Peritumoral Region Research Articles

Auto-Segmentation and Classification of Glioma Tumors with the Goals of Treatment Response Assessment Using Deep Learning Based on Magnetic Resonance Imaging.

Glioma is the most common primary intracranial neoplasm in adults. Radiotherapy is a treatment approach in glioma patients, and Magnetic Resonance Imaging (MRI) is a beneficial diagnostic tool in treatment planning. Treatment response assessment in glioma patients is usually based on the Response Assessment in Neuro Oncology (RANO) criteria. The limitation of assessment based on RANO is two-dimensional (2D) manual measurements. Deep learning (DL) has great potential in neuro-oncology to improve the accuracy of response assessment. In the current research, firstly, the BraTS 2018 Challenge dataset included 210 HGG and 75 LGGwere applied to train a designed U-Net network for automatic tumor and intra-tumoral segmentation, followed by training of the designed classifier with transfer learning for determining grading HGG and LGG. Then, designed networks were employed for the segmentation and classification of local MRI images of 49 glioma patients preand post-radiotherapy. The results of tumor segmentation and its intra-tumoral regions were utilized to determine the volume of different regions and treatment response assessment. Treatment response assessment demonstrated that radiotherapy is effective on the whole tumor and enhancing region with p-value ≤ 0.05 with a 95% confidence level, while it did not affect necrosis and peri-tumoral edema regions. This work demonstrated the potential of using deep learning in MRI images to provide a beneficial tool in the automated treatment response assessment so that the patient can obtain the best treatment.

Multimodal radiomics and nomogram-based prediction of axillary lymph node metastasis in breast cancer: An analysis considering optimal peritumoral region.

To explore the optimal peri-tumoral regions on ultrasound (US) images and investigate the performance of multimodal radiomics for predicting axillary lymph node metastasis (ALNM). This retrospective study included 326 patients (training cohort: n = 162, internal validation cohort: n = 74, external validation cohort: n = 90). Intra-tumoral region of interests (ROIs) were delineated on US and digital mammography (DM) images. Peri-tumoral ROI (PTR) on US images were gained by dilating actual 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 and 3.5 mm radius surrounding the tumor. Support vector machine (SVM) method was used to calculate the importance of radiomics features and to pick the 10 most important. Recursive feature elimination-SVM was used to evaluate the efficacy of models with different feature numbers used. The PTR0.5mm yielded a maximum AUC of 0.802 (95% confidence interval (CI): 0.676-0.901) within the validation cohort using SVM classifier. The multimodal radiomics (intra-tumoral US and DM and US-based PTR0.5mm radiomics model) achieved the highest predictive ability (AUC = 0.888/0.844/0.835 and 95% CI = 0.829-0.936/0.741-0.929/0.752-0.896 for training/internal validation/external validation cohort, respectively). The PTR0.5mm could be the optimal area for predicting ALNM. A favorable predictive accuracy for predicting ALNM was achieved using multimodal radiomics and its based nomogram.

The role of 3-Tesla magnetic resonance perfusion and spectroscopy in distinguishing glioblastoma from solitary brain metastasis.

This study aimed to assess the value of magnetic resonance perfusion (MR perfusion) and magnetic resonance spectroscopy (MR spectroscopy) in 3.0-Tesla magnetic resonanceimaging (MRI) for differential diagnosis of glioblastoma (GBM) and solitary brain metastasis (SBM). This retrospective study involved 36 patients, including 24 cases of GBM and 12 of SBM diagnosed using histopathology. All patients underwent a 3.0-Tesla MRI examination with pre-operative MR perfusion and MR spectroscopy. We assessed the differences in age, sex, cerebral blood volume (CBV), relative CBV (rCBV), and the metabolite ratios of choline/N-acetylaspartate (Cho/NAA) and Cho/creatine between the GBM and SBM groups using the Mann-Whitney U-test and Chi-square test. The cutoff value, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value of the significantly different parameters between these two groups were determined using the receiver operating characteristic curve. In MR perfusion, the CBV of the peritumoral region (pCBV) had the highest preoperative predictive value in discriminating GBM from SBM (cutoff: 1.41; sensitivity: 70.83%; and specificity: 83.33%), followed by the ratio of CBV of the solid tumor component to CBV of normal white matter (rCBVt/n) and the ratio of CBV of the pCBV to CBV of normal white matter (rCBVp/n). In MR spectroscopy, the Cho/NAA ratio of the pCBV (pCho/NAA; cutoff: 1.02; sensitivity: 87.50%; and specificity: 75%) and the Cho/NAA ratio of the solid tumor component (tCho/NAA; cutoff: 2.11; sensitivity: 87.50%; and specificity: 66.67%) were significantly different between groups. Moreover, combining these remarkably different parameters increased their diagnostic utility for distinguishing between GBM and SBM. pCBV, rCBVt/n, rCBVp/n, pCho/NAA, and tCho/NAA are useful indices for differentiating between GBM and SBM. Combining these indices can improve diagnostic performance in distinguishing between these two tumors.

