Peritumoral Area Research Articles

Intratumoral and peritumoral radiomics of MRIs predicts pathologic complete response to neoadjuvant chemoimmunotherapy in patients with head and neck squamous cell carcinoma

BackgroundFor patients with locally advanced head and neck squamous cell carcinoma (HNSCC), combined programmed death receptor-1 inhibitor and chemotherapy improved response rate to neoadjuvant therapy. However, treatment response varies among...

A Histopathologic Correlation Study Evaluating Glymphatic Function in Brain Tumors by Multiparametric MRI.

This study aimed to elucidate the impact of brain tumors on cerebral edema and glymphatic drainage by leveraging advanced MRI techniques to explore the relationships among tumor characteristics, glymphatic function, and aquaporin-4 (AQP4) expression levels. In a prospective cohort from March 2022 to April 2023, patients with glioblastoma, brain metastases, and aggressive meningiomas, alongside age- and sex-matched healthy controls, underwent 3.0T MRI, including diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index and multiparametric MRI for quantitative brain mapping. Tumor and peritumor tissues were analyzed for AQP4 expression levels via immunofluorescence. Correlations among MRI parameters, glymphatic function (DTI-ALPS index), and AQP4 expression levels were statistically assessed. Among 84 patients (mean age: 55 ± 12 years; 38 males) and 59 controls (mean age: 54 ± 8 years; 23 males), patients with brain tumor exhibited significantly reduced glymphatic function (DTI-ALPS index: 2.315 vs. 2.879; P = 0.001) and increased cerebrospinal fluid volume (201.376 cm³ vs. 115.957 cm³; P = 0.001). A negative correlation was observed between tumor volume and the DTI-ALPS index (r: -0.715, P < 0.001), whereas AQP4 expression levels correlated positively with peritumoral brain edema volume (r: 0.989, P < 0.001) and negatively with proton density in peritumoral brain edema areas (ρ: -0.506, P < 0.001). Our findings highlight the interplay among tumor-induced compression, glymphatic dysfunction, and altered fluid dynamics, demonstrating the utility of DTI-ALPS and multiparametric MRI in understanding the pathophysiology of tumor-related cerebral edema. These insights provide a radiological foundation for further neuro-oncological investigations into the glymphatic system. See related commentary by Surov and Borggrefe, p. 4813.

The apparent diffusion coefficient can serve as a predictor of survival in patients with gliomas

Background and purposeMagnetic resonance imaging is indispensable for the preoperative diagnosis of glioma. This study aimed to investigate the role of the apparent diffusion coefficient values as predictors of survival in patients with gliomas.Methods and materialsA retrospective analysis was conducted on 101 patients with gliomas who underwent surgery between 2015 and 2020. Diffusion-weighted MRI was performed before the surgery. The regions of interest were categorized into parenchymal area, non-enhancing peritumoral area, and necrotic or cystic area. All the patients were divided into three subgroups: the parenchyma group, the non-enhancing peritumoral signal abnormality group, and the necrosis or cyst group. Univariate and multivariate analyses were performed using COX regression.ResultsIn the parenchymal group, Ki67, P53, IDH, and the high or low ADC values were identified as independent prognosticators for disease-free survival, while Ki67, IDH, and the high or low ADC values for overall survival. In the non-enhancing peritumoral signal abnormality group, Ki67, P53, IDH, and the ADC parenchymal area/ADC non−enhancing peritumoral area ratio were identified as independent prognostic factors for disease-free survival, while Ki67, IDH, and the ADC parenchymal area/ADC non−enhancing peritumoral area ratio for overall survival. In the necrosis or cyst group, Ki67 was significantly associated with disease-free survival, while Ki67 and the ADC value of the necrotic or cystic area for overall survival.ConclusionsThe ADC values, including the ADC value in the parenchymal area, the ADC parenchymal area/ADC non−enhancing peritumoral area ratio, and the ADC value in the necrotic or cystic area, can serve as an efficient and potential index for predicting the survival of patients with glioma.

