Effect of Cyclic Ice Plug Deformation on Microstructure and Mechanical Behaviors of Nuclear-Grade Low-Carbon Tubular Steel.

This study subjected nuclear-grade 20# pipeline steel to cyclic freeze-thaw ice plugging tests, simulating the plastic deformation experienced by pipes during ice plug removal procedures. Subsequently, the dislocation morphology and mechanical properties of the specimens post cyclic ice plugging were examined. The cyclic ice plugging process led to an increase in the dislocation density within the specimens. After 20 and 40 cycles of ice plugging, the internal dislocation structures evolved from individual dislocation lines and dislocation tangles to high-density dislocation walls and dislocation cells. These high-density dislocation walls and cells hindered dislocation motion, giving rise to strain hardening phenomena, thereby resulting in increased strength and hardness of the specimens with an increasing number of ice plugging cycles. In addition, a large stress field was generated around the dislocation buildup, which reduced the pipe material's plastic toughness. The findings elucidate the effects of cyclic ice plugging on the microstructure and properties of nuclear-grade 20# pipeline steel, aiming to provide a theoretical basis for the safe and stable application of ice plugging technology in nuclear piping systems.

Multiple metastases of human epidermal growth factor receptor 2­positive, hormone receptor­positive, pT1a pN0 breast cancer within 1 year after surgery: A case report.

Adjuvant chemotherapy is usually not considered for pT1a pN0 human epidermal growth factor receptor 2 (HER2)-positive breast cancer due to its low recurrence rate. The present report describes a case of pT1a hormone receptor-positive HER2-positive breast cancer with multiple recurrences in the axillary lymph nodes and liver within 1 year after radical surgery. A 58-year-old woman underwent left total mastectomy and sentinel lymph node biopsy for left breast cancer with pathological stage IA (pT1a pN0). The subtype corresponded to luminal B-like breast cancer with a nuclear grade of 3 and a Ki-67 labeling index of 37%. An aromatase inhibitor (letrozole) was planned to be administered for 5 years after surgery, but the patient was diagnosed with multiple liver and axillary lymph node metastases 11 months after surgery. After 1 year of chemotherapy (paclitaxel) in combination with anti-HER2 therapy (pertuzumab and trastuzumab), liver metastases resolved. A complete response of the liver lesion has been maintained 4 years after the anti-HER2 therapy initiation. The present case exhibited two poor prognostic factors: High Ki-67 labeling index and nuclear grade 3. Based on the 'Predict' tool, the present case would be expected to have a cancer-related mortality rate of 6% 10 years after surgery with adjuvant endocrine therapy. Although this value may be controversial for postoperative anti-HER2 therapy, the present case should not be considered to be a low-risk case. When the identification of high-risk pT1a pN0 HER2-positive breast cancer is possible, postoperative anti-HER2 therapy plus chemotherapy would be effective in decreasing the rate of recurrence.

Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma in situ.

Background: Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular markers, which is also predictive of different outcomes and risks of recurrence. DCIS ultrasound findings are mostly non mass lesions, making it difficult to determine boundaries. Currently, studies have shown that models based on deep learning radiomics (DLR) have advantages in automatic recognition of tumor contours. Machine learning models based on clinical imaging features can explain the importance of imaging features. Methods: The available ultrasound data of 349 patients with pure DCIS confirmed by surgical pathology [54 low nuclear grade, 175 positive estrogen receptor (ER+), 163 positive progesterone receptor (PR+), and 81 positive human epidermal growth factor receptor 2 (HER2+)] were collected. Radiologists extracted ultrasonographic features of DCIS lesions based on the 5th Edition of Breast Imaging Reporting and Data System (BI-RADS). Patient age and BI-RADS characteristics were used to construct clinical machine learning (CML) models. The RadImageNet pretrained network was used for extracting radiomics features and as an input for DLR modeling. For training and validation datasets, 80% and 20% of the data, respectively, were used. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms were performed and compared for the final classification modeling. Each task used the area under the receiver operating characteristic curve (AUC) to evaluate the effectiveness of DLR and CML models. Results: In the training dataset, low nuclear grade, ER+, PR+, and HER2+ DCIS lesions accounted for 19.20%, 65.12%, 61.21%, and 30.19%, respectively; the validation set, they consisted of 19.30%, 62.50%, 57.14%, and 30.91%, respectively. In the DLR models we developed, the best AUC values for identifying features were 0.633 for identifying low nuclear grade, completed by the XGBoost Classifier of ResNet50; 0.618 for identifying ER, completed by the RF Classifier of InceptionV3; 0.755 for identifying PR, completed by the XGBoost Classifier of InceptionV3; and 0.713 for identifying HER2, completed by the LR Classifier of ResNet50. The CML models had better performance than DLR in predicting low nuclear grade, ER+, PR+, and HER2+ DCIS lesions. The best AUC values by classification were as follows: for low nuclear grade by RF classification, AUC: 0.719; for ER+ by XGBoost classification, AUC: 0.761; for PR+ by XGBoost classification, AUC: 0.780; and for HER2+ by RF classification, AUC: 0.723. Conclusions: Based on small-scale datasets, our study showed that the DLR models developed using RadImageNet pretrained network and CML models may help predict low nuclear grade, ER+, PR+, and HER2+ DCIS lesions so that patients benefit from hierarchical and personalized treatment.