Preoperative prediction of lymphovascular space invasion in endometrioid adenocarcinoma: an MRI-based radiomics nomogram with consideration of the peritumoral region.

Lymphovascular space invasion (LVSI) of endometrial cancer (EC) is a postoperative histological index, which is associated with lymph node metastases. A preoperative acknowledgement of LVSI status might aid in treatment decision-making. To explore the utility of multiparameter magnetic resonance imaging (MRI) and radiomic features obtained from intratumoral and peritumoral regions for predicting LVSI in endometrioid adenocarcinoma (EEA). A total of 334 EEA tumors were retrospectively analyzed. Axial T2-weighted (T2W) imaging and apparent diffusion coefficient (ADC) mapping were conducted. Intratumoral and peritumoral regions were manually annotated as the volumes of interest (VOIs). A support vector machine was applied to train the prediction models. Multivariate logistic regression analysis was used to develop a nomogram based on clinical and tumor morphological parameters and the radiomics score (RadScore). The predictive performance of the nomogram was assessed by the area under the receiver operator characteristic curve (AUC) in the training and validation cohorts. Among the features obtained from different imaging modalities (T2W imaging and ADC mapping) and VOIs, the RadScore had the best performance in predicting LVSI classification (AUCtrain = 0.919, and AUCvalidation = 0.902). The nomogram based on age, CA125, maximum anteroposterior tumor diameter on sagittal T2W images, tumor area ratio, and RadScore was established to predict LVSI had AUC values in the training and validation cohorts of 0.962 (sensitivity 94.0%, specificity 86.0%) and 0.965 (sensitivity 90.0%, specificity 85.3%), respectively. The intratumoral and peritumoral imaging features were complementary, and the MRI-based radiomics nomogram might serve as a non-invasive biomarker to preoperatively predict LVSI in patients with EEA.

Deep radiomic model based on the sphere–shell partition for predicting treatment response to chemotherapy in lung cancer

BackgroundThe prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients. ObjectivesTo develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images. Materials and MethodsThis retrospective multicenter study enrolled 485 patients with NSCLC who received chemotherapy alone as a first-line treatment. Two integrated models were developed using radiomic and deep-learning-based features. First, we partitioned pre-chemotherapy CT images into spheres and shells with different radii around the tumor (0–3, 3–6, 6–9, 9–12, 12–15 mm) containing intratumoral and peritumoral regions. Second, we extracted radiomic and deep-learning-based features from each partition. Third, using radiomic features, five sphere–shell models, one feature fusion model, and one image fusion model were developed. Finally, the model with the best performance was validated in two cohorts. ResultsAmong the five partitions, the model of 9–12 mm achieved the highest area under the curve (AUC) of 0.87 (95% confidence interval: 0.77–0.94). The AUC was 0.94 (0.85–0.98) for the feature fusion model and 0.91 (0.82–0.97) for the image fusion model. For the model integrating radiomic and deep-learning-based features, the AUC was 0.96 (0.88–0.99) for the feature fusion method and 0.94 (0.85–0.98) for the image fusion method. The best-performing model had an AUC of 0.91 (0.81–0.97) and 0.89 (0.79–0.93) in two validation sets, respectively. ConclusionsThis integrated model can predict the response to chemotherapy in NSCLC patients and assist physicians in clinical decision-making.

Radiomics from Mesorectal Blood Vessels and Lymph Nodes: A Novel Prognostic Predictor for Rectal Cancer with Neoadjuvant Therapy.

