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Objectives: The objective is to develop and validate intratumoral and peritumoral ultrasomics models utilizing endoscopic ultrasonography (EUS) to predict pathological grading in pancreatic neuroendocrine tumors (PNETs). Methods: Eighty-one patients, including 51 with grade 1 PNETs and 30 with grade 2/3 PNETs, were included in this retrospective study after confirmation through pathological examination. The patients were randomly allocated to the training or test group in a 6:4 ratio. Univariate and multivariate logistic regression were used for screening clinical and ultrasonic characteristics. Ultrasomics is ultrasound-based radiomics. Ultrasomics features were extracted from both the intratumoral and peritumoral regions of conventional EUS images. Subsequently, the dimensionality of these radiomics features was reduced using the least absolute shrinkage and selection operator (LASSO) algorithm. A machine learning algorithm, namely multilayer perception (MLP), was employed to construct prediction models using only the nonzero coefficient features and retained clinical features, respectively. Results: One hundred seven ultrasomics features based on EUS were extracted, and only features with nonzero coefficients were ultimately retained. Among all the models, the combined ultrasomics model achieved the greatest performance, with an AUC of 0.858 (95% CI, 0.7512 - 0.9642) in the training group and 0.842 (95% CI, 0.7061 - 0.9785) in the test group. A calibration curve and a decision curve analysis (DCA) also demonstrated its accuracy and utility. Conclusions: The integrated model using EUS ultrasomics features from intratumoral and peritumoral tumors accurately predicts PNETs' pathological grades pre-surgery, aiding personalized treatment planning. Trial registration: ChiCTR2400091906. [ABSTRACT FROM AUTHOR]

Background: Urothelial carcinoma is a common malignant tumor of the urinary system, with prognosis linked to pathological grade and TNM stage. Alterations in chromosomes 3, 7, and 17, along with the P16 locus on chromosome 9 (CSP3, CSP7, CSP17, and GLPp16), are associated with cancer progression and may serve as important biomarkers. This study aimed to explore the relationships between these chromosomal factors and the pathological grade and TNM stage of UCC, potentially leading to a novel diagnostic approach that enhances patient stratification and treatment planning. Methods: A retrospective analysis was conducted on 149 patients to evaluate the correlation between CSP3, CSP7, CSP17, GLPp16, TNM stage, and pathological grade using chi-square tests and logistic regression. Immunohistochemistry was employed to assess the associated changes. Results: Univariate analysis indicated that only CSP7 and GLPp16 were significantly associated with pathological grade. Logistic regression linked GLPp16 and gender to pathological grade in urothelial carcinoma. A nomogram model incorporating these factors demonstrated reliable calibration in the training set (non-significant Hosmer-Lemeshow test, P = 0.436; AUC = 0.785, 95% CI: 0.707 - 0.863) and effective discrimination in the test set (AUC = 0.740, 95% CI: 0.559 - 0.920). Immunohistochemistry revealed P16 gene deletion in low-grade urothelial carcinoma and amplification in high-grade urothelial carcinoma. Conclusion: Mutations at the GLPp16 were significantly correlated with the pathological grade of urothelial carcinoma. Additionally, the amplification of GLPp16 was recognized as a contributing factor to the development of high-grade urothelial carcinoma. [ABSTRACT FROM AUTHOR]

Abstract Objectives The objective is to develop and validate intratumoral and peritumoral ultrasomics models utilizing endoscopic ultrasonography (EUS) to predict pathological grading in pancreatic neuroendocrine tumors (PNETs). Methods Eighty-one patients, including 51 with grade 1 PNETs and 30 with grade 2/3 PNETs, were included in this retrospective study after confirmation through pathological examination. The patients were randomly allocated to the training or test group in a 6:4 ratio. Univariate and multivariate logistic regression were used for screening clinical and ultrasonic characteristics. Ultrasomics is ultrasound-based radiomics. Ultrasomics features were extracted from both the intratumoral and peritumoral regions of conventional EUS images. Subsequently, the dimensionality of these radiomics features was reduced using the least absolute shrinkage and selection operator (LASSO) algorithm. A machine learning algorithm, namely multilayer perception (MLP), was employed to construct prediction models using only the nonzero coefficient features and retained clinical features, respectively. Results One hundred seven ultrasomics features based on EUS were extracted, and only features with nonzero coefficients were ultimately retained. Among all the models, the combined ultrasomics model achieved the greatest performance, with an AUC of 0.858 (95% CI, 0.7512 - 0.9642) in the training group and 0.842 (95% CI, 0.7061 - 0.9785) in the test group. A calibration curve and a decision curve analysis (DCA) also demonstrated its accuracy and utility. Conclusions The integrated model using EUS ultrasomics features from intratumoral and peritumoral tumors accurately predicts PNETs' pathological grades pre-surgery, aiding personalized treatment planning. Trial registration ChiCTR2400091906.