Improvement in Grain Size Distribution Uniformity for Nuclear-Grade Austenitic Stainless Steel through Thermomechanical Treatment.

In this work, thermomechanical treatment (single-pass rolling at 800 °C and solution treatment) was applied to nuclear-grade hot-rolled austenitic stainless steel to eliminate the mixed grain induced by the uneven hot-rolled microstructure. By employing high-temperature laser scanning confocal microscopy, microstructure evolution during solution treatment was observed in situ, and the effect of single-pass rolling reduction on it was investigated. In uneven hot-rolled microstructure, the millimeter-grade elongated grains (MEGs) possessed an extremely large size and a high Schmid factor for slip compared to the fine grains, which led to greater plastic deformation and increased dislocation density and deformation energy storage during single-pass rolling. During subsequent solution treatment, there were fewer nucleation sites for the new grain, and the grain boundary (GB) was the main nucleation site in MEGs at a lower rolling reduction. In contrast, at a higher reduction, increased uniformly distributed rolling deformation and more nucleation sites were developed in MEGs. As the reduction increased, the number of in-grain nucleation sites gradually exceeded that of GB nucleation sites, and in-grain nucleation preferentially occurred. This was beneficial for promoting the refinement of new recrystallized grains and a reduction in the size difference of new grains during recrystallization. The single-pass rolling reduction of 15-20% can effectively increase the nucleation sites and improve the uniformity of rolling deformation distribution in the MEGs, promote in-grain nucleation, and finally refine the abnormally coarse elongated grain, and eliminate the mixed-grain structure after solution treatment.

Multiphase comparative study for WHO/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma.

To compare and analyze the diagnostic value of different enhancement stages in distinguishing low and high nuclear grade clear cell renal cell carcinoma (ccRCC) based on enhanced computed tomography (CT) images by building machine learning classifiers. A total of 51 patients (Dateset1, including 41 low-grade and 10 high-grade) and 27 patients (Independent Dateset2, including 16 low-grade and 11 high-grade) with pathologically proven ccRCC were enrolled in this retrospective study. Radiomic features were extracted from the corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP) CT images, and selected using the recursive feature elimination cross-validation (RFECV) algorithm, the group differences were assessed using T-test and Mann-Whitney U test for continuous variables. The support vector machine (SVM), random forest (RF), XGBoost (XGB), VGG11, ResNet18, and GoogLeNet classifiers are established to distinguish low-grade and high-grade ccRCC. The classifiers based on CT images of NP (Dateset1, RF: AUC = 0.82 ± 0.05, ResNet18: AUC = 0.81 ± 0.02; Dateset2, XGB: AUC = 0.95 ± 0.02, ResNet18: AUC = 0.87 ± 0.07) obtained the best performance and robustness in distinguishing low-grade and high-grade ccRCC, while the EP-based classifier performance in poorer results. The CT images of enhanced phase NP had the best performance in diagnosing low and high nuclear grade ccRCC. Firstorder_Kurtosis and firstorder_90Percentile feature play a vital role in the classification task.

Deep learning using contrast-enhanced ultrasound images to predict the nuclear grade of clear cell renal cell carcinoma.

PURPOSE: To assess the effectiveness of a deep learning model using contrastenhanced ultrasound (CEUS) images in distinguishing between low-grade (grade I and II) and high-grade (grade III and IV) clear cell renal cell carcinoma (ccRCC). METHODS: A retrospective study was conducted using CEUS images of 177 Fuhrmangraded ccRCCs (93 low-grade and 84 high-grade) from May 2017 to December 2020. A total of 6412 CEUS images were captured from the videos and normalized for subsequent analysis. A deep learning model using the RepVGG architecture was proposed to differentiate between low-grade and high-grade ccRCC. The model's performance was evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Class activation mapping (CAM) was used to visualize the specific areas that contribute to the model's predictions. RESULTS: For discriminating high-grade ccRCC from low-grade, the deep learning model achieved a sensitivity of 74.8%, specificity of 79.1%, accuracy of 77.0%, and an AUC of 0.852 in the test set. CONCLUSION: The deep learning model based on CEUS images can accurately differentiate between low-grade and high-grade ccRCC in a non-invasive manner.