The objective of our study is to investigate the predictive value of various combinations of radiomic features from intratumoral and different peritumoral regions of interest (ROIs) for achieving a good pathological response (pGR) following neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). This retrospective study was conducted using data from LARC patients who underwent nCRT between 2013 and 2021. Patients were divided into training and validation cohorts at a ratio of 4:1. Intratumoral ROIs (ROIITU) were segmented on T2-weighted imaging, while peritumoral ROIs were segmented using two methods: ROIPTU_2mm, ROIPTU_4mm, and ROIPTU_6mm, obtained by dilating the boundary of ROIITU by 2 mm, 4 mm, and 6 mm, respectively; and ROIMR_F and ROIMR_BVLN, obtained by separating the fat and blood vessels + lymph nodes in the mesorectum. After feature extraction and selection, 12 logistic regression models were established using radiomics features derived from different ROIs or ROI combinations, and five-fold cross-validation was performed. The average area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the models. The study included 209 patients, consisting of 118 pGR and 91 non-pGR patients. The model that integrated ROIITU and ROIMR_BVLN features demonstrated the highest predictive ability, with an AUC (95% confidence interval) of 0.936 (0.904-0.972) in the training cohort and 0.859 (0.745-0.974) in the validation cohort. This model outperformed models that utilized ROIITU alone (AUC = 0.779), ROIMR_BVLN alone (AUC = 0.758), and other models. The radscore derived from the optimal model can predict the treatment response and prognosis after nCRT. Our findings validated that the integration of intratumoral and peritumoral radiomic features, especially those associated with mesorectal blood vessels and lymph nodes, serves as a potent predictor of pGR to nCRT in patients with LARC. Pending further corroboration in future research, these insights could provide novel imaging markers for refining therapeutic strategies.

RNA Sequencing of Intraoperative Peritumoral Tissues Reveals Potential Pathways Involved in Glioma-Related Seizures.

Tumor-induced changes in the peritumoral neocortex play a crucial role in generation of seizures. This study aimed to investigate the molecular mechanisms potentially involved in peritumoral epilepsy in low-grade gliomas (LGGs). Intraoperative peritumoral brain tissues resected from LGG patients with seizures (pGRS) or without seizures (pGNS) were used for RNA sequencing (RNA-seq). Comparative transcriptomics was performed to identify differentially expressed genes (DEGs) in pGRS compared to pGNS using deseq2 and edgeR packages (R). Gene set enrichment analysis (GSEA) using Gene Ontology terms and Kyoto Encyclopedia of Genes & Genomes (KEGG) pathways was performed using the clusterProfiler package (R). The expression of key genes was validated at the transcript and protein levels in the peritumoral region using real-time PCR and immunohistochemistry, respectively. A total of 1073 DEGs were identified in pGRS compared to pGNS, of which 559 genes were upregulated and 514 genes were downregulated (log2 fold-change ≥ 2, padj < 0.001). The DEGs in pGRS were highly enriched in the "Glutamatergic Synapse" and "Spliceosome" pathways, with increased expression of GRIN2A (NR2A), GRIN2B (NR2B), GRIA1 (GLUR1), GRIA3 (GLUR3), GRM5, CACNA1C, CACNA1A, and ITPR2. Moreover, increased immunoreactivity was observed for NR2A, NR2B, and GLUR1 proteins in the peritumoral tissues of GRS. These findings suggest that altered glutamatergic signaling and perturbed Ca2+ homeostasis may be potential causes of peritumoral epilepsy in gliomas. This explorative study identifies important genes/pathways that merit further characterization for their potential involvement in glioma-related seizures.

Intra- and peri-tumoral radiomics based on dynamic contrast-enhanced MRI for prediction of benign disease in BI-RADS 4 breast lesions: A multicentre study.