Objective To investigate the predictive value of inflammatory markers and Cancer antigen 125 (CA125) for preoperative pathological staging and grading of endometrial carcinoma (EC) . Methods A selection of 214 EC patients admitted from 2021-2023, randomized into the training set (134 cases) and validation set (80 cases) . Inflammatory markers (NLR, PLR, SII) and CA125 levels were measured in all patients. The pathological and stages grades of EC patients were evaluated, and the differences in inflammatory markers and CA125 levels among patients with different pathological stages and grades were compared, Establish a column chart prediction model to analyze the predictive value of inflammation indicators and CA125 for preoperative early EC pathological and stages grades. Results There were 120 cases of TNM stage Ⅰ, 4 cases of stage Ⅱ and 10 cases of stage Ⅲ in the training set. 16 cases of International Federation of Gynecology and Obstetrics (FIGO) grade 1, 79 cases of grade 2 and 39 cases of grade 3 in the training set. NLR, PLR, and CA125 in EC patients with TNM pathological stages Ⅱ and Ⅲ were higher than those TNM in pathological stage I (P < 0.05) . NLR, PLR, and CA125 in EC patients with FIGO grades 2 and 3 were higher than those in FIGO grade 1 (P < 0.05) . Based on NLR, PLR, SII and CA125, a nomogram prediction model for EC TNM pathological stage and FIGO grade in the training set was established. Bootstrap method was used to verify the model discrimination and draw the calibration curve. The results showed that the calibration curve Y and X lines of the training set and the validation set were similar, and the nomogram model discrimination was good. The ROC curve revealed that the AUC of the nomogram model between the training set and the validation set for predicting the risk of high preoperative EC stage and high pathological grade was > 0.90, which had a high predictive value. Conclusion The nomogram prediction model based on inflammation and CA125 has a high predictive value for the pathological stage and grade of preoperative EC.

Purpose: To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC). Methods: The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit. Results: 166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000–1.000) in the training set and 0.927 (95%CI: 0.837–0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837–0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908. Conclusion: This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making. [ABSTRACT FROM AUTHOR]

BackgroundUrothelial carcinoma is a common malignant tumor of the urinary system, with prognosis linked to pathological grade and TNM stage. Alterations in chromosomes 3, 7, and 17, along with the P16 locus on chromosome 9 (CSP3, CSP7, CSP17, and GLPp16), are associated with cancer progression and may serve as important biomarkers. This study aimed to explore the relationships between these chromosomal factors and the pathological grade and TNM stage of UCC, potentially leading to a novel diagnostic approach that enhances patient stratification and treatment planning.MethodsA retrospective analysis was conducted on 149 patients to evaluate the correlation between CSP3, CSP7, CSP17, GLPp16, TNM stage, and pathological grade using chi-square tests and logistic regression. Immunohistochemistry was employed to assess the associated changes.ResultsUnivariate analysis indicated that only CSP7 and GLPp16 were significantly associated with pathological grade. Logistic regression linked GLPp16 and gender to pathological grade in urothelial carcinoma. A nomogram model incorporating these factors demonstrated reliable calibration in the training set (non-significant Hosmer-Lemeshow test, P = 0.436; AUC = 0.785, 95% CI: 0.707 - 0.863) and effective discrimination in the test set (AUC = 0.740, 95% CI: 0.559 - 0.920). Immunohistochemistry revealed P16 gene deletion in low-grade urothelial carcinoma and amplification in high-grade urothelial carcinoma.ConclusionMutations at the GLPp16 were significantly correlated with the pathological grade of urothelial carcinoma. Additionally, the amplification of GLPp16 was recognized as a contributing factor to the development of high-grade urothelial carcinoma.

Objective: To investigate the ultrasound signs of benign and malignant lesions of ovarian tumors and their correlation with clinical staging and pathological grading of ovarian cancer. Methods: 148 patients with ovarian tumors admitted to our hospital from August 2020 to August 2023 were selected for retrospective analysis, and surgical pathology and pathological biopsy were used as the gold standard for diagnosis, 60 patients diagnosed with ovarian cancer were included in the malignant group, and 88 patients diagnosed with benign ovarian tumors were included in the benign group. Ultrasonography was performed on all patients and their ultrasound image characteristics were analyzed. Subsequently, the ultrasound diagnostic parameters of 60 patients with ovarian cancer in different clinical stages and pathological grades were analyzed, and the correlation between ultrasound diagnostic related parameters and ovarian clinical stages and pathological grades was analyzed. Results: The accuracy of ultrasound in the diagnosis of benign ovarian tumors was 86.36% (76/88), while the diagnostic rate of malignant tumors was 88.64% (78/88); There were significant differences in the comparison of RI, PI, EDV, and PSV among patients with different clinical stages. The RI and PI of stage I patients were (0.77± 0.14) and (1.67± 0.24) higher than those of stage II patients (0.64± 0.15) and (1.25± 0.16), stage III patients (0.52± 0.17) and (0.96± 0.16), stage IV patients (0.41± 0.12) and (0.76± 0.12). The EDV and PSV of stage I patients were (8.63± 1.27) cm/s and (16.53± 2.53) cm/s lower than those of stage II patients (10.25± 1.68) cm/s and (18.44± 1.58) cm/s Phase III patients (12.73± 1.79) cm/s and (20.14± 2.25) cm/s, and Phase IV patients (15.51± 1.12) cm/s and (23.06± 1.98) cm/s (P<0.05); There were significant differences in the comparison of RI, PI, EDV, and PSV among patients with different clinical stages. The RI and PI of grade I patients were (0.81± 0.16) and (1.62± 0.19) higher than those of grade II patients (0.65± 0.12) and (0.91± 0.22), grade III patients (0.47± 0.17) and (0.67± 0.13). The EDV and PSV of grade I patients were (8.32± 1.51) cm/s and (15.12± 3.33) cm/s lower than those of grade II patients (12.75± 1.14) cm/s and (21.31± 3.14) cm/s Grade III patients (15.35± 1.79) cm/s and (24.08± 2.04) cm/s, (P<0.05); The Spearman correlation analysis results showed that clinical staging and pathological grading were negatively correlated with RI and PI, but positively correlated with EDV and PSV (P<0.05). Conclusion: Ultrasound has important guiding value in the diagnosis of benign and malignant ovarian tumors, and has a significant correlation with the clinical staging and pathological grading of ovarian cancer, which is worthy of clinical application and promotion. [ABSTRACT FROM AUTHOR]