Patients with high nuclear grade pT1-ccRCC are more suitable for radical nephrectomy than partial nephrectomy: a multicenter retrospective study using propensity score.

BACKGROUND: Partial nephrectomy (PN) is usually recommended for T1 stage clear cell renal cell carcinoma (ccRCC) regardless of the nuclear grades. However, the question remains unresolved as to whether PN is non-inferior to RN in patients with T1-ccRCC at higher risk of recurrence. In fact, we found that patients with high nuclear grades treated with PN had poorer prognosis compared with those treated with radical nephrectomy (RN). Therefore, this study was designed to evaluate the associations of PN and RN in the four nuclear grade subsets with oncologic outcomes. METHODS: A retrospective study was conducted in three Chinese urological centers that included 1,714 patients who underwent PN or RN for sporadic, unilateral, pT1, N0, and M0 ccRCC without positive surgical margins and neoadjuvant therapy between 2010 and 2019. Associations of nephrectomy type with local ipsilateral recurrence, distant metastases, and all-cause mortality (ACM) were evaluated using the Kaplan-Meier method and multivariable Cox proportional hazards regression models after overlap weighting (OW). RESULTS: A total of 1675 patients entered the OW cohort. After OW, in comparison to PN, RN associated with a reduced risk of local ipsilateral recurrence in the G2 subset (HR = 0.148, 95% CI 0.046-0.474; p < 0.05), G3 subset (HR = 0.097, 95% CI 0.021-0.455; p < 0.05), and G4 subset (HR = 0.091, 95% CI 0.011-0.736; p < 0.05), and resulting in increased five-year local recurrence-free survival rates of 7.0%, 17.9%, and 36.2%, respectively. An association between RN and a reduced risk of distant metastases in the G4 subset (HR = 0.071, 95% CI 0.016-0.325; p < 0.05), with the five-year distant metastases-free survival rate increasing by 33.1% was also observed. No significant difference in ACM between PN and RN was identified. CONCLUSIONS: Our findings substantiate that opting for RN, as opposed to PN, is more advantageous for local recurrence-free survival and distant metastases-free survival in patients with high nuclear grade (especially G4) pT1-ccRCC. We recommend placing a heightened emphasis on enhancing preoperative nuclear grade assessment, as it can significantly influence the choice of surgical plan. TRIAL REGISTRATION: This study was registered at Chinese Clinical Trial Registry (ID: ChiCTR2200063333).

Sonography-based multimodal information platform for identifying the surgical pathology of ductal carcinoma in situ.

BACKGROUND: The risk of ductal carcinoma in situ (DCIS) identified by biopsy often increases during surgery. Therefore, confirming the DCIS grade preoperatively is necessary for clinical decision-making. PURPOSE: To train a three-classification deep learning (DL) model based on ultrasound (US), combining clinical data, mammography (MG), US, and core needle biopsy (CNB) pathology to predict low-grade DCIS, intermediate-to-high-grade DCIS, and upstaged DCIS. MATERIALS AND METHODS: Data of 733 patients with 754 DCIS cases confirmed by biopsy were retrospectively collected from May 2013 to June 2022 (N1), and other data (N2) were confirmed by biopsy as low-grade DCIS. The lesions were randomly divided into training (n=471), validation (n=142), and test (n = 141) sets to establish the DCIS-Net. Information on the DCIS-Net, clinical (age and sign), US (size, calcifications, type, breast imaging reporting and data system [BI-RADS]), MG (microcalcifications, BI-RADS), and CNB pathology (nuclear grade, architectural features, and immunohistochemistry) were collected. Logistic regression and random forest analyses were conducted to develop Multimodal DCIS-Net to calculate the specificity, sensitivity, accuracy, receiver operating characteristic curve, and area under the curve (AUC). RESULTS: In the test set of N1, the accuracy and AUC of the multimodal DCIS-Net were 0.752-0.766 and 0.859-0.907 in the three-classification task, respectively. The accuracy and AUC for discriminating DCIS from upstaged DCIS were 0.751-0.780 and 0.829-0.861, respectively. In the test set of N2, the accuracy and AUC of discriminating low-grade DCIS from upstaged low-grade DCIS were 0.769-0.987 and 0.818-0.939, respectively. DL was ranked from one to five in the importance of features in the multimodal-DCIS-Net. CONCLUSION: By developing the DCIS-Net and integrating it with multimodal information, diagnosing low-grade DCIS, intermediate-to high-grade DCIS, and upstaged DCIS is possible. It can also be used to distinguish DCIS from upstaged DCIS and low-grade DCIS from upstaged low-grade DCIS, which could pave the way for the DCIS clinical workflow.