e13542 Background: The study aimed to develop and optimize a radiomics model with features extracted from breast intra- and peri-tumoral region in dynamic contrast-enhanced (DCE) MRI to distinguish benign from malignant breast lesions of Breast Imaging Reporting and Data System (BI-RADS) 4, thereby obviating unnecessary biopsies. Methods: We retrospectively analyzed data of women with BI-RADS 4 or 5 lesions from three hospitals. The regions of interest (ROIs) of intra-tumoral regions were delineated manually on each slice of image. The peri-tumoral regions were then obtained by equidistant 3-dimensinal dilation of the tumor border with 1, 2 and 3mm, respectively. Seven classification models were built with features extracted from the intra- and peri-tumoral regions, including the Intra, Peri1mm, Peri2mm, Peri3mm, Intra+peri1mm, Intra+peri2mm and Intra+peri3mm. Diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC) and compared by DeLong test. Subgroup analysis was performed after stratifying all lesions by enhancement pattern. To assess the rate of avoidable biopsies, three exploratory cutoff values on the ROC curve were examined. Results: The Comb2 model, built with features from peri-tumoral 2mm and intra-tumoral region, demonstrated the best performance with AUCs of 0·901 and 0·863 in the development and external test cohort, respectively. The Comb2 model was robust in both mass and non-mass enhancement lesions. At the three exploratory cutoff values on the ROC curve, the model identified 11·2% (sensitivity of C1≥90%), 49·5% (sensitivity of C2≥95%) and 63·3% (sensitivity of C3≥98%) of the benign lesions in the development cohort. Applying the identified cutoff values in the external test cohort showed the potential to lower the number of unnecessary biopsies of benign lesions. Conclusions: An MRI-based radiomics model built with features extracted from the intra-tumoral region combined with the peri-tumoral 2mm showed the best potential to reduce false-positive diagnoses and may avoid unnecessary biopsies with a low underestimate risk.

DCE-MRI-derived microvasculature mapping and outcome prediction in patients with breast cancer.

e12614 Background: Tumor response to neoadjuvant therapy (NAT) in patients with breast cancer is related to perfusion in the tumor microenvironment and surrounding tissue. Direct and indirect measures of microvascular (MV) permeability could improve the ability to predict short- and long-term patient outcomes. Detection and quantification of circulating tumor DNA (ctDNA) is one such measurement that depends on microvasculature and has been shown to strongly correlate with both response to therapy and overall prognosis in a wide range of cancers. We hypothesized that a combination of ctDNA and numerical computations of MV permeability using pretreatment dynamic contrast-enhanced (DCE) MRI images (MV maps) can predict patient response to NAT and long-term survival outcomes. Here, we show how MV maps — computed from standard-of-care (SOC) imaging — improve upon ctDNA measurements in predicting pathologic complete response (pCR) following NAT as well as distant recurrence free survival (DRFS). Methods: DCE-MRIs were obtained for a sub-sample of I-SPY2 patients (n = 81) whose ctDNA levels were previously reported (Magbanua et al 2021). Tumors were segmented from pretreatment images by a convolutional neural network with manual verification. MV maps were computed using a Tofts-like model to describe the transfer kinetics between microvasculature and extracellular extravascular space. Microvasculature parameters (MVPs), such as median and skewness, were extracted from MV maps to describe the distribution of kinetic parameters in the intra- and peritumoral regions. Logistic regression was used to predict pCR with leave-one-out cross validation. Univariate Cox models were used to stratify DRFS. Results: MVPs exhibited a moderate correlation with pretreatment ctDNA levels (Spearman rho = -0.26; p = 0.018) and with pCR status in this cohort (rho = 0.25; p = 0.026). In a series of logistic regression models, a combination of SOC clinical features and ctDNA levels correctly classified 23% of true pCRs, with an F1 score of 0.58. This predictive power increased when informed by MV maps; 45% of true pCRs were identified after the addition of MVPs, and the F1 score increased to 0.68. In univariate Cox models, 24 MVPs significantly stratified DRFS (p &lt; 0.05) when uncorrected for multiple hypothesis testing bias. The top MVP carried a hazard ratio 3.8 (95%CI 1.6 - 9.0) compared to 11.9 (95%CI 2.7 - 53.6) for ctDNA levels. Conclusions: DCE-MRI-derived kinetic features are calculated from SOC images with no additional clinical intervention. We found that MVPs substantially improve pCR prediction when added to models trained on ctDNA levels and clinical features. ctDNA levels and MVPs were predictive of DRFS to differing degrees of confidence. Although further validation in larger datasets is needed, this exploratory analysis uncovered a promising role for MV maps in predicting response to NAT as well as patient outcomes.