Objective To investigate the expression of receptor tyrosine kinase-like orphan rectetpor 1 (ROR1) in invasive lung adenocarcinoma with different histological grades, and to analyze its relationship with histopathological grade of lung adenocarcinoma so as to explore whether it can be used as a potential indicator for the diagnostic evaluation of invasive lung adenocarcinoma. Methods A total of 290 paraffin-embedded tissue specimens of invasive lung adenocarcinoma were collected, and the relationship between ROR1 expression and invasive lung adenocarcinoma grade was statistically analyzed by HE staining sections. Results ROR1 was significantly highly expressed in the poorly differentiated invasive lung adenocarcinoma (P < 0.001), the Ki-67 index in the low-differentiated group was significantly higher than that in the high-differentiation and medium-differentiated groups (P < 0.001), and the Ki-67 index was higher in the high-expression group than in the low-expression group (P < 0.01). Conclusion There are significant differences in the expression levels of ROR1 in different grades of lung adenocarcinoma, and its expression level is closely related to the degree of differentiation and malignancy of invasive lung adenocarcinoma, which makes it possible to become a new biomarker for the diagnosis of lung adenocarcinoma and assist in the evaluation of clinical features and prognosis of lung adenocarcinoma, and provide a scientific basis to formulate personalized treatment strategies for patients. [ABSTRACT FROM AUTHOR]

Objective: To investigate the potential of CT-enhanced quantitative parameters for preoperative prediction of WHO/ISUP grading for renal clear cell carcinoma (ccRCC). Methods: The study involved collecting clinical and CT-enhanced data of 98 patients with ccRCC, who were then classified into low level group (76 cases) and high level group (22 cases) based on the WHO/ISUP classification. Differences in CT-enhanced quantitative parameters between the two groups were compared, and the diagnostic efficacy of each parameter for predicting ccRCC WHO/ISUP grading was evaluated. External verification was conducted to identify CT-enhanced quantitative parameters with the best generalization ability. Results: There were significant differences in the CT value, net increment, and enhancement rate in both cortical and substantive phases between the two groups. The AUC values were 0.834, 0.871, 0.900, 0.707, 0.678, and 0.762, respectively. The cut-off values were 123.5 HU, 71 HU, 0.73, 87.5 HU, 54 HU, 0.67, respectively. The diagnostic efficacy of cortical enhancement rate was the highest with an AUC of 0.900, a sensitivity of 0.842, and a specificity of 0.864. The external validation results revealed that the diagnostic efficacy of cortical phase enhancement rate (AUC=0.867) was better than that of cortical phase CT (AUC=0.735) and cortical phase net increment (AUC=0.709). The Z values were 2.134 and 2.417, respectively. Conclusion: The quantitative parameters of CT enhancement can be used to predict ccRCC WHO/ISUP grading. Cortical phase enhancement rate is the parameter with the highest diagnostic efficiency and the best generalization ability.

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Abstract Background The determination of pathological grading has a guiding significance for the treatment of pancreatic ductal adenocarcinoma (PDAC) patients. However, there is a lack of an accurate and safe method to obtain pathological grading before surgery. The aim of this study is to develop a deep learning (DL) model based on 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT) for a fully automatic prediction of preoperative pathological grading of pancreatic cancer. Methods A total of 370 PDAC patients from January 2016 to September 2021 were collected retrospectively. All patients underwent 18F-FDG-PET/CT examination before surgery and obtained pathological results after surgery. A DL model for pancreatic cancer lesion segmentation was first developed using 100 of these cases and applied to the remaining cases to obtain lesion regions. After that, all patients were divided into training set, validation set, and test set according to the ratio of 5:1:1. A predictive model of pancreatic cancer pathological grade was developed using the features computed from the lesion regions obtained by the lesion segmentation model and key clinical characteristics of the patients. Finally, the stability of the model was verified by sevenfold cross-validation. Results The Dice score of the developed PET/CT-based tumor segmentation model for PDAC was 0.89. The area under curve (AUC) of the PET/CT-based DL model developed on the basis of the segmentation model was 0.74, with an accuracy, sensitivity, and specificity of 0.72, 0.73, and 0.72, respectively. After integrating key clinical data, the AUC of the model improved to 0.77, with its accuracy, sensitivity, and specificity boosted to 0.75, 0.77, and 0.73, respectively. Conclusion To the best of our knowledge, this is the first deep learning model to end-to-end predict the pathological grading of PDAC in a fully automatic manner, which is expected to improve clinical decision-making.

There is significant variability with respect to the prognosis of nonmetastatic clear cell renal cell carcinoma (ccRCC) patients with venous tumor thrombus (VTT). By applying multiregion whole‐exome sequencing on normal‐tumor‐thrombus‐metastasis quadruples from 33 ccRCC patients, we showed that metastases were mainly seeded by VTT (81.8%) rather than primary tumors (PTs). A total of 706 nonmetastatic ccRCC patients with VTT from three independent cohorts were included in this study. C‐index analysis revealed that pathological grading of VTT outperformed other indicators in risk assessment (OS: 0.663 versus 0.501–0.610, 0.667 versus 0.544–0.651, and 0.719 versus 0.511–0.700 for Training, China‐Validation, and Poland‐Validation cohorts, respectively). We constructed a risk predicting model, TT‐GPS score, based on four independent variables: VTT height, VTT grading, perinephric fat invasion, and sarcomatoid differentiation in PT. The TT‐GPS score displayed better discriminatory ability (OS, c‐index: 0.706–0.840, AUC: 0.788–0.874; DFS, c‐index: 0.691–0.717, AUC: 0.771–0.789) than previously reported models in risk assessment. In conclusion, we identified for the first‐time pathological grading of VTT as an unheeded prognostic factor. By incorporating VTT grading, the TT‐GPS score is a promising prognostic tool in predicting the survival of nonmetastatic ccRCC patients with VTT. [ABSTRACT FROM AUTHOR]