Assessment of nuclear grade-based recurrence risk classification in patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative, node-positive high-risk early breast cancer.

BACKGROUND: Histological grade (HG) has been used in the MonrachE trial to select patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, node-positive high-risk early breast cancer (EBC). Although nuclear grade (NG) is widely used in Japan, it is still unclear whether replacing HG with NG can appropriately select high-risk patients. METHODS: We retrospectively reviewed 647 patients with HR-positive, HER2-negative, node-positive EBC and classified them into the following four groups: group 1: ≥ 4 positive axillary lymph nodes (pALNs) or 1-3 pALNs and either grade 3 of both grading systems or tumors ≥ 5 cm; group 2: 1-3 pALNs, grade < 3, tumor < 5 cm, and Ki-67 ≥ 20%; group 3: 1-3 pALNs, grade < 3, tumor < 5 cm, and Ki-67 < 20%; and group 4: group 2 or 3 by HG classification but group 1 by NG classification. We compared invasive disease-free survival (IDFS) and distant relapse-free survival (DRFS) among the four groups using the Kaplan-Meier method with the log-rank test. RESULTS: Group 1 had a significantly worse 5-year IDFS and DRFS than groups 2 and 3 (IDFS 80.8% vs. 89.5%, P = 0.0319, 80.8% vs. 95.5%, P = 0.002; DRFS 85.2% vs. 95.3%, P = 0.0025, 85.2% vs. 98.4%, P < 0.001, respectively). Group 4 also had a significantly worse 5-year IDFS (78.0%) and DRFS (83.6%) than groups 2 and 3. CONCLUSIONS: NG was useful for stratifying the risk of recurrence in patients with HR-positive, HER2-negative, node-positive EBC and was the appropriate risk assessment for patient groups not considered high-risk by HG classification.

T1a Renal Cell Carcinoma With Metastasis: Japanese Society of Renal Cancer Retrospective Multi-institute Results.

BACKGROUND/AIM: Small renal cell carcinomas (sRCC) have drastically increased in recent years. Considering that sRCC have heterogeneous biology, it would be clinically relevant if specific clinical or pathological parameters could predict sRCC metastasis. In the present study, we aimed to assess the clinicopathological factors of pathologic T1a RCC (pT1a RCC) with or without metastasis to explore factors predicting metastasis. PATIENTS AND METHODS: The present study included 198 patients with pT1a RCC who underwent radical or partial nephrectomy at fifteen institutions belonging to the Japanese Society of Renal Cancer, between1985 and 2017. Clinicopathological parameters, including age, sex, tumour size, tumour side, histological subtype, histological nuclear grade, lymphovascular invasion, and histological growth patterns, were analysed. RESULTS: Fuhrman grade 3 or 4 tumours and infiltrative tumour growth patterns were significantly higher in patients with metastasis than in those without. The most common site of synchronous metastasis was the bone in patients with pT1a RCC (65.4%), whereas for patients with post-surgery metachronous metastasis (46.2%), it was the lungs. CONCLUSION: Histological growth pattern and nuclear grade are vital for predicting metastasis in pT1a RCC, suggesting careful long-term follow-up for such patients.

Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma.

Purpose: To develop models based on radiomics and genomics for predicting the histopathologic nuclear grade with localized clear cell renal cell carcinoma (ccRCC) and to assess whether macro-radiomics models can predict the microscopic pathological changes. Method: In this multi-institutional retrospective study, a computerized tomography (CT) radiomic model for nuclear grade prediction was developed. Utilizing a genomics analysis cohort, nuclear grade-associated gene modules were identified, and a gene model was constructed based on top 30 hub mRNA to predict the nuclear grade. Using a radiogenomic development cohort, biological pathways were enriched by hub genes and a radiogenomic map was created. Results: The four-features-based SVM model predicted nuclear grade with an area under the curve (AUC) score of 0.94 in validation sets, while a five-gene-based model predicted nuclear grade with an AUC of 0.73 in the genomics analysis cohort. A total of five gene modules were identified to be associated with the nuclear grade. Radiomic features were only associated with 271 out of 603 genes in five gene modules and eight top 30 hub genes. Differences existed in the enrichment pathway between associated and un-associated with radiomic features, which were associated with two genes of five-gene signatures in the mRNA model. Conclusion: The CT radiomics models exhibited higher predictive performance than mRNA models. The association between radiomic features and mRNA related to nuclear grade is not universal.