Prognostic Significance of Angiogenesis in invasive ductal carcinoma

Background: Angiogenesis is essential for tumor growth and metastasis. Axillary lymphnode status has been the most important prognostic factor in operable breast carcinoma, but it does not fully account for the varied disease outcome. More accurate prognostic indicators would help in selection of patients at high risk for disease recurrence and death who are candidates for systemic adjuvant therapy. Microvessel density in invasive ductal carcinoma (measures of tumor angiogenesis) is associated with metastasis and thus may be a prognostic indicator. Objective: To correlate intratumoral and peritumoral angiogenic microvessel density with lymphnode metastasis in invasive ductal carcinoma. Material and Methods: It is a cross sectional observational study, carried out at the department of Pathology, BSMMU from January 2016 to December 2017. A total 48 mastectomy samples with axillary nodes from histologically confirmed invasive ductal carcinoma were included in this study. Weidner method was used for calculating micro vessels density. Sections examined to evaluate the density of angiogenic vessels by immuohistological stain with vWF expression in invasive breast cancer. Correlation between angiogenic vessels density with or without lymphnode metastasis was taken. Results: In this study angiogenic vessel count is more in the intratumoral area than peritumoural area. There was a positive significant correlation between lymphnode metastasis with micro vessel density in both peritumoral and intratumoral areas in Weidner method in vWF stain. J Shaheed Suhrawardy Med Coll 2023; 15(1): 59-65

Siglec-15 expression in diffuse gliomas and its correlation with MRI morphologic features and apparent diffusion coefficient.

Sialic acid-binding immunoglobulin-like lectin 15 (Siglec-15) enhances tumor immune escape and leads to tumor growth. To investigate the expression of Siglec-15 in diffuse gliomas and its correlation with tumor magnetic resonance imaging (MRI) features. This study included 57 patients with gliomas. Morphological MRI features, including the largest tumor diameter, enhancement category, location, calcification, cysts, and hemorrhage, were visually rated. Apparent diffusion coefficient (ADC) values were calculated in tumor region. MRI morphologic features and ADC were compared between patients with positive and negative Siglec-15 expression. Receiver operating characteristic (ROC) curves were further constructed to assess the diagnostic performance. Siglec-15 was expressed in immunocytes, such as macrophages in the peritumoral area. Siglec-15 expression was positive in 20/57 (35.09%) patients, with higher expression in patients with IDH-mutant gliomas and lower grade gliomas. The tumor diameter was significantly smaller in patients with positive Siglec-15 expression than in those with negative expression for all patients (P = 0.017) and for patients with IDH-mutant gliomas (P = 0.020). Moreover, ADC values of the tumor were significantly higher in patients with positive Siglec-15 expression than in those with negative expression for all patients (P = 0.027). The areas under the ROC curve (AUCs) of the diameter and ADC were 0.702 and 0.686, respectively. A combination of these two parameters generated an improved AUC of 0.762. Siglec-15 was expressed in immunocytes such as macrophages in the peritumoral area, with a positive rate of 35.09%. Positive Siglec-15 expression in diffuse gliomas was correlated with smaller tumor size and higher ADC values.

Differentiation between high-grade gliomas and solitary brain metastases based on multidiffusion MRI model quantitative analysis.

Differentiating high-grade gliomas (HGGs) from solitary brain metastases (SBMs) using conventional magnetic resonance imaging (MRI) remains challenging due to their similar imaging features. This study aimed to evaluate the diagnostic performance of advanced diffusion models, such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator magnetic resonance imaging (MAP-MRI), incomparison to traditional techniques like diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI) for distinguishing HGGs from SBMs. In total, 17 patients with HGGs and 26 patients with SBMs were prospectively recruited based on the established inclusion and exclusion criteria. Structural MRI sequences and diffusion spectrum imaging (DSI) were utilized to assess quantitative parameter models, including NODDI, MAP-MRI, DWI, DTI, and DKI. Quantitative parameters were measured for both the tumor parenchymal area and the peritumoral edema area. The quantitative parameters of the two patient groups were compared using either the independent Student's t-test or the Mann-Whitney U test. The effectiveness of each model was evaluated using receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Finally, the DeLong test was employed to compare the diagnostic performance of each model through pairwise comparisons of ROC curves. Isotropic volume fraction (Viso) based on NODDI; mean squared displacement (MSD) and the return to plane probabilities (RTPP) based on MAP-MRI; radial diffusivity (RDk) and mean diffusivity (MDk) based on DKI; and axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) based on DTI of the peritumoral edema tumor were significantly different between HGGs and SBMs (p < 0.05). The optimal single discriminant parameters for each model are NODDI_Viso, MAP-MRI_MSD, DKI_MDk, and DTI_AD. Among these, the AUC of Viso (0.809) exceeds that of MSD (0.733), MDk (0.718), and AD (0.779). The combined model, which incorporates DTI_AD, DKI_RD, and NODDI_Viso, demonstrated superior diagnostic performance (0.897). Advanced diffusion MRI quantitative parameters derived from NODDI, such as Viso, have the potential to enhance the differentiation between HGGs and SBMs. The integrated utilization of these models is anticipated to enhance diagnostic accuracy and refine MRI protocols for brain tumor assessment.