Background: The determination of pathological grading has a guiding significance for the treatment of pancreatic ductal adenocarcinoma (PDAC) patients. However, there is a lack of an accurate and safe method to obtain pathological grading before surgery. The aim of this study is to develop a deep learning (DL) model based on 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT) for a fully automatic prediction of preoperative pathological grading of pancreatic cancer. Methods: A total of 370 PDAC patients from January 2016 to September 2021 were collected retrospectively. All patients underwent 18F-FDG-PET/CT examination before surgery and obtained pathological results after surgery. A DL model for pancreatic cancer lesion segmentation was first developed using 100 of these cases and applied to the remaining cases to obtain lesion regions. After that, all patients were divided into training set, validation set, and test set according to the ratio of 5:1:1. A predictive model of pancreatic cancer pathological grade was developed using the features computed from the lesion regions obtained by the lesion segmentation model and key clinical characteristics of the patients. Finally, the stability of the model was verified by sevenfold cross-validation. Results: The Dice score of the developed PET/CT-based tumor segmentation model for PDAC was 0.89. The area under curve (AUC) of the PET/CT-based DL model developed on the basis of the segmentation model was 0.74, with an accuracy, sensitivity, and specificity of 0.72, 0.73, and 0.72, respectively. After integrating key clinical data, the AUC of the model improved to 0.77, with its accuracy, sensitivity, and specificity boosted to 0.75, 0.77, and 0.73, respectively. Conclusion: To the best of our knowledge, this is the first deep learning model to end-to-end predict the pathological grading of PDAC in a fully automatic manner, which is expected to improve clinical decision-making. [ABSTRACT FROM AUTHOR]

Abstract There is significant variability with respect to the prognosis of nonmetastatic clear cell renal cell carcinoma (ccRCC) patients with venous tumor thrombus (VTT). By applying multiregion whole‐exome sequencing on normal‐tumor‐thrombus‐metastasis quadruples from 33 ccRCC patients, we showed that metastases were mainly seeded by VTT (81.8%) rather than primary tumors (PTs). A total of 706 nonmetastatic ccRCC patients with VTT from three independent cohorts were included in this study. C‐index analysis revealed that pathological grading of VTT outperformed other indicators in risk assessment (OS: 0.663 versus 0.501–0.610, 0.667 versus 0.544–0.651, and 0.719 versus 0.511–0.700 for Training, China‐Validation, and Poland‐Validation cohorts, respectively). We constructed a risk predicting model, TT‐GPS score, based on four independent variables: VTT height, VTT grading, perinephric fat invasion, and sarcomatoid differentiation in PT. The TT‐GPS score displayed better discriminatory ability (OS, c‐index: 0.706–0.840, AUC: 0.788–0.874; DFS, c‐index: 0.691–0.717, AUC: 0.771–0.789) than previously reported models in risk assessment. In conclusion, we identified for the first‐time pathological grading of VTT as an unheeded prognostic factor. By incorporating VTT grading, the TT‐GPS score is a promising prognostic tool in predicting the survival of nonmetastatic ccRCC patients with VTT.

Purpose: To investigate the value of pre-operative gadoxetate disodium (Gd-EOB-DTPA) enhanced MRI predicting early post-operative recurrence (< 2 years) of hepatocellular carcinoma (HCC) with different degrees of pathological differentiation. Methods: Retrospective analysis of pre-operative MR imaging features of 177 patients diagnosed as suffering from HCC and that underwent radical resection. Multivariate logistic regression assessment was adopted to assess predictors for HCC recurrence with different degrees of pathological differentiation. The area under the curve (AUC) of receiver operating characteristics (ROC) was utilized to assess the diagnostic efficacy of the predictors. Results: Among the 177 patients, 155 (87.5%) were males, 22 (12.5%) were females; the mean age was 49.97 ± 10.71 years. Among the predictors of early post-operative recurrence of highly-differentiated HCC were an unsmooth tumor margin and an incomplete/without tumor capsule (p = 0.037 and 0.033, respectively) whereas those of early post-operative recurrence of moderately-differentiated HCC were incomplete/without tumor capsule, peritumoral enhancement along with peritumoral hypointensity (p = 0.006, 0.046 and 0.004, respectively). The predictors of early post-operative recurrence of poorly-differentiated HCC were peritumoral enhancement, peritumoral hypointensity, and tumor thrombosis (p = 0.033, 0.006 and 0.021, respectively). The AUCs of the multi-predictor diagnosis of early post-operative recurrence of highly-, moderately-, and poorly-differentiated HCC were 0.841, 0.873, and 0.875, respectively. The AUCs of the multi-predictor diagnosis were each higher than for those predicted separately. Conclusions: The imaging parameters for predicting early post-operative recurrence of HCC with different degrees of pathological differentiation were different and combining these predictors can improve the diagnostic efficacy of early post-operative HCC recurrence. [ABSTRACT FROM AUTHOR]

Bladder cancer (BC), a urologic disease, commonly occurs globally and is very invasive. Patients with invasive BC have low 5-year survival rate. Hence, the mechanisms underlying BC development and progression should be elucidated. MicroRNAs (miRNAs), as common noncoding RNAs, are receiving increasing attention because of their biological functions. The irregular expression patterns of miRNAs are linked to BC occurrence; therefore, determining the functions of miRNAs in abnormally expressed BC tissues might enable to elucidate the pathogenetic mechanism of BC and offer new markers for the prognosis, diagnosis, and therapy of BC. Here, we consolidate the primary roles of miRNAs with atypical expression in BC development as well as their association with BC pathological grades and chemotherapy resistance development in patients with BC. [ABSTRACT FROM AUTHOR]

