvant chemotherapy for locally advanced rectal cancer: might less be more?

BackgroundLocally advanced rectal cancer (LARC) poses unique challenges in treatment, with current neoadjuvant chemoradiotherapy (NA-CRT) showing limitations. The CapeOX regimen emerges as a potential less aggressive neoadjuvant chemotherapy (NAC) for LARC.MethodsWe conducted a retrospective study involving treatment-naïve patients with LARC from March 2014 to March 2021 who received 2–4 cycles of CapeOX NAC followed by radical surgery. Treatment response was evaluated using tumor regression grade (TRG), MRI-based TRG (MRI-TRG), and Neoadjuvant Rectal (NAR) score.Results94.7% of patients experienced symptom improvement and 96.4% achieved sphincter-preserving surgery. Post-NAC showed significant tumor regression and MRI confirmed a tumor length reduction (P < 0.001). Clinical and pathological staging discrepancies post-NAC suggest broader therapeutic advantages. 5-year overall and disease-free survival rates were 78.4% and 73.4%. NAR scores provided better prognostic accuracy than MRI-TRG.ConclusionCapeOX NAC presents notable benefits for LARC patients and its clinical significance may be underestimated. The NAR score demonstrates superior prognostic value over MRI-TRG.

Detection and classification on MRI images of brain tumor using YOLO NAS deep learning model

If a brain tumor is not properly diagnosed, it might result in fatal consequences and major health issues. As a result, a key component of diagnosis is the early identification of brain tumors and the precise categorization of brain tumor types. This research work focusses on detection and classification such as pituitary, meningioma, glioma, and non-tumorous tumors from brain tumor MRI image using YOLO NAS model. Advances in deep learning have led to an increasing awareness of computer-aided diagnosis technology. To improve classification performance, the input data set is acquired from the REMBRANDT repository using the Digital Database of Brain Tumor Magnetic Resonance Images. The primary phases of this task include segmentation, classification, and pre-processing. The researcher preprocessed the RGB image to exclude any undesired spots before locating the ROI. For each brain tumor image, we used the hybrid anisotropic diffusion filtering (HADF) technique to remove noise. The Encoder-Decoder Network (En–DeNet), which uses a deep neural network based on U-Net as the encoder and a pre-trained EfficientNet as the decoder, is then used to segment the MRI images. A proposed model was developed using a deep learning-based YOLO NAS technique to identify brain cancers from MRI images such as meningioma tumors, non-tumor, glioma tumors, and pituitary tumors. The suggested model's classification performance is weighed based on parameters such as precision, F1-score, sensitivity, accuracy, and specificity. The proposed fusion method YOLO NAS has improved classification results of accuracy (AC = 0.997%), specificity (SP = 0.985%), precision (PR = 0.982%), F1-score (F1 = 0.992%), and sensitivity (SE = 0.985%) using 2570 MRI data for training and 630 data for testing and validation of brain tumor cases. The results show that the brain tumor type classification system based on the proposed YOLO NAS technique works better than the DNN, PDCNN, DenseNet-161, and DCNN-SGD model classifiers.

Progress in the diagnosis and treatment of pancreatic cancer with acute pancreatitis as the initial symptom

Pancreatic cancer patients often have complaints such as upper abdominal pain and obstructive jaundice when seeking diagnosis and treatment. However, acute pancreatitis as a rare initial clinical manifestation of pancreatic cancer is often overlooked in clinical practice. This oversight often leads to a delayed diagnosis of pancreatic cancer, uncertainty in treatment strategies, and significantly affects patients' quality of life and prognosis. Therefore, early diagnosis and treatment, and active follow-up are crucial for patients with acute pancreatitis as an initial symptom of pancreatic cancer. Upon admission to such patients, common causes such as gallstones, alcohol abuse, and hyperlipidemia should be initially ruled out. Evaluation with tumor markers, CT and MRI, and endoscopic ultrasound are essential to confirm the diagnosis of pancreatic cancer. For patients with mild pancreatitis, managing peripancreatic inflammation first before radical resection of pancreatic cancer could reduce postoperative complications. Moreover, pancreatitis serves as a high-risk factor for pancreatic cancer, so it is crucial to closely follow up patients with pancreatitis to detect pancreatic cancer early.

Cross-site Validation of AI Segmentation and Harmonization in Breast MRI.

This work aims to perform a cross-site validation of automated segmentation for breast cancers in MRI and to compare the performance to radiologists. A three-dimensional (3D) U-Net was trained to segment cancers in dynamic contrast-enhanced axial MRIs using a large dataset from Site 1 (n = 15,266; 449 malignant and 14,817 benign). Performance was validated on site-specific test data from this and two additional sites, and common publicly available testing data. Four radiologists from each of the three clinical sites provided two-dimensional (2D) segmentations as ground truth. Segmentation performance did not differ between the network and radiologists on the test data from Sites 1 and 2 or the common public data (median Dice score Site 1, network 0.86 vs. radiologist 0.85, n = 114; Site 2, 0.91 vs. 0.91, n = 50; common: 0.93 vs. 0.90). For Site 3, an affine input layer was fine-tuned using segmentation labels, resulting in comparable performance between the network and radiologist (0.88 vs. 0.89, n = 42). Radiologist performance differed on the common test data, and the network numerically outperformed 11 of the 12 radiologists (median Dice: 0.85-0.94, n = 20). In conclusion, a deep network with a novel supervised harmonization technique matches radiologists' performance in MRI tumor segmentation across clinical sites. We make code and weights publicly available to promote reproducible AI in radiology.