Integrating intratumoral and peritumoral radiomics with clinical risk factors for prognostic prediction in pancreatic ductal adenocarcinoma patients undergoing combined chemotherapy and HIFU ablation

Objective A radiomics nomogram will be created utilizing MRI data from intratumoral and peritumoral areas to forecast survival outcomes in patients who have had treatment for pancreatic ductal adenocarcinoma (PDAC). Methods A total of 87 individuals diagnosed with PDAC were included in the study, with 60 patients in the training cohort and 27 patients in the validation cohort. A grand total of 2395 radiomics characteristics were extracted from the tumor region and the peritumoral region. The least absolute shrinkage and selection operator (LASSO) method was used to select features and create a radiomics score, also known as the Rad-score. A multivariate regression analysis was then conducted to build the radiomics nomogram. The evaluation of the nomogram included discrimination, calibration, and clinical utility assessments. Results Based on the conclusions derived from the multivariate Cox model, Rad-Score, jaundice, and tumor size were identified as independent risk factors for overall survival (OS). The inclusion of the Rad-score in the radiomics nomogram led to improved accuracy in predicting survival compared to the clinical model. Patients were categorized into high-risk and low-risk groups based on their Rad-Score. Kaplan-Meier analysis revealed a statistically significant difference between the two groups (p < 0.05). Furthermore, the radiomics nomogram demonstrated excellent ability to differentiate, calibrate, and provide clinical utility in both the training and validation groups. Conclusions The MRI-based intratumoral and peritumoral radiomics nomogram, integrating the Rad-score and clinical data, provided better prognostic prediction for PDAC patients after HIFU treatment, which may hold great potential for guiding personalized care for these patients.

Machine learning radiomics based on intra and peri tumor PA/US images distinguish between luminal and non-luminal tumors in breast cancers

PurposeThis study aimed to evaluate a radiomics model using Photoacoustic/ultrasound (PA/US) imaging at intra and peri-tumoral area to differentiate Luminal and non-Luminal breast cancer (BC) and to determine the optimal peritumoral area for accurate classification. Materials and methodsFrom February 2022 to April 2024, this study continuously collected 322 patients at Shenzhen People’s Hospital, using standardized conditions for PA/US imaging of BC. Regions of interest were delineated using ITK-SNAP, with peritumoral regions of 2 mm, 4 mm, and 6 mm automatically expanded using code from the Pyradiomic package. Feature extraction was subsequently performed using Pyradiomics. The study employed Z-score normalization, Spearman correlation for feature correlation, and LASSO regression for feature selection, validated through 10-fold cross-validation. The radiomics model integrated intra and peri-tumoral area, evaluated by receiver operating characteristic curve(ROC), Calibration and Decision Curve Analysis(DCA). ResultsWe extracted and selected features from intratumoral and peritumoral PA/US images regions at 2 mm, 4 mm, and 6 mm. The comprehensive radiomics model, integrating these regions, demonstrated enhanced diagnostic performance, especially the 4 mm model which showed the highest area under the curve(AUC):0.898(0.78–1.00) and comparably high accuracy (0.900) and sensitivity (0.937). This model outperformed the standalone clinical model and combined clinical-radiomics model in distinguishing between Luminal and non-Luminal BC, as evidenced in the test set results. ConclusionThis study developed a radiomics model integrating intratumoral and peritumoral at 4 mm region PA/US model, enhancing the differentiation of Luminal from non-Luminal BC. It demonstrated the diagnostic utility of peritumoral characteristics, reducing the need for invasive biopsies and aiding chemotherapy planning, while emphasizing the importance of optimizing tumor surrounding size for improved model accuracy.

Utilizing Radiomics of Peri-Lesional Edema in T2-FLAIR Subtraction Digital Images to Distinguish High-Grade Glial Tumors From Brain Metastasis.