Pretreatment radiomic biomarker for immunotherapy responder prediction in IB-IV stage NSCLC: A multi-center retrospective study.

e21060 Background: The current biomarker of immune checkpoint inhibitors (ICIs) efficacy is not sufficient. This study investigated the correlation between radiomic biomarker and immunotherapy responder in patients with IB-IV stage non-small-cell lung cancer (NSCLC), including its biological explanation for ICIs treatment response prediction. Methods: CT images from 319 pretreatment NSCLC patients receiving immunotherapy between January, 2015 and November, 2021 were retrospectively collected and were distributed into a discovery (n = 214), independent validation (n = 54), and external test cohort (n = 51). 851 features were extracted from both tumoral and each peri-tumoral VOIs. The reference standard is the durable clinical benefit (DCB) and the crucial exclusion criteria are missing pretreatment CT scans. The predictive value of combined radiomic signature (CRS) was assessed in a cohort of 98 resectable NSCLC patients receiving ICIs. The association of radiomic features and tumor immune landscape on the online dataset (n = 60) was also evaluated. A clinical model was constructed and incorporated with radiomic signature to improve performance further. Results: CRS discriminated between DCB and non-DCB patients in the training cohort (area under the curve (AUC): 0.82, 95% CI: 0.75 to 0.88)) and independent cohort (AUC: 0.75, 95%CI: 0.62 to 0.88). In this study, the predictive value of CRS was better than PD-L1 expression (AUC of PD-L1 subset: 0.59, 95%CI: 0.50 to 0.69) or clinical model (AUC of validation cohort: 0.66, 95%CI: 0.51 to 0.81). When predicting pathological response, CRS divided patients into a major pathological response (MPR) and non-MPR group (AUC: 0.76, 95%CI: 0.67 to 0.81). Moreover, CRS showed a promising stratified ability on overall survival (OS; HR: 0.48, 95%CI: 0.27 to 0.89; p = 0.020) and progression-free survival (PFS; HR: 0.43, 95%CI: 0.26 to 0.74; p = 0.002). Biological pathway analysis on online dataset revealed that dysregulation of Hedgehog and Hippo signaling pathways got involved in the prediction of radiomic features. Conclusions: By analyzing both tumoral and peri-tumoral regions of CT images in a radiomic strategy, we developed a non-invasive biomarker distinguishing responder from ICIs therapy, which might inform clinical decisions on the use of ICIs in advanced as well as resectable NSCLC patients.

Intra- and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer.

Radiomics has been applied for assessing lymphovascular invasion (LVI) in patients with breast cancer. However, associations between features from peritumoral regions and the LVI status were not investigated. To investigate the value of intra- and peritumoral radiomics for assessing LVI, and to develop a nomogram to assist in making treatment decisions. Retrospective. Three hundred and sixteen patients were enrolled from two centers and divided into training (N = 165), internal validation (N = 83), and external validation (N = 68) cohorts. 1.5 T and 3.0 T/dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI). Radiomics features were extracted and selected based on intra- and peritumoral breast regions in two magnetic resonance imaging (MRI) sequences to create the multiparametric MRI combined radiomics signature (RS-DCE plus DWI). The clinical model was built with MRI-axillary lymph nodes (MRI ALN), MRI-reported peritumoral edema (MPE), and apparent diffusion coefficient (ADC). The nomogram was constructed with RS-DCE plus DWI, MRI ALN, MPE, and ADC. Intra- and interclass correlation coefficient analysis, Mann-Whitney U test, and least absolute shrinkage and selection operator regression were used for feature selection. Receiver operating characteristic and decision curve analyses were applied to compare performance of the RS-DCE plus DWI, clinical model, and nomogram. A total of 10 features were found to be associated with LVI, 3 from intra- and 7 from peritumoral areas. The nomogram showed good performance in the training (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.884 vs. 0.695 vs. 0.870), internal validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.813 vs. 0.695 vs. 0.794), and external validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.862 vs. 0.601 vs. 0.849) cohorts. The constructed preoperative nomogram might effectively assess LVI. 3 TECHNICAL EFFICACY: Stage 2.

Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study.