Prediction of pathological response and lymph node metastasis after neoadjuvant therapy in rectal cancer through tumor and mesorectal MRI radiomic features

Establishing predictive models for the pathological response and lymph node metastasis in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT) based on MRI radiomic features derived from the tumor and mesorectal compartment (MC). This study included 209 patients with LARC who underwent rectal MRI both before and after nCRT. The patients were divided into a training set (n = 146) and a test set (n = 63). Regions of interest (ROIs) for the tumor and MC were delineated on both pre- and post-nCRT MRI images. Radiomic features were extracted, and delta radiomic features were computed. The predictive endpoints were pathological complete response (pCR), pathological good response (pGR), and lymph node metastasis (LNM). Feature selection for various models involved sequentially removing features with a correlation coefficient > 0.9, and features with P-values ≥ 0.05 in univariate analysis, followed by LASSO regression on the remaining features. Logistic regression models were developed, and their performance was evaluated using the area under the receiver operating characteristic curve (AUC). Among the 209 LARC patients, the number of patients achieving pCR, pGR, and LNM were 44, 118, and 40, respectively. The optimal model for predicting each endpoint is the combined model that incorporates pre- and delta-radiomics features for both the tumor and MC. These models exhibited superior performance with AUC values of 0.874 (for pCR), 0.801 (for pGR), and 0.826 (for LNM), outperforming the MRI tumor regression grade (mrTRG) which yielded AUC values of 0.800, 0.715, and 0.603, respectively. The results demonstrate the potential utility of the tumor and MC radiomics features, in predicting treatment efficacy among LARC patients undergoing nCRT.

Tumor infiltrating lymphocytes and change in tumor load on MRI to assess response and prognosis after neoadjuvant chemotherapy in breast cancer.

In this study, we aimed to explore if the combination of tumor infiltrating lymphocytes (TILs) and change in tumor load on dynamic contrast-enhanced magnetic resonance imaging leads to better assessment of response to neoadjuvant chemotherapy (NAC) in patients with breast cancer, compared to either alone. In 190 NAC treated patients, MRI scans were performed before and at the end of treatment. The percentage of stromal TILs (%TILs) was assessed in pre-NAC biopsies according to established criteria. Prediction models were developed with linear regression by least absolute shrinkage and selection operator and cross validation (CV), with residual cancer burden as the dependent variable. Discrimination for pathological complete response (pCR) was evaluated using area under the receiver operating characteristic curves (AUC). We used Cox regression analysis for exploring the association between %TILs and recurrence-free survival (RFS). Fifty-one patients reached pCR. In all patients, the %TILs model and change in MRI tumor load model had an estimated CV AUC of 0.69 (95% confidence interval (CI) 0.53-0.78) and 0.69 (95% CI 0.61-0.79), respectively, whereas a model combining the variables resulted in an estimated CV AUC of 0.75 (95% CI 0.66-0.83). In the group with tumors that were ER positive and HER2 negative (ER+/HER2-) and in the group with tumors that were either triple negative or HER2 positive (TN&HER2+) separately, the combined model reached an estimated CV AUC of 0.72 (95% CI 0.60-0.88) and 0.70(95% CI 0.59-0.82), respectively. A significant association was observed between pre-treatment %TILS and RFS (hazard ratio (HR) 0.72 (95% CI 0.53-0.98), for every standard deviation increase in %TILS, p = 0.038). The combination of TILs and MRI is informative of response to NAC in patients with both ER+/HER2- and TN&HER2+ tumors.

Differentiating struma ovarii from FIGO stage I malignant ovarian tumors in O-RADS MRI 5 lesions: a targeted cohort study.

Differentiating struma ovarii from FIGO stage I malignant ovarian tumors in O-RADS MRI 5 lesions: a targeted cohort study.

Integrated MRI radiomics, tumor microenvironment, and clinical risk factors for improving survival prediction in patients with glioblastomas.

To construct acomprehensive model for predicting the prognosis of patients with glioblastoma (GB) using aradiomics method and integrating clinical risk factors, tumor microenvironment (TME), and imaging characteristics. In this retrospective study, we included 148 patients (85males and 63females; median age 53years) with isocitrate dehydrogenase-wildtype GB between January 2016 and April 2022. Patients were randomly divided into the training (n = 104) and test (n = 44) sets. The best feature combination related to GB overall survival (OS) was selected using LASSO Cox regression analyses. Clinical, radiomics, clinical-radiomics, clinical-TME, and clinical-radiomics-TME models were established. The models' concordance index (C-index) was evaluated. The survival curve was drawn using the Kaplan-Meier method, and the prognostic stratification ability of the model was tested. LASSO Cox analyses were used to screen the factors related to OS in patients with GB, including MGMT (hazard ratio [HR] = 0.642; 95% CI 0.414-0.997; P = 0.046), TERT (HR = 1.755; 95% CI 1.095-2.813; P = 0.019), peritumoral edema (HR = 1.013; 95% CI 0.999-1.027; P = 0.049), tumor purity (TP; HR = 0.982; 95% CI 0.964-1.000; P = 0.054), CD163 + tumor-associated macrophages (TAMs; HR = 1.049; 95% CI 1.021-1.078; P < 0.001), CD68 + TAMs (HR = 1.055; 95% CI 1.018-1.093; P = 0.004), and the six radiomics features. The clinical-radiomics-TME model had the best survival prediction ability, the C‑index was 0.768 (0.717-0.819). The AUC of 1‑, 2‑, and 3‑year OS prediction in the test set was 0.842, 0.844, and 0.795, respectively. The clinical-radiomics-TME model is the most effective for predicting the survival of patients with GB. Radiomics features, TP, and TAMs play important roles in the prognostic model.