• We propose MamlFormer for effective multimodal fusion of LSCC high and low magnification image models. • The manifold block eliminates the redundancy of feature information caused by the background semantics. Eventually, it improves the consistency of LSCC high and low magnification image modalities in the multimodal model. • The adversarial block achieves adaptive learning of a latent metric for the modal distribution of LSCC high- and low-magnification images. Hereby, it enhances the complementarity of the LSCC high- and low-magnification image modalities. Pathologic grading of laryngeal squamous cell carcinoma (LSCC) plays a crucial role in diagnosis, prognosis, and migration. However, the grading performance and interpretability of the intelligent grading model based on LSCC low magnification images are poor. This is because it lacks the delicate nuclear information and information more relevant to grading contained in the high magnification images labeled by pathologists. Yet, low magnification images have information such as tissue texture and contours. Thus, we proposed an end-to-end transformer network with manifold adversarial multi-modal learning (MamlFormer). It effectively fuses and learns LSCC high and low magnification pathology image modalities. Firstly, we demonstrate the feasibility and sufficient conditions for modal fusion of LSCC high and low magnification images from Hoeffding's inequality and multimodal co-regularization. Secondly, we design a new manifold block. It constructs the manifold subspace by some principles. Those principles are divisibility, recoverability, and local distance closest of the feature matrix before and after the mapping of the LSCC each magnification image modalities. Meanwhile it can well solve the problems of redundant feature matrix information and weak modal semantic consistency after multimodal learning. Thirdly, we utilize the encoder and the adversarial loss function to implement adversarial block. It can adaptively learn the latent metrics of the modal distributions of LSCC high and low magnification images. Therefore, it also enhances the complementarity of LSCC high and low magnification image modalities. Then, numerous experiments show that MamlFormer outperforms other SOTA models in both grading performance and interpretability. Finally, we also performed generalization experiments on highly prevalent cervix squamous cell carcinoma. The MamlFormer over is superior to other SOTA models in terms of grading performance and interpretability. This indicates its excellent generalization performance and clinical practicability. [ABSTRACT FROM AUTHOR]

PurposeTo evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics.Materials and MethodsA retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model.ResultOur analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively.ConclusionIn conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect.

Purpose: To evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics. Materials and Methods: A retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model. Result: Our analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively. Conclusion: In conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect. [ABSTRACT FROM AUTHOR]

PurposeTo assess the utility of texture analysis for predicting the pathological degree of differentiation of pancreatic carcinoma (PC).MethodsEighty-three patients with PC who went through postoperative pathology diagnose and CT examination were selected at Anhui Provincial Hospital. Among them, 34 cases were moderately differentiated, 13 cases were poorly differentiated, and 36 cases were moderately poorly differentiated. The images in the arterial and venous phase (VP) with the lesions at their largest cross section were selected to manually outline the region of interest (ROI) to delineate lesions using open-source software. A total of 396 features were extracted from the ROI using AK software. Spearman correlation analysis and random forest selection by filter (rfSBF) in the caret package of R studio were used to select the discriminating features. The receiver operating characteristic ROC analysis was used to evaluate their discriminative performance.ResultsTwelve and six features were selected in the arterial and VPs, respectively. The areas under the ROC curve (AUC) in the arterial phase (AP) for diagnosing poorly differentiated, moderately differentiated and moderate-poorly differentiated cases were 0.80, 1, and 0.80 in the training group and 0.77, 1, and 0.77 in the test group; in the VP, the values were 0.81, 1, and 0.82 in the training group and 0.74, 1, and 0.74 in the test group.ConclusionTexture analysis based on contrast-enhanced CT images can be used as an adjunct for the preoperative assessment of the pathological degrees of differentiation of PC.

Background/Aims: There is an increasing risk of end-stage renal disease (ESRD) among Asian people with immunoglobulin A nephropathy (IgAN). A computer-aided system for ESRD prediction in Asian IgAN patients has not been well studied. Methods: We retrospectively reviewed biopsy-proven IgAN patients treated at the Department of Nephrology of the Second Xiangya Hospital from January 2009 to November 2013. Demographic and clinicopathological data were obtained within 1 month of renal biopsy. A random forest (RF) model was employed to predict the ESRD status in IgAN patients. All cases were initially trained and validated, taking advantage of the out-of-bagging(OOB) error. Predictors used in the model were selected according to the Gini impurity index in the RF model and verified by logistic regression analysis. The area under the receiver operating characteristic(ROC) curve (AUC) and F-measure were used to evaluate the RF model. Results: A total of 262 IgAN patients were enrolled in this study with a median follow-up time of 4.66 years. The importance rankings of predictors of ESRD in the RF model were first obtained, indicating some of the most important predictors. Logistic regression also showed that these factors were statistically associated with ESRD status. We first trained an initial RF model using gender, age, hypertension, serum creatinine, 24-hour proteinuria and histological grading suggested by the Clinical Decision Support System for IgAN (CDSS, www.IgAN.net). This 6-predictor model achieved a F-measure of 0.8 and an AUC of 92.57%. By adding Oxford-MEST scores, this model outperformed the initial model with an improved AUC (96.1%) and F-measure (0.823). When C3 staining was incorporated, the AUC was 97.29% and F-measure increased to 0.83. Adding the estimated glomerular filtration rate (eGFR) improved the AUC to 95.45%. We also observed improved performance of the model with additional inputs of blood urea nitrogen (BUN), uric acid, hemoglobin and albumin. Conclusion: In addition to the predictors in the CDSS, Oxford-MEST scores, C3 staining and eGFR conveyed additional information for ESRD prediction in Chinese IgAN patients using a RF model.