Elaboration of geopolymer package derived from uncalcined phosphate sludge and its solidification performance on nuclear grade resins loaded with 134Cs.

Nuclear-grade Spent Organic Resin (SOR) contains high concentrations of radioactive nuclides and metal contaminants, while phosphate sludge contains high amount of fine clayey particles and CO32-, both posing a major threat to the biosphere. In this study, a novel geopolymer package (GP) was proposed to directly solidify SOR loaded with 134Cs by incorporating uncalcined phosphate sludge (UPS) as feedstocks, activated by NaOH/KOH. The results showed that alkali-mixed reagents-activated GP is more advantageous in terms of chemical stability and mechanical properties than NaOH-activated GP, recording compressive strength values greater than the waste acceptance criteria and OPC. The 28-day compressive strength of solidified packages can exceed 31 MPa at the highest amount of 42 wt% UPS. The addition of NaF powder into the solidified packages generates more hybrid type gels, which are more conducive to partial dissolution and bonding UPS particles, thereby producing stable and stronger GP. Leaching results of solidified GP in presence of up to 13 wt% SORs showed that only 0.15 % of total 134Cs was leached, even under aggressive solutions. Solidification mechanism revealed that activation of UPS-MK blend forms N,K-A-S-H, (N,K,C)-A-S-H/C-S-H gels coexisting with unreacted particles, thereby solidify/stabilize metal contaminants and Cs+ by a synergetic immobilization action of hydration products via substitution and encapsulation. This study provides a promising paradigm for effective solidification of nuclear-grade resins and synergetic harmless treatment of industrial/phosphate mine solid wastes.

Prognostic value of PD-L1 expression in recurrent renal cell carcinoma after nephrectomy: a secondary analysis of the ARCHERY study.

BACKGROUND: Nephrectomy is a curative treatment for localized renal cell carcinoma (RCC), but patients with poor prognostic features may experience relapse. Understanding the prognostic impact of programmed death-ligand 1 (PD-L1) expression in patients who underwent nephrectomy for RCC may aid in future development of adjuvant therapy. METHODS: Of 770 surgical specimens collected from Japanese patients enrolled in the ARCHERY study, only samples obtained from patients with recurrent RCC after nephrectomy were examined for this secondary analysis. Patients were categorized into low- and high-risk groups based on clinical stage and Fuhrman grade. Time to recurrence (TTR) and overall survival (OS) were analyzed. RESULTS: Both TTR and OS were shorter in patients with PD-L1-positive than -negative tumors (median TTR 12.1 vs. 21.9 months [HR 1.46, 95% CI 1.17, 1.81]; median OS, 75.8 vs. 97.7 months [HR 1.32, 95% CI 1.00, 1.75]). TTR and OS were shorter in high-risk patients with PD-L1-positive than -negative tumors (median TTR 7.6 vs. 15.3 months [HR 1.49, 95% CI 1.11, 2.00]; median OS, 55.2 vs. 83.5 months [HR 1.53, 95% CI 1.06, 2.21]) but not in low-risk patients. CONCLUSIONS: This ARCHERY secondary analysis suggests that PD-L1 expression may play a role in predicting OS and risk of recurrence in high-risk patients with localized RCC. CLINICAL TRIAL REGISTRATION: UMIN000034131.

Clinical study of centrality index in predicting the postoperative pathological nuclear grade of clear cell renal cell carcinoma.