To develop a fusion model based on clinicopathological factors and MRI radiomics features for the prediction of recurrence risk in patients with endometrial cancer (EC). A total of 421 patients with histopathologically proved EC (101 recurrence vs. 320 non-recurrence EC) from four medical centers were included in this retrospective study, and were divided into the training (n = 235), internal validation (n = 102), and external validation (n = 84) cohorts. In total, 1702 radiomics features were respectively extracted from areas with different extensions for each patient. The extreme gradient boosting (XGBoost) classifier was applied to establish the clinicopathological model (CM), radiomics model (RM), and fusion model (FM). The performance of the established models was assessed by the discrimination, calibration, and clinical utility. Kaplan-Meier analysis was conducted to further determine the prognostic value of the models by evaluating the differences in recurrence-free survival (RFS) between the high- and low-risk patients of recurrence. The FMs showed better performance compared with the models based on clinicopathological or radiomics features alone but with a reduced tendency when the peritumoral area (PA) was extended. The FM based on intratumoral area (IA) [FM (IA)] had the optimal performance in predicting the recurrence risk in terms of the ROC, calibration curve, and decision curve analysis. Kaplan-Meier survival curves showed that high-risk patients of recurrence defined by FM (IA) had a worse RFS than low-risk ones of recurrence. The FM integrating intratumoral radiomics features and clinicopathological factors could be a valuable predictor for the recurrence risk of EC patients. An accurate prediction based on our developed FM (IA) for the recurrence risk of EC could facilitate making an individualized therapeutic decision and help avoid under- or over-treatment, therefore improving the prognosis of patients. • The fusion model combined clinicopathological factors and radiomics features exhibits the highest performance compared with the clinicopathological model and radiomics model. • Although higher values of area under the curve were observed for all fusion models, the performance tended to decrease with the extension of the peritumoral region. • Identifying patients with different risks of recurrence, the developed models can be used to facilitate individualized management.

Combining amide proton transfer-weighted and arterial spin labeling imaging to differentiate solitary brain metastases from glioblastomas

PurposeTo evaluate the clinical utility of amide proton transfer-weighted imaging (APTw) and arterial spin labeling (ASL) in differentiating solitary brain metastases (SBMs) from glioblastomas (GBMs). MethodsForty-eight patients diagnosed with brain tumors were enrolled. All patients underwent conventional MRI, APTw, and ASL scans on a 3.0 T MRI system. The mean APTw value and mean cerebral blood flow (CBF) value were measured. The differences in various parameters between GBMs and SBMs were assessed using the independent-samples t-test. The quantitative performance of these MRI parameters in distinguishing between GBMs and SBMs was evaluated using receiver operating characteristic (ROC) curve analysis. ResultsGBMs exhibited significantly higher APTw and CBF values in peritumoral regions compared with SBMs (P < 0.05). There was no significant difference between SBMs and GBMs in tumor cores. APTw MRI had a higher diagnostic efficiency in differentiating SBMs from GBMs (area under the curve [AUC]: 0.864; 75.0% sensitivity and 81.8% specificity). Combined use of APTw and CBF value increased the AUC to 0.927. ConclusionAPTw may be superior to ASL for distinguishing between SBMs and GBMs. Combination of APTw and ASL showed better discrimination and a superior diagnostic performance.

Immune-Related Gene Expression Profile at Peritumoral Tonsillar Tissue Is Modified by Oropharyngeal Cancer Nodal Status

Secondary lymphoid organs, such as lymph nodes and tonsils, serve as an interface between the immune system and tumor cells as an initial antigen-presentation site, crucial in antitumor immune response and disease progression. In oropharyngeal cancers originating from palatine tonsils, it was hypothesized that characterizing the immunologic process occurring in the peritumoral tonsil tissue would elucidate immune mechanisms of the lymphatic spread of the disease. A total of 33 patients were enrolled and divided into two cohorts. In Cohort 1 (6 patients), gene expression profiles at the peritumoral lymph regions and tumor regions were analyzed using the whole-transcriptome atlas. In the peritumoral lymph regions, 237 genes were up-regulated in metastasis-negative cases compared with metastasis-positive ones, but only 1 gene was up-regulated in tumor regions. In Cohort 2 (27 patients), microarray analysis of peritumoral tonsil tissue revealed 192 up-regulated genes. Gene ontology analysis revealed the significantly enriched Gene Ontology terms associated with T-cell activation; top 10 hub genes, as ranked by degree, were PTPRC, TLR4, CD80, CD40, STAT3, CD28, CD40LG, CD44, CCR7, and IL7R. Gene set enrichment analysis combined with principal component analysis were used to effectively classify patients as lymph node metastasis positive or negative. These findings suggest peritumoral tonsils as a potential target for investigating the immune mechanisms associated with the lymphatic spread of the disease in oropharyngeal cancers.

Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm.