Differentiating high-grade glioma (HGG) and isolated brain metastasis (BM) is important for determining appropriate treatment. Radiomics, utilizing quantitative imaging features, offers the potential for improved diagnostic accuracy in this context. To differentiate high-grade (grade 4) glioma and BM using machine learning models from radiomics data obtained from T2-FLAIR digital subtraction images and the peritumoral edema area. Retrospective. The study included 1287 patients. Of these, 602 were male and 685 were female. Of the 788 HGG patients included in the study, 702 had solitary masses. Of the 499 BM patients included in the study, 112 had solitary masses. Initially, the model was developed and tested on solitary masses. Subsequently, the model was developed and tested separately for all patients (solitary and multiple masses). Axial T2-weighted fast spin-echo sequence (T2WI) and T2-weighted fluid-attenuated inversion recovery sequence (T2-FLAIR), using 1.5-T and 3.0-T scanners. Radiomic features were extracted from digitally subtracted T2-FLAIR images in the area of peritumoral edema. The maximum relevance-minimum redundancy (mRMR) method was then used for dimensionality reduction. The naive Bayes algorithm was used in model development. The interpretability of the model was explored using SHapley Additive exPlanations (SHAP). Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. The performance metrics include area under curve (AUC), sensitivity (SENS), and specificity (SPEC). The mean age of HGG patients was 61.4 ± 13.2 years and 61.7 ± 12.2 years for BM patients. In the external validation cohort, the model achieved AUC: 0.991, SENS: 0.983, and SPEC: 0.922. The external cohort results for patients with solitary lesions were AUC: 0.987, SENS: 0.950, and SPEC: 0.922. The artificial intelligence model, developed with radiomics data from the peritumoral edema area in T2-FLAIR digital subtraction images, might be able to differentiate isolated BM from HGG. 3 TECHNICAL EFFICACY: Stage 2.

Distribution characteristics of immune infiltration and lymphovascular invasion in patients with breast cancer skin recurrence

BackgroundTo assess the distribution characteristics of immune infiltration and lymphovascular invasion in breast cancer skin recurrence patients.MethodsWe retrospectively analyzed the clinicopathological data of patients who underwent radical surgery for primary breast cancer and experienced skin recurrence between January 2001 and April 2019. Immune and lymphovascular biomarkers were quantified in primary breast cancers, skin lesions and visceral metastatic lesions. Differences in biomarkers distribution between matched tissues were statistically analyzed using the Wilcoxon signed-rank test and Kruskal–Wallis one-way ANOVA.ResultsA total of 71 female breast cancer patients were reviewed in this study. Our study found that the expression levels of various lymphocyte immune markers in primary tumor specimens were higher than those in skin recurrences. The expression of CD8, CD57 and CD31 in primary breast cancer was higher than those in the skin. Compared to visceral metastatic lesions, D2-40 was highly expressed in the skin, while CD8 tended to decrease. In the skin specimens, the expression of CD8 (P < 0.001), FOXP3 (P = 0.006) and CD68 (P < 0.001) in the intratumoral area was higher, while the expression of CD57 (P < 0.001) was higher in the peritumoral area. Analyzing specimens from the same patient at different time points of skin progression, it was found that the expression of peritumoral CD4 decreased (P = 0.044) as the disease progressed. The low expression of D2-40 and CD163 in the skin lesions suggested a decrease in DFS.ConclusionThe immune microenvironment of breast cancer skin recurrence may be in a state of suppression, and this suppression may intensify with disease progression. The pattern of skin recurrence may be more inclined toward lymphatic invasion. Our study provides new insights into the biological behaviors of this disease and its response to immunotherapy.

Multiparametric MRI-Based Deep Learning Radiomics Model for Assessing 5-Year Recurrence Risk in Non-Muscle Invasive Bladder Cancer.

Accurately assessing 5-year recurrence rates is crucial for managing non-muscle-invasive bladder carcinoma (NMIBC). However, the European Organization for Research and Treatment of Cancer (EORTC) model exhibits poor performance. To investigate whether integrating multiparametric MRI (mp-MRI) with clinical factors improves NMIBC 5-year recurrence risk assessment. Retrospective. One hundred ninety-one patients (median age, 65 years; age range, 54-73 years; 27 females) underwent mp-MRI between 2011 and 2017, and received ≥5-year follow-ups. They were divided into a training cohort (N = 115) and validation/testing cohorts (N = 38 in each). Recurrence rates were 23.5% (27/115) in the training cohort and 23.7% (9/38) in both validation and testing cohorts. 3-T, fast spin echo T2-weighted imaging (T2WI), single-shot echo planar diffusion-weighted imaging (DWI), and volumetric spoiled gradient echo dynamic contrast-enhanced (DCE) sequences. Radiomics and deep learning (DL) features were extracted from the combined region of interest (cROI) including intratumoral and peritumoral areas on mp-MRI. Four models were developed, including clinical, cROI-based radiomics, DL, and clinical-radiomics-DL (CRDL) models. Student's t-tests, DeLong's tests with Bonferroni correction, receiver operating characteristics with the area under the curves (AUCs), Cox proportional hazard analyses, Kaplan-Meier plots, SHapley Additive ExPlanations (SHAP) values, and Akaike information criterion for clinical usefulness. A P-value <0.05 was considered statistically significant. The cROI-based CRDL model showed superior performance (AUC 0.909; 95% CI: 0.792-0.985) compared to other models in the testing cohort for assessing 5-year recurrence in NMIBC. It achieved the highest Harrell's concordance index (0.804; 95% CI: 0.749-0.859) for estimating recurrence-free survival. SHAP analysis further highlighted the substantial role (22%) of the radiomics features in NMIBC recurrence assessment. Integrating cROI-based radiomics and DL features from preoperative mp-MRI with clinical factors could improve 5-year recurrence risk assessment in NMIBC. 3 TECHNICAL EFFICACY: Stage 3.