OBJECTIVE: This study aimed to investigate whether the centrality index score (C index) can be used to predict the histological nuclear grade of clear cell renal cell carcinoma (ccRCC) and guide the clinical treatment of this disease. METHODS: This study included 194 patients with ccRCC who underwent renal surgery at our center between 2016 and 2020 and had complete computed tomography or computed tomography angiography (CT/CTA) data and C index. The relationship between the pathological grade of renal masses and the C index was evaluated. RESULTS: In univariate analysis, the gender, body mass index (BMI), tumor size, or height from the center of the renal hilum to the maximum diameter of the tumor along the 90° vertical axis (in cm) is y. The horizontal distance from the reference point of the central axis of the renal hilum to the tumor center is x. The distance from the center of the kidney to the center of the tumor is c and the C index was significantly correlated with postoperative tumor grade (p < 0.05). Multivariate analysis showed that tumor size and C index were independent prognostic factors for the preoperative prediction of the pathological grade factor of ccRCC. The receiver operating characteristic curves of the multi-parameter regression model [0.9471, 95% confidence interval (95% CI) 0.9138-0.9803], C index (0.9324, 95% CI 0.8899-0.9748), and tumor size (0.9307, 95% CI 0.8951-0.9663) were compared. CONCLUSION: Tumor size and C index were independent prognostic factors for high-grade pathology, and large tumors and small C index were associated with high-grade pathology. Therefore, the C index can help urologists make treatment decisions.

Validation of a nuclear grading system for resected stage I-IIIA, high-risk, node-negative invasive breast carcinoma in the N·SAS-BC 01 trial.

BACKGROUND: This retrospective observational study validated nuclear grading criteria developed to identify a high-risk group with recurrence rate ≥20-30% and local pathology diagnosis used in a previous multi-institutional randomized N·SAS-BC 01 trial, where the efficacy of adjuvant chemotherapy regimens was evaluated in 733 high-risk node-negative invasive breast cancer patients. METHODS: Of 545 patients with long-term follow-up data (median 12.1 years), pathology slides, and local pathology diagnosis, 530 eligible patients were subjected to central pathology review (CPR) for histological type and nuclear grade (NG). Concordance in NGs was compared with local diagnosis. The 10/15-year recurrence-free survival (RFS) and overall survival (OS) rates stratified by NG and histological type were calculated. RESULTS: Local diagnoses were invasive ductal carcinoma (IDC)-NG2, IDC-NG3, invasive lobular carcinoma (ILC), and metaplastic carcinoma (MC) in 158/327/38/7 patients, respectively. The 10/15-year RFS rates were 87.2/82.6% for IDC-NG2 and 81.8/75.0% for IDC-NG3 (p = 0.061), and OS rates were 95.0/92.8% for IDC-NG2 and 90.8/85.7% for IDC-NG3 (p = 0.042). CPR graded 485 locally diagnosed IDCs as IDC-NG1/NG2/NG3/unknown in 98/116/267/4 patients, respectively. No significant difference was found among survival curves for the three NG groups. Although the agreement level between local and CPR diagnoses was low (κ = 0.311), both diagnoses identified a patient group with a 15-year recurrence rate ≥ 20%. The 10/15-year RFS rates were 79.4/63.5% for ILC and 68.6%/unknown for MC. CONCLUSIONS: The N·SAS grading system identified a patient group with high-risk node-negative invasive breast cancer, suggesting that local diagnosis was performed efficiently in the N·SAS-BC 01 trial. TRIAL REGISTRATION NUMBER: UMIN000022571. Date of registration: June 1, 2016.

Assessing the correlation between FDG PET findings of IDC breast carcinoma and histopathology of coexisting ductal carcinoma in-situ.

BACKGROUND: Ductal carcinoma in-situ (DCIS) often coexists with invasive ductal carcinoma (IDC) of the breast. DCIS is considered as a non-obligate precursor of IDC when both coexist. 18F-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG PET/CT) imaging is commonly used in the staging and follow-up assessment of breast cancer. In this study, we aimed to assess if there is any correlation between primary tumor PET and histopathology findings and histopathological features of the coexisting DCIS. MATERIAL AND METHODS: FDG PET/CT images and histopathology results of the patients with newly diagnosed breast cancer (IDC) with coexisting DCIS were analyzed in this retrospective study. The grade and size of the primary tumor and histopathological features of the coexisting DCIS (nuclear grade and architectural pattern) were obtained from the postoperative histopathology results. Maximum standardized uptake values (SUV: SUVmax and SULmax) of the primary tumor normalized by weight and lean body mass were measured. Statistical analysis was performed to assess the correlation between various parameters of IDC and DCIS. RESULTS: This study included sixty-two (62) patients with IDC-DCIS. Primary tumor grade was significantly correlated and associated with the nuclear grade of the coexisting DCIS (polychoric correlation r = 0.736, and Fisher exact test, PV < 0.001, respectively). Primary tumor SUV was not correlated with the nuclear grade and architectural pattern of the coexisting DCIS (polyserial correlation r = 0.172, PV = 0.155, and Point Bi-Serial correlation r = -0.009, PV = 0.955, respectively). Median primary tumor size was marginally significantly different among DCIS nuclear grades but it was not significantly different in comedo and non-comedo cases (Kruskal-Wallis test PV = 0.053, and Mann-Whitney U test PV = 0.890, respectively). CONCLUSIONS: Primary tumor grade is correlated with the nuclear grade of the coexisting DCIS. SUV of primary tumor does not seem to be correlated with the histopathological features of coexisting DCIS (nuclear grade and architectural pattern) but this may be further studied in a larger number of patients.