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Copyright of Journal of China Medical University is the property of Journal of China Medical University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background/Aims: There is an increasing risk of end-stage renal disease (ESRD) among Asian people with immunoglobulin A nephropathy (IgAN). A computer-aided system for ESRD prediction in Asian IgAN patients has not been well studied. Methods: We retrospectively reviewed biopsy-proven IgAN patients treated at the Department of Nephrology of the Second Xiangya Hospital from January 2009 to November 2013. Demographic and clinicopathological data were obtained within 1 month of renal biopsy. A random forest (RF) model was employed to predict the ESRD status in IgAN patients. All cases were initially trained and validated, taking advantage of the out-of-bagging(OOB) error. Predictors used in the model were selected according to the Gini impurity index in the RF model and verified by logistic regression analysis. The area under the receiver operating characteristic(ROC) curve (AUC) and F-measure were used to evaluate the RF model. Results: A total of 262 IgAN patients were enrolled in this study with a median follow-up time of 4.66 years. The importance rankings of predictors of ESRD in the RF model were first obtained, indicating some of the most important predictors. Logistic regression also showed that these factors were statistically associated with ESRD status. We first trained an initial RF model using gender, age, hypertension, serum creatinine, 24-hour proteinuria and histological grading suggested by the Clinical Decision Support System for IgAN (CDSS, www.IgAN.net). This 6-predictor model achieved a F-measure of 0.8 and an AUC of 92.57%. By adding Oxford-MEST scores, this model outperformed the initial model with an improved AUC (96.1%) and F-measure (0.823). When C3 staining was incorporated, the AUC was 97.29% and F-measure increased to 0.83. Adding the estimated glomerular filtration rate (eGFR) improved the AUC to 95.45%. We also observed improved performance of the model with additional inputs of blood urea nitrogen (BUN), uric acid, hemoglobin and albumin. Conclusion: In addition to the predictors in the CDSS, Oxford-MEST scores, C3 staining and eGFR conveyed additional information for ESRD prediction in Chinese IgAN patients using a RF model. [ABSTRACT FROM AUTHOR]

Objective To explore the diffusion and kurtosis features of cervical cancer (CC) and study the feasibility of diffusion kurtosis imaging (DKI) based on the non-Gaussian diffusion-weighted model to differentiate the stage and grade of CC. Methods A total of 50 patients with pathologically confirmed CC were enrolled. MRI examinations including DKI (with 5b values 200, 500, 1000, 1500, and 2000smm − 2 were performed before any treatment. The apparent coefficient (D app ) and the apparent kurtosis value (K app ) were derived from the non-gaussian diffusion model, and the apparent diffusion coefficient (ADC) was derived from the Gaussian model. The parameters of CC and normal tissue (myometrium) were obtained, analyzed statistically, and evaluated with respect to differentiating stage and grade between the tissue and the CC. Results ADC and D app values of CC were significantly lower than that of the normal myometrium (P = 0.024 and P < 0.001, respectively), while the K app value was not found to exhibit a significant difference. Compared to the well/moderately differentiated CC, poorly differentiated CC had a significantly decreased mean ADC and D app (P = 0.018 and P = 0.026, respectively); however, the mean K app (P = 0.035) increased significantly. In the clinical staging, the DKI sequence was advantageous over conventional MRI sequences (degree of accuracy: 90% vs . 74%), Although in the quantitative analysis, these parameters did not show a significant difference. Conclusions The pilot study demonstrated that these diffusion and kurtosis indices from DKI based on the non-Gaussian diffusion-weighted model putatively differentiated the grade and stage of CC. [ABSTRACT FROM AUTHOR]