Shear-Wave Elastography Gradient Analysis of Newly Diagnosed Breast Tumours: A Critical Analysis.

A better understanding of the peritumoral stroma changes due to tumour invasion using non-invasive diagnostic methods may improve the differentiation between benign and malignant breast lesions. This study aimed to assess the correlation between breast lesion differentiation and intra- and peritumoral shear-wave elastography (SWE) gradients. A total of 135 patients with newly diagnosed breast lesions were included. Intratumoral, subsurface, and three consecutive peritumoral SWE value measurements (with three repetitions) were performed. Intratumoral, interface, and peritumoral gradients (Gradient 1 and Gradient 2) were calculated using averaged SWE values. Statistical analysis included descriptive statistics and an ordinary one-way ANOVA to compare overall and individual gradients among Breast Imaging-Reporting and Data System (BI-RADS) 2, 3, and 5 groups. Malignant tumours showed higher average SWE velocity values at the tumour centre (BI-RADS 2/3: 4.1 ± 1.8 m/s vs. BI-RADS 5: 4.9 ± 2.0 m/s, p = 0.04) and the first peritumoral area (BI-RADS 2/3: 3.4 ± 1.8 m/s vs. BI-RADS 5: 4.3 ± 1.8 m/s, p = 0.003). No significant difference was found between intratumoral gradients (0.03 ± 0.32 m/s vs. 0.0 ± 0.28 m/s; p > 0.999) or gradients across the tumour-tissue interface (-0.17 ± 0.18 m/s vs. -0.13 ± 0.35 m/s; p = 0.202). However, the first peritumoral gradient (-0.16 ± 0.24 m/s vs. -0.35 ± 0.31 m/s; p < 0.0001) and the second peritumoral gradient (-0.11 ± 0.18 m/s vs. -0.22 ± 0.28 m/s; p = 0.037) were significantly steeper in malignant tumours. The AUC was best for PTG1 (0.7358) and PTG2 (0.7039). A threshold value for peritumoral SWI PT1 above 3.76 m/s and for PTG1 below -0.238 m/s·mm-1 indicated malignancy in 90.6% of cases. Evaluating the peritumoral SWE gradient may improve the diagnostic pre-test probability, as malignant tumours showed a significantly steeper curve of the elasticity values in the peritumoral stroma compared to the linear regression with a relatively flat curve of benign lesions.

CX 3 CR1 + macrophages interact with HSCs to promote HCC through CD8 + T-cell suppression.

HSCs contribute to HCC progression by regulating multiple factors. However, the entire immunoregulatory functions of HSCs are still obscure. Here, we aim to investigate whether HSCs impose CX 3 CR1 + macrophages to protumorigenic properties in the peritumoral area. In single-cell RNA-sequencing analysis of patients with HCC, a subpopulation of macrophages specifically expressed Arg1 and Cx3cr1 in the peritumoral area and were highly enriched with retinol metabolism-related genes. Flow cytometry analysis showed significantly increased frequencies of CD14 + CD11b + HLA-DR - macrophages with CX 3 CR1 in the HCC adjacent region where α-smooth muscle actin-expressing activated hepatic stellate cells (aHSCs) showed colocalized expression of CX 3 CL1. Accordingly, in tumor-bearing mice, Cx3cl1 mRNA expression was notably increased in aHSCs within the adjacent HCC, where infiltration of CX 3 CR1 + Ly6C + macrophages was mostly observed with decreased CD8 + T cells. In adoptive transfer and in vitro coculture of myeloid cells, we demonstrated that CX 3 CR1 + Ly6C + macrophages migrated and highly expressed arginase-1 by interacting with retinoid-enriched aHSCs in the adjacent HCC. Direct treatment of retinoids or coculturing with retinol-storing mouse aHSCs or human LX-2 cells significantly increased arginase-1 expression in CX 3 CR1 + Ly6C + macrophages and human blood CD14 + cells, leading to the suppression of CD8 + T-cell proliferation. Moreover, genetic deficiency of CX 3 CR1 in myeloid cells or pharmacological inhibition of retinol metabolism remarkably attenuated HCC development. We showed that CX 3 CR1 + Ly6C + macrophages migrate and interact with aHSCs in the peritumoral region where retinoids induce arginase-1 expression in CX 3 CR1 + Ly6C + macrophages, subsequently depriving CD8 + T cells of arginine and promoting HCC.