This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI of 202 patients with solitary brain tumor (104 glioblastoma and 98 BM) were retrospectively obtained between February 2016 and September 2022. The data were divided into training and validation sets in a 7:3 ratio. An additional 32 patients (19 glioblastoma and 13 BM) from a different hospital were considered testing set. Single-MRI-sequence DL models were developed using the 3D residual network-18 architecture in tumoral (T model) and tumoral + peritumoral regions (T&P model). Furthermore, the combination model based on conventional MRI and DWI was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The attention area of the model was visualized as a heatmap by gradient-weighted class activation mapping technique. For the single-MRI-sequence DL model, the T2WI sequence achieved the highest AUC in the validation set with either T models (0.889) or T&P models (0.934). In the combination models of the T&P model, the model of DWI combined with T2WI and contrast-enhanced T1WI showed increased AUC of 0.949 and 0.930 compared with that of single-MRI sequences in the validation set, respectively. And the highest AUC (0.956) was achieved by combined contrast-enhanced T1WI, T2WI, and DWI. In the heatmap, the central region of the tumoral was hotter and received more attention than other areas and was more important for differentiating glioblastoma from BM. A conventional MRI-based DL model could differentiate glioblastoma from solitary BM, and the combination models improved classification performance.

A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region.

Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In this manuscript, we apply a transfer learning approach to predict recurrence in NSCLC patients, exploiting only data acquired during its screening phase. Particularly, we used a public radiogenomic dataset of NSCLC patients having a primary tumor CT image and clinical information. Starting from the CT slice containing the tumor with maximum area, we considered three different dilatation sizes to identify three Regions of Interest (ROIs): CROP (without dilation), CROP 10 and CROP 20. Then, from each ROI, we extracted radiomic features by means of different pre-trained CNNs. The latter have been combined with clinical information; thus, we trained a Support Vector Machine classifier to predict the NSCLC recurrence. The classification performances of the devised models were finally evaluated on both the hold-out training and hold-out test sets, in which the original sample has been previously divided. The experimental results showed that the model obtained analyzing CROP 20 images, which are the ROIs containing more peritumoral area, achieved the best performances on both the hold-out training set, with an AUC of 0.73, an Accuracy of 0.61, a Sensitivity of 0.63, and a Specificity of 0.60, and on the hold-out test set, with an AUC value of 0.83, an Accuracy value of 0.79, a Sensitivity value of 0.80, and a Specificity value of 0.78. The proposed model represents a promising procedure for early predicting recurrence risk in NSCLC patients.

Multiregional radiomic model for breast cancer diagnosis: value of ultrasound-based peritumoral and parenchymal radiomics.

Breast cancer consists not only of neoplastic cells but also of significant changes in the surrounding and parenchymal stroma, which can be reflected in radiomics. This study aimed to perform breast lesion classification through an ultrasound-based multiregional (intratumoral, peritumoral, and parenchymal) radiomic model. We retrospectively reviewed ultrasound images of breast lesions from institution #1 (n=485) and institution #2 (n=106). Radiomic features were extracted from different regions (intratumoral, peritumoral, and ipsilateral breast parenchymal) and selected to train the random forest classifier with the training cohort (n=339, a subset of the institution #1 dataset). Then, the intratumoral, peritumoral, and parenchymal, intratumoral & peritumoral (In&Peri), intratumoral & parenchymal (In&P), and intratumoral & peritumoral & parenchymal (In&Peri&P) models were developed and validated on the internal (n=146, another subset of institution 1) and external (n=106, institution #2 dataset) test cohorts. Discrimination was evaluated using the area under the curve (AUC). Calibration curve and Hosmer-Lemeshow test assessed calibration. Integrated discrimination improvement (IDI) was used to assess performance improvement. The performance of the In&Peri (AUC values 0.892 and 0.866), In&P (0.866 and 0.863), and In&Peri&P (0.929 and 0.911) models was significantly better than that of the intratumoral model (0.849 and 0.838) in the internal and external test cohorts (IDI test, all P<0.05). The intratumoral, In&Peri and In&Peri&P models showed good calibration (Hosmer-Lemeshow test, all P>0.05). The multiregional (In&Peri&P) model had the highest discrimination among the 6 radiomic models in the test cohorts, respectively. The multiregional model combining radiomic information of intratumoral, peritumoral, and ipsilateral parenchymal regions yielded better performance than the intratumoral model in distinguishing malignant breast lesions from benign lesions.

Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model.

Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach for predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials and Methods Patients with pathologically proven HCC from May 2012 to September 2020 were retrospectively included from four medical centers. Radiomics features were extracted from tumors and peritumor regions on preoperative registration or subtraction CT images. In the training set, these features were used to build five radiomics models via logistic regression after feature reduction. The models were tested using internal and external test sets against a pathologic reference standard to calculate area under the receiver operating characteristic curve (AUC). The optimal AUC radiomics model and clinical-radiologic characteristics were combined to build the hybrid model. The log-rank test was used in the outcome cohort (Kunming center) to analyze early recurrence-free survival and overall survival based on high versus low model-derived score. RNA sequencing data from The Cancer Image Archive were used for gene expression analysis. Results A total of 773 patients (median age, 59 years; IQR, 49-64 years; 633 men) were divided into the training set (n = 334), internal test set (n = 142), external test set (n = 141), outcome cohort (n = 121), and RNA sequencing analysis set (n = 35). The AUCs from the radiomics and hybrid models, respectively, were 0.76 and 0.86 for the internal test set and 0.72 and 0.84 for the external test set. Early recurrence-free survival (P < .01) and overall survival (P < .007) can be categorized using the hybrid model. Differentially expressed genes in patients with findings positive for MVI were involved in glucose metabolism. Conclusion The hybrid model showed the best performance in prediction of MVI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Summers in this issue.

Intra- and Peritumoral Radiomics of Contrast-Enhanced Mammography Predicts Axillary Lymph Node Metastasis in Patients With Breast Cancer: A Multicenter Study

This multicenter study aimed to explore the feasibility of radiomics based on intra- and peritumoral regions on preoperative breast cancer contrast-enhanced mammography (CEM) to predict axillary lymph node (ALN) metastasis. A total of 809 patients with preoperative breast cancer CEM images from two centers were retrospectively recruited. Least absolute shrinkage and selection operator (LASSO) regression was used to select radiomics features extracted from CEM images in regions of the tumor and peritumoral area of five and ten mm as well as construct radiomics signature. A nomogram, including the optimal radiomics signature and clinicopathological factors, was then constructed. Nomogram performance was evaluated using AUC and compared with breast radiologists directly. In the internal testing set, AUCs of peritumoral signatures decreased when the peritumoral area increased and signaturetumor+10mm demonstrated the best performance with an AUC of 0.712. The nomogram incorporating signaturetumor+10mm, tumor diameter, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and CEM-reported lymph node status yielded maximum AUCs of 0.753 and 0.732 in internal and external testing sets, respectively. Moreover, the nomogram outperformed radiologists and improved diagnostic performance of radiologists. The nomogram based on CEM intra- and peritumoral regions may provide a noninvasive auxiliary tool to guide treatment strategy of ALN metastasis in breast cancer.

Evaluation of parametrial infiltration in patients with IB-IIB cervical cancer by a radiomics model integrating features from tumoral and peritumoral regions on 18 F-FDG PET/MRI images.

Parametrial infiltration (PMI) is an essential factor in staging and planning treatment of cervical cancer. The purpose of this study was to develop a radiomics model for accessing PMI in patients with IB-IIB cervical cancer using the features from 18 F-FDG PET/MRI images. In this retrospective study, 66 patients with FIGO stage IB-IIB cervical cancer (22 with PMI and 44 without PMI) underwent 18 F-FDG PET/MRI were divided into a training (n = 46) and a testing dataset (n = 20). Features were extracted from both tumoral and peritumoral regions on 18 F-FDG PET/MRI. Single-modality and multi-modality radiomics models were developed with random forest to predict PMI. The performance of models was evaluated with F1 score, accuracy and AUC. Kappa test was used to observe the differences between pathological results and PMI evaluated by radiomics-based models. Intraclass correlation coefficient for features extracted from each ROI was measured. Three-fold cross-validation was conducted to confirm the diagnostic ability of the features. The radiomics models developed by features from primary tumor on T2 weighted (F1 Score = 0.400, accuracy = 0.700, AUC = 0.708, Kappa = 0.211, P = 0.329) and peritumoral region on PET (F1 Score = 0.533, accuracy = 0.650, AUC = 0.714, Kappa = 0.271, P = 0.202) achieved better performances in the testing dataset among four single-ROI radiomics models. The combined model using the features from primary tumor on T2 weighted and peritumoral region on PET achieved the best performance (F1 Score = 0.727, accuracy = 0.850, AUC = 0.774, Kappa = 0.625, P < 0.05). The results suggest that 18 F-FDG PET/MRI could provide complementary information of cervical cancer. The radiomics-based method integrating the features from tumoral and peritumoral regions on 18 F-FDG PET/MRI had a superior performance on PMI evaluation.