Objectives: The Gleason score (GS) is the primary classification of clinical risk in prostate cancer (PCa). Here, we estimated the factors predictive of accordance of local and central pathologist-dependent GS and clinical risk classification in an increased number of cases. Methods: Between January 2009 and December 2013, 388 patients were diagnosed with PCa by 80 independent pathologists from local communities and were referred to our hospital. Validation of the GS with needle-core biopsy specimens was carried out by a single central pathologist, and clinical risk, according to the D'Amico risk classification, was determined. Discrepancies between the GS and risk classification, based on the GS estimated by the local or central pathologist, were reviewed, and predictive factors for accordance of clinical risk classification were estimated. Results: When pathological results were compared, 59.5% of cases were given concordant GSs by local and central pathologists. A significant discrepancy existed in the classification of intermediate risk (p < 0.0001). Multivariate analysis indicated that local pathologist-dependent GS7, lower prostate-specific antigen (≤10 ng/ml), and lower T stage (T1 or T2a) were significant predictive factors for discordance with the central pathologist-dependent risk classification. Conclusion: Review of bioptic GSs by central pathologists affected discrepancies in risk classification in patients with PCa. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Purpose: To evaluate dynamic contrast-enhanced computed tomography (CT) features of pancreatic neuroendocrine tumors (PNETs) containing areas of iso- or hypoattenuation and the relationship with pathological grading.Materials and Methods: Between June 2006 and March 2014, 61 PNETs in 58 consecutive patients (29 male, 29 female; median-age 55 years), which were surgically diagnosed, underwent preoperative dynamic contrast-enhanced CT. PNETs were classified based on contrast enhancement patterns in the pancreatic phase: iso/hypo-PNETs were defined as tumors containing areas of iso- or hypoattenuation except for cystic components, and hyper-PNETs were tumors showing hyperattenuation over the whole area. CT findings and contrast-enhancement patterns of the tumors were evaluated retrospectively by two radiologists and compared with the pathological grading.Results: Iso/hypo-PNETs comprised 26 tumors, and hyper-PNETs comprised 35 tumors. Not only hyper-PNETs but also most iso/hypo-PNETs showed peak enhancement in the pancreatic phase and a washout from the portal venous phase to the delayed phase. Iso/hypo-PNETs showed larger tumor size than the hyper-PNETs (mean, 3.7 cm vs. 1.6 cm; P<0.001), and were significantly correlated with unclear tumor margins (n=4 vs. n=0; P=0.029), the existence of cystic components (n=10 vs. n=3; P=0.006), intratumoral blood vessels in the early arterial phase (n=13 vs. n=3; P<0.001), and a smooth rim enhancement in the delayed phase (n=12 vs. n=6; P=0.019). Iso/hypo-PNETs also showed significantly higher pathological grading (WHO 2010 classification; iso/hypo, G1=14, G2=11, G3=1; hyper, G1=34, G2=1; P<0.001).Conclusion: PNETs containing iso/hypo-areas showed a rapid enhancement pattern as well as hyper-PNETs, various radiological features and higher malignant potential. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to investigate the association of the Laennec staging system with degree of cirrhosis, clinical stage and liver function. Methods: Liver biopsy was performed for 30 patients with hepatitis B cirrhosis to test the content of hydroxyproline in hepatic tissue, judge the degree of cirrhosis and determine the Laennec staging system. The association of the Laennec staging system with the degree of cirrhosis, clinical stage and liver function was compared. Results: The Laennec staging system had a close association with clinical stage, model for end-stage liver disease score and degree of cirrhosis ( r = 0.58, p < 0.01; r = 0.60, p < 0.01; r = 0.53, p < 0.01). Conclusions: The Laennec histological grading system can to some extent reflect the degree of cirrhosis, clinical stage and liver function, and is expected to predict the incidence of patient complications in a useful way. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the value of quantitative 3-Tesla (3T) magnetic resonance (MR) assessment in the diagnosis of breast lesions. A total of 44 patients with breast lesions were selected. All the patients underwent MR plain scanning and T1 dynamic contrast-enhanced imaging. The vascular function parameters of the lesions, namely volume transfer constant (Ktrans), rate constant (Kep), extravascular extracellular volume fraction (Ve) and integrated area under the curve (iAUC), were acquired. These parameters were compared between benign and malignant breast lesions, and also among differential grades of invasive ductal carcinoma. The values of Ktrans, Kep and iAUC were significantly different between the benign and malignant tumors; however, the values of Ve in the benign and malignant tumors were not significantly different. The values of Ktrans, Kep and iAUC in invasive ductal carcinoma were significantly different between grade I and grade II, and between grade I and grade III; however, there was no significant difference between grade II and grade III. The Ve values in invasive ductal carcinoma did not significantly differ among grades I, II and III. Among the vascular function parameters, Ktrans exhibited the highest sensitivity and specificity in the differentiation of benign and malignant lesions. Quantitative 3-T MR assessment is valuable in the diagnosis of benign and malignant breast lesions. It can also provide reference values for the differentiation of the histological grade of breast invasive ductal carcinoma. [ABSTRACT FROM AUTHOR]

Introduction: Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images). To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG) and high-grade glioma (HGG) which represent a more advanced stage of the disease. Results: We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1) A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2) a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP). In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1) Successful detection rates of pseudopalisading necrosis and MVP regions, (2) overall classification accuracy into LGG and HGG categories, and (3) receiver operating characteristic curves which can facilitate a desirable trade-off between HGG detection and false-alarm rates. Conclusion: The proposed method demonstrates fairly high accuracy and compares favorably against best-known alternatives such as the state-of-the-art WND-CHARM feature set provided by NIH combined with powerful support vector machine classifier. Our results reveal that the proposed method can be beneficial to a clinician in effectively separating histopathology slides into LGG and HGG categories, particularly where the analysis of a large number of slides is needed. Our work also reveals that MVP regions are much harder to detect than Pseudopalisading Necrosis and increasing accuracy of automated image processing for MVP detection emerges as a significant future research direction. [ABSTRACT FROM AUTHOR]

The morbidity and mortality of hepatocellular carcinoma (HCC) rank 6 th and 4 th , respectively, among malignant tumors worldwide. Traditional diffusion-weighted imaging (DWI) uses the apparent diffusion coefficient (ADC) obtained by applying the monoexponential model to reflect water molecule diffusion in active tissue; however, the value of ADC is affected by microcirculation perfusion. Using a biexponential model, intravoxel incoherent motion (IVIM)-DWI quantitatively measures information related to pure water molecule diffusion and microcirculation perfusion, thus compensating for the shortcomings of DWI. The number of studies examining the application of IVIM-DWI in patients with HCC has gradually increased over the last few years, and many results show that IVIM-DWI has vital value for HCC differentiation, pathological grading, and predicting and evaluating the treatment response. The present study principally reviews the principle of IVIM-DWI and its research progress in HCC differentiation, pathological grading, predicting and evaluating the treatment response, predicting postoperative recurrence and predicting gene expression prediction., Competing Interests: Conflict-of-interest statement: The authors have no conflicts of interest related to this article to declare., (©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.)