Changes in the Expression of Genes Regulating the Response to Hypoxia, Inflammation, Cell Cycle, Apoptosis, and Epithelial Barrier Functioning during Colitis-Associated Colorectal Cancer Depend on Individual Hypoxia Tolerance.

One of the factors contributing to colorectal cancer (CRC) development is inflammation, which is mostly hypoxia-associated. This study aimed to characterize the morphological and molecular biological features of colon tumors in mice that were tolerant and susceptible to hypoxia based on colitis-associated CRC (CAC). Hypoxia tolerance was assessed through a gasping time evaluation in a decompression chamber. One month later, the animals were experimentally modeled for colitis-associated CRC by intraperitoneal azoxymethane administration and three dextran sulfate sodium consumption cycles. The incidence of tumor development in the distal colon in the susceptible to hypoxia mice was two times higher and all tumors (100%) were represented by adenocarcinomas, while in the tolerant mice, only 14% were adenocarcinomas and 86% were glandular intraepithelial neoplasia. The tumor area assessed on serially stepped sections was statistically significantly higher in the susceptible animals. The number of macrophages, CD3-CD19+, CD3+CD4+, and NK cells in tumors did not differ between animals; however, the number of CD3+CD8+ and vimentin+ cells was higher in the susceptible mice. Changes in the expression of genes regulating the response to hypoxia, inflammation, cell cycle, apoptosis, and epithelial barrier functioning in tumors and the peritumoral area depended on the initial mouse's hypoxia tolerance, which should be taken into account for new CAC diagnostics and treatment approaches development.

Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma

ObjectivesThis study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).MethodsThe study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation cohort (n = 71). An external validation cohort comprising 40 patients from Center B also was included. Computed tomography (CT) radiomics features were extracted from the internal area of the tumor (IAT) and IAT combined peritumoral areas of the tumor at 3 mm (PAT 3 mm) and 5 mm (PAT 5 mm). Independent predictors from both clinical and radiomics scores (Radscore) were used to construct a radiomics nomogram. Kaplan–Meier analysis with a log-rank test was performed to evaluate the correlation between factors and OS.ResultsThe PAT 5-mm radiomics model (RM) exhibited exceptional predictive capability for grading, achieving an area under the curves of 0.80, 0.80, and 0.90 in the training, internal validation, and external validation cohorts. The nomogram and RM gained from the PAT 5-mm region were more clinically useful than the clinical model. The association between OS and predicted nuclear grade derived from the PAT 5-mm Radscore and the nomogram-predicted score was statistically significant (p < 0.05).ConclusionThe CT-based radiomics and nomograms showed valuable predictive capabilities for the World Health Organization/International Society of Urological Pathology grade and OS in patients with ccRCC.Critical relevance statementThe intratumoral and peritumoral radiomics are feasible and promising to predict nuclear grade and overall survival in patients with clear cell renal cell carcinoma, which can contribute to the development of personalized preoperative treatment strategies.Key PointsThe multi-regional radiomics features are associated with clear cell renal cell carcinoma (ccRCC) grading and prognosis.The combination of intratumoral and peritumoral 5 mm regional features demonstrated superior predictive performance for grading.The nomogram and radiomics models have a broad range of clinical applications.Graphical

Probe-guided endoscopic system for 5-aminolevulinic acid-based photodynamic diagnosis in cholangiocarcinoma