Association between nuclear grade of renal cell carcinoma and the aorta-lesion-attenuation-difference.

INTRODUCTION AND BACKGROUND: Several features noted on renal mass biopsy (RMB) can influence treatment selection including tumor histology and nuclear grade. However, there is poor concordance between renal cell carcinoma (RCC) nuclear grade on RMB compared to nephrectomy specimens. Here, we evaluate the association of nuclear grade with aorta-lesion-attenuation-difference (ALAD) values determined on preoperative CT scan. METHODS AND MATERIALS: A retrospective review of preoperative CT scans and surgical pathology was performed on patients undergoing nephrectomy for solid renal masses. ALAD was calculated by measuring the difference in Hounsfield units (HU) between the aorta and the lesion of interest on the same image slice on preoperative CT scan. The discriminative ability of ALAD to differentiate low-grade (nuclear grade 1 and 2) and high-grade (nuclear grade 3 and 4) tumors was evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC) using ROC analysis. Sub-group analysis by histologic sub-type was also performed. RESULTS: A total of 368 preoperative CT scans in patients with RCC on nephrectomy specimen were reviewed. Median patient age was 61 years (IQR 52-68). The majority of patients were male, 66% (243/368). Tumor histology was chromophobe RCC in 7.6%, papillary RCC in 15.5%, and clear cell RCC in 76.9%. The majority, 69.3% (253/365) of tumors, were stage T1a. Nuclear grade was grade 1 in 5.46% (19/348), grade 2 in 64.7% (225/348), grade 3 in 26.2% (91/348), and grade 4 in 3.2% (11/348). Nephrographic ALAD values for grade 1, 2, 3, and 4 were 73.7, 46.5, 36.4, and 43.1, respectively (p = 0.0043). Nephrographic ALAD was able to differentiate low-grade from high-grade RCC with a sensitivity of 32%, specificity of 89%, PPV of 86%, and NPV of 36%. ROC analysis demonstrated the predictive utility of nephrographic ALAD to predict high- versus low-grade RCC with an AUC of 0.60 (95% CI 0.51-0.69). CONCLUSION: ALAD was significantly associated with nuclear grade in our nephrectomy series. Strong specificity and PPV for the nephrographic phrase demonstrate a potential role for ALAD in the pre-operative setting that may augment RMB findings in assessing nuclear grade of RCC. Although this association was statistically significant, the clinical utility is limited at this time given the results of the statistical analysis (relatively poor ROC analysis). Sub-group analysis by histologic subtype yielded very similar diagnostic performance and limitations of ALAD. Further studies are necessary to evaluate this relationship further.

Can renalase be a novel candidate biomarker for distinguishing renal tumors?

Renalase (RNLS) is synthesized mainly in renal tissues. The function of RNLS in cancerous renal tissues has not been investigated. We investigated the synthesis of RNLS in chromophobe renal cell carcinoma, papillary renal cell carcinoma and clear cell renal cell carcinoma with Fuhrman grades (FG): FG1, nucleoli are absent or inconspicuous and basophilic; FG2, nucleoli are conspicuous and eosinophilic and visible but not prominent; FG3, nucleoli are conspicuous and eosinophilic; FG4, extreme nuclear pleomorphism, multinucleate giant cells, and/or rhabdoid and/or sarcomatoid differentiation. We used 90 tissue samples including 15 healthy controls, 15 chromophobe renal cell carcinoma tissues and 10 papillary renal cell carcinoma renal tissues: 12 FG1, 14 FG 2, 14 FG 3 and 10 FG4. RNLS in the tissue samples was measured using enzyme linked immunosorbent assay and immunostaining of RNLS in these tissues. RNLS was significantly greater in the chromophobe renal cell carcinoma and papillary renal cell carcinoma tissues than the control. The least amount of RNLS was found in the renal tissues of clear cell renal cell carcinoma FG1; the amount of RNLS increased as the FG grades increased. Because RNLS increased significantly in renal tissues due to cancer, except for clear cell renal cell carcinoma FG1, RNLS may be useful biomarker for distinguishing grades of renal cancer. Because RNLS increases cell survival, anti-RNLS preparations may be useful for treating cancer in the future.