Background: Glioma is a common primary craniocerebral malignant tumor, due to the lack of specificity of imaging examination and clinical manifestations, its diagnostic accuracy is relatively low, which may result in misdiagnosis and missed diagnosis. The apparent diffusion coefficient (ADC) in magnetic resonance diffusion weighted imaging (DWI) can reflect the histological characteristics of gliomas, which can be widely applied to classify gliomas and evaluate the extent of metastasis of glioma. The present study aimed to assess the clinical value of magnetic resonance DWI in the pathological grading of glioma and its therapeutic application in clinical surgery., Methods: This article retrospectively analyzed the clinical data of 41 patients with glioma confirmed by surgical pathology results from January 1, 2019 to March 31, 2020 in the People's Hospital of Gaozhou. Among them, 16 patients had low-grade gliomas [World Health Organization (WHO) grade I-II] and 25 patients had high-grade gliomas (WHO grade III-IV). They were subjected to conventional T1WI and T2WI plain scans, along with DWI and enhanced scans before surgery. The ADC values of the glioma parenchyma, the peritumoral edema area, the surrounding white matter, and the contralateral normal white matter were measured. We selected some tumor tissues for pathological analysis as well, and conducted pathological grading according to WHO grading standards., Results: We compared and evaluated the ADC values of the observed areas for low-grade gliomas and high-grade gliomas. The ADC values of low-grade gliomas in the tumor parenchyma, peritumoral edema, and white matter around the edema area were significantly lower than those of high-grade gliomas, and the differences were statistically significant (P<0.05). The difference in ADC values of normal white matter between the two groups of patients was not statistically significant (P=0.125)., Conclusions: DWI has prognostic predictive value in the preoperative differential diagnosis and pathological classification of gliomas. This advanced technology can verify the extent of glioma infiltration in the surrounding brain tissue. It can help clinicians formulate a safer and more effective therapeutic strategy by providing accurate information on prognostic evaluation before the successful surgical intervention of gliomas., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-114/coif). The authors have no conflicts of interest to declare., (2022 Translational Cancer Research. All rights reserved.)

Purpose: The present study investigated the value of ultrasomics signatures in the preoperative prediction of the pathological grading of hepatocellular carcinoma (HCC) via machine learning.Methods: A total of 193 patients were collected from three hospitals. The patients from two hospitals (n = 160) were randomly divided into training set (n = 128) and test set (n = 32) at a 8:2 ratio. The patients from a third hospital were used as an independent validation set (n = 33). The ultrasomics features were extracted from the tumor lesions on the ultrasound images. Support vector machine (SVM) was used to construct three preoperative pathological grading models for HCC on each dataset. The performance of the three models was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.Results: The ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between high- and low-grade HCC lesions on the training set, test set, and the independent validation set (p < 0.05). On the test set and the validation set, the combined model's performance was the highest, followed by the ultrasomics model and the clinical model successively (p < 0.05). Their AUC (along with 95 %CI) of these models was 0.874(0.709-0.964), 0.789(0.608-0.912), 0.720(0.534-0.863) and 0.849(0.682-0.949), 0.825(0.654-0.935), 0.770(0.591-0.898), respectively.Conclusion: Machine learning-based ultrasomics signatures could be used for noninvasive preoperative prediction of pathological grading of HCC. The combined model displayed a better predictive performance for pathological grading of HCC and had a stronger generalization ability. [ABSTRACT FROM AUTHOR]

Developing a tissue diagnosis technology to avoid the complicated processes and the usage of expensive reagents while achieving rapid pathological grading diagnosis to provide a better strategy for clinical treatment is an important strategy of tumor diagnose. Herein, we selected the integrin αvβ3 as target based on the analysis of clinical data, and then designed a stable and cancer-targeted selenium nanosystem (RGD@SeNPs) by using RGD polypeptide as the targeting modifier. In vitro experiments showed that RGD@SeNPs could specifically recognized tumor cells, especially in co-culture cells model. The RGD@SeNPs can be used for clinical samples staining without the use of primary and secondary antibody. Fluorescence difference of the tissue specimens staining with RGD@SeNPs could be used to distinguish normal tissues and tumor tissues or estimate different pathological grades of cancer at tissue level. 132 clinical tumor specimens with three types of tumor and 76 non-tumor specimens were examined which verified that the nanoparticles could specific and sensitive distinguish tumor tissue from normal tissue with a specificity of 92% and sensitivity of 96%. These results demonstrate the potential of cancer-targeted RGD@SeNPs as translational nanodiagnostics for rapid visualizing and pathological grading of bladder tumor tissues in clinical specimens. Image 1 [ABSTRACT FROM AUTHOR]

Background: Pseudomyxoma peritonei (PMP) is an extremely rare malignancy, characterized by extensive peritoneal implantation and colloidal ascites. This study was to explore the pathological prognostic factors of PMP., Methods: Specimens from 155 PMP patients were analyzed by H&E and immunohistochemistry. Parameters included primary tumor location, histological grade, lymph node metastasis, tumor emboli in the blood and lymph vessels, perineural invasion, Ki67 labeling index, p53, mismatch repair (MMR) gene mutations, MUC1, MUC2, MUC5AC, and MUC6. Clinicopathological and follow-up data were subjected to univariate and multivariate analyses., Results: The patients included 63.2% (n = 98) low-grade mucinous carcinoma peritonei, 31.6% (n = 49) high-grade mucinous carcinoma peritonei and 5.2% (n = 8) high-grade mucinous carcinoma peritonei with signet ring cells. There were 9.7% (n = 15) with lymph node metastasis; 11.6% (n = 18) with angiolymphatic invasion; 6.3% (n = 8) with defective MMR (dMMR); 35.5% (n = 55) with Ki67 labeling index ≥ 50%; 36.1% (n = 56) with p53 mutation. For PMP from appendiceal origin (n = 140), univariate analysis identified 10 potential prognostic factors. But Multivariate analysis identified only histologic grade was the independent prognostic factor for OS. Mortality risk of high-grade peritoneal mucinous carcinoma or high-grade peritoneal mucinous carcinoma with signet ring cells was 7.056 times (P < .0001, 95% CI: 2.701-18.435) or 27.224 times (P < .0001, 95% CI: 6.207-119.408), respectively, higher than low-grade., Conclusions: For PMP from the appendiceal origin, histological grade could be the only independent prognostic factor., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective To evaluate the role of the Gleason score of needle biopsies of the prostate in predicting the final pathological staging of patients with carcinoma of the prostate treated by radical prostatectomy. Patients and methods The records of 466 patients with carcinoma of the prostate treated by radical prostatectomy were reviewed, comparing the Gleason scores of the core-needle biopsies with the Gleason score and final pathological staging of the surgical specimens. Results The biopsy grade was