Background and AimThe diagnostic accuracy for cholangiocarcinoma (CCA) is inadequate, necessitating the exploration of novel diagnostic approaches. Protoporphyrin IX (Pp IX), a metabolic product of 5-aminolevulinic acid (5-ALA), emits red fluorescence upon blue light exposure. Because it accumulates selectively in cancer cells, photodynamic diagnosis using 5-ALA (5-ALA-PDD) has been integrated into clinical practice for diverse cancer types. Nevertheless, there is currently no device capable of capturing Pp IX-derived fluorescence for real-time 5-ALA-PDD within the biliary tract, largely due to challenges in device miniaturization. MethodsTo investigate the feasibility of real-time 5ALA-PDD in CCA, we developed two essential components of the cholangioscopy system: a small-diameter flexible camera and a light guide for emitting blue light. We evaluated the detectability of Pp IX fluorescence using these devices in experimental gels and animal models. ResultsOur camera and light guide were smoothly inserted into the lumen of existing cholangioscopes. Incorporating a long-pass filter at the camera tip enabled efficient detection of red fluorescence without significantly impacting white-light observation. The integration of these devices facilitated clear visualization of red fluorescence from gels containing Pp IX at concentrations of 5 μM or higher. Additionally, when observing subcutaneous human CCA tumor models in nude mice treated with 5-ALA, we successfully demonstrated distinct red fluorescence from Pp IX accumulation in tumors compared to peritumoral subcutaneous areas. ConclusionThe integration of our device combination holds promise for real-time 5-ALA-PDD in human CCA, potentially enhancing the diagnostic accuracy for this complex condition.

Spatial proteomic landscape of primary and relapsed hepatocellular carcinoma reveals immune escape characteristics in early relapse.

Surgical resection serves as the principal curative strategy for hepatocellular carcinoma (HCC), yet the incidence of postoperative recurrence remains alarmingly high. However, the spatial molecular structural alterations contributing to postoperative recurrence in HCC are still poorly understood. We employed imaging mass cytometry to profile the in-situ expression of 33 proteins within 358,729 single cells of 92 clinically annotated surgical specimens from 46 patients who were treated with surgical resections for primary and relapsed tumors. We revealed the recurrence progression of HCC was governed by the dynamic spatial distribution and functional interplay of diverse cell types across adjacent normal, tumor margin, and intratumor regions. Our exhaustive analyses revealed an aggressive, immunosuppression-related spatial ecosystem in relapsed HCC. Additionally, we illustrated the prominent implications of the TME of tumor margins in association with relapse HCC. Moreover, we identified a novel subpopulation of dendritic cells (PDL1+CD103+ DCs) enriched in the peritumoral area that correlated with early postoperative recurrence, which was further validated in an external cohort. Through the analysis of scRNA-seq data, we found the interaction of PDL1+CD103+ DCs with regulatory T cells and exhausted T cells enhanced immunosuppression and immune escape via multiple ligand-receptor pathways. We comprehensively depicted the spatial landscape of single-cell dynamics and multicellular architecture within primary and relapsed HCC. Our findings highlight spatial organization is a prominent determinant of HCC recurrence and provide a valuable insight into the immune evasion mechanisms driving recurrence.

Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: A multicenter study comparing performance with radiologists

PurposeTo conduct the fusion of radiomics and deep transfer learning features from the intratumoral and peritumoral areas in breast DCE-MRI images to differentiate between benign and malignant breast tumors, and to compare the diagnostic accuracy of this fusion model against the assessments made by experienced radiologists. Materials and MethodsThis multi-center study conducted a retrospective analysis of DCE-MRI images from 330 women diagnosed with breast cancer, with 138 cases categorized as benign and 192 as malignant. The training and internal testing sets comprised 270 patients from center 1, while the external testing cohort consisted of 60 patients from center 2. A fusion feature set consisting of radiomics features and deep transfer learning features was constructed from both intratumoral (ITR) and peritumoral (PTR) areas. The Least absolute shrinkage and selection operator (LASSO) based support vector machine was chosen as the classifier by comparing its performance with five other machine learning models. The diagnostic performance and clinical usefulness of fusion model were verified and assessed through the area under the receiver operating characteristics (ROC) and decision curve analysis. Additionally, the performance of the fusion model was compared with the diagnostic assessments of two experienced radiologists to evaluate its relative accuracy. The study strictly adhered to CLEAR and METRICS guidelines for standardization to ensure rigorous and reproducible methods. ResultsThe findings show that the fusion model, utilizing radiomics and deep transfer learning features from the ITR and PTR, exhibited exceptional performance in classifying breast tumors, achieving AUCs of 0.950 in the internal testing set and 0.921 in the external testing set. This performance significantly surpasses that of models relying on singular regional radiomics or deep transfer learning features alone. Moreover, the fusion model demonstrated superior diagnostic accuracy compared to the evaluations conducted by two experienced radiologists, thereby highlighting its potential to support and enhance clinical decision-making in the differentiation of benign and malignant breast tumors. ConclusionThe fusion model, combining multi-regional radiomics with deep transfer learning features, not only accurately differentiates between benign and malignant breast tumors but also outperforms the diagnostic assessments made by experienced radiologists. This underscores the model’s potential as a valuable tool for improving the accuracy and reliability of breast tumor diagnosis.