Can texture analysis based on single unenhanced CT accurately predict the WHO/ISUP grading of localized clear cell renal cell carcinoma?

OBJECTIVE: The purpose was to investigate the value of texture analysis in predicting the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of localized clear cell renal cell carcinoma (ccRCC) based on unenhanced CT (UECT). MATERIALS AND METHODS: Pathologically confirmed subjects (n = 104) with localized ccRCC who received UECT scanning were collected retrospectively for this study. All cases were classified into low grade (n = 53) and high grade (n = 51) according to the WHO/ISUP grading and were randomly divided into training set and test set as a ratio of 7:3. Using 3D-ROI segmentation on UECT images and extracted ninety-three texture features (first-order, gray-level co-occurrence matrix [GLCM], gray-level run length matrix [GLRLM], gray-level size zone matrix [GLSZM], neighboring gray tone difference matrix [NGTDM] and gray-level dependence matrix [GLDM] features). Univariate analysis and the least absolute shrinkage selection operator (LASSO) regression were used for feature dimension reduction, and logistic regression classifier was used to develop the prediction model. Using receiver operating characteristic (ROC) curve, bar chart and calibration curve to evaluate the performance of the prediction model. RESULTS: Dimension reduction screened out eight optimal texture features (maximum, median, dependence variance [DV], long run emphasis [LRE], run entropy [RE], gray-level non-uniformity [GLN], gray-level variance [GLV] and large area low gray-level emphasis [LALGLE]), and then the prediction model was developed according to the linear combination of these features. The accuracy, sensitivity, specificity, and AUC of the model in training set were 86.1%, 91.4%, 81.1%, and 0.937, respectively. The accuracy, sensitivity, specificity, and AUC of the model in test set were 81.2%, 81.2%, 81.2%, and 0.844, respectively. The calibration curves showed good calibration both in training set and test set (P > 0.05). CONCLUSION: This study has demonstrated that the radiomics model based on UECT texture analysis could accurately evaluate the WHO/ISUP grading of localized ccRCC.

Application of diffusion kurtosis tensor MR imaging in characterization of renal cell carcinomas with different pathological types and grades.

BACKGROUND: To probe the feasibility and reproducibility of diffusion kurtosis tensor imaging (DKTI) in renal cell carcinoma (RCC) and to apply DKTI in distinguishing the subtypes of RCC and the grades of clear cell RCC (CCRCC). METHODS: Thirty-eight patients with pathologically confirmed RCCs [CCRCC for 30 tumors, papillary RCC (PRCC) for 5 tumors and chromophobic RCC (CRCC) for 3 tumors] were involved in the study. Diffusion kurtosis tensor MR imaging were performed with 3 b-values (0, 500, 1000s/mm2) and 30 diffusion directions. The mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr) values and mean diffusity (MD) for RCC and contralateral normal parenchyma were acquired. The inter-observer agreements of all DKTI metrics of contralateral renal cortex and medulla were evaluated using Bland-Altman plots. Statistical comparisons with DKTI metrics of 3 RCC subtypes and between low-grade (Furman grade I ~ II, 22 cases) and high-grade (Furman grade III ~ IV, 8 cases) CCRCC were performed with ANOVA test and Student t test separately. Receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic efficacy of DKTI metrics for predicting nuclear grades of CCRCC. Correlations between DKTI metrics and nuclear grades were also evaluated with Spearman correlation analysis. RESULTS: Inter-observer measurements for each metric showed great reproducibility with excellent ICCs ranging from 0.81 to 0.87. There were significant differences between the DKTI metrics of RCCs and contralateral renal parenchyma, also among the subtypes of RCC. MK and Ka values of CRCC were significantly higher than those of CCRCC and PRCC. Statistical difference of the MK, Ka, Kr and MD values were also obtained between CCRCC with high- and low-grades. MK values were more effective for distinguishing between low- and high- grade CCRCC (area under the ROC curve: 0.949). A threshold value of 0.851 permitted distinction with high sensitivity (90.9%) and specificity (87.5%). CONCLUSION: Our preliminary results suggest a possible role of DKTI in differentiating CRCC from CCRCC and PRCC. MK, the principle DKTI metric might be a surrogate biomarker to predict nuclear grades of CCRCC. TRIAL REGISTRATION: ChiCTC, ChiCTR-DOD-17010833, Registered 10 March, 2017, retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=17559 .