Unraveling malignant phenotype of peritumoral tissue: transcriptomic insights into early-stage breast cancer

BackgroundEarly-stage invasive ductal carcinoma displays high survival rates due to early detection and treatments. However, there is still a chance of relapse of 3–15% after treatment. The aim of this study was to uncover the distinctive transcriptomic characteristics and monitoring prognosis potential of peritumoral tissue in early-stage cases.MethodsRNA was isolated from tumoral, peritumoral, and non-tumoral breast tissue from surgical resection of 10 luminal early-stage invasive ductal carcinoma patients. Transcriptome expression profiling for differentially expressed genes (DEGs) identification was carried out through microarray analysis. Gene Ontology and KEGG pathways enrichment analysis were explored for functional characterization of identified DEGs. Protein-Protein Interactions (PPI) networks analysis was performed to identify hub nodes of peritumoral tissue alterations and correlated with Overall Survival and Relapse Free Survival.ResultsDEGs closely related with cell migration, extracellular matrix organization, and cell cycle were upregulated in peritumoral tissue compared to non-tumoral. Analyzing PPI networks, we observed that the proximity to tumor leads to the alteration of gene modules involved in cell proliferation and differentiation signaling pathways. In fact, in the peritumoral area were identified the top ten upregulated hub nodes including CDK1, ESR1, NOP58, PCNA, EZH2, PPP1CA, BUB1, TGFBR1, CXCR4, and CCND1. A signature performed by four of these hub nodes (CDK1, PCNA, EZH2, and BUB1) was associated with relapse events in untreated luminal breast cancer patients.ConclusionsIn conclusion, our study characterizes in depth breast peritumoral tissue providing clues on the changes that tumor signaling could cause in patients with early-stage breast cancer. We propose that the use of a four gene signature could help to predict local relapse. Overall, our results highlight the value of peritumoral tissue as a potential source of new biomarkers for early detection of relapse and improvement in invasive ductal carcinoma patient’s prognosis.

Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis.

This study seeks to construct a machine learning model that merges clinical characteristics with ultrasound radiomic analysis-encompassing both the intratumoral and peritumoral-to predict the status of axillary lymph nodes in patients with early-stage breast cancer. The study employed retrospective methods, collecting clinical information, ultrasound data, and postoperative pathological results from 321 breast cancer patients (including 224 in the training group and 97 in the validation group). Through correlation analysis, univariate analysis, and Lasso regression analysis, independent risk factors related to axillary lymph node metastasis in breast cancer were identified from conventional ultrasound and immunohistochemical indicators, and a clinical feature model was constructed. Additionally, features were extracted from ultrasound images of the intratumoral and its 1-5 mm peritumoral to establish a radiomics feature formula. Furthermore, by combining clinical features and ultrasound radiomics features, six machine learning models (Logistic Regression, Decision Tree, Support Vector Machine, Extreme Gradient Boosting, Random Forest, and K-Nearest Neighbors) were compared for diagnostic efficacy, and constructing a joint prediction model based on the optimal ML algorithm. The use of Shapley Additive Explanations (SHAP) enhanced the visualization and interpretability of the model during the diagnostic process. Among the 321 breast cancer patients, 121 had axillary lymph node metastasis, and 200 did not. The clinical feature model had an AUC of 0.779 and 0.777 in the training and validation groups, respectively. Radiomics model analysis showed that the model including the Intratumor +3 mm peritumor area had the best diagnostic performance, with AUCs of 0.847 and 0.844 in the training and validation groups, respectively. The joint prediction model based on the XGBoost algorithm reached AUCs of 0.917 and 0.905 in the training and validation groups, respectively. SHAP analysis indicated that the Rad Score had the highest weight in the prediction model, playing a significant role in predicting axillary lymph node metastasis in breast cancer. The predictive model, which integrates clinical features and radiomic characteristics using the XGBoost algorithm, demonstrates significant diagnostic value for axillary lymph node metastasis in breast cancer. This model can provide significant references for preoperative surgical strategy selection and prognosis evaluation for breast cancer patients, helping to reduce postoperative complications and improve long-term survival rates. Additionally, the utilization of SHAP enhancing the global and local interpretability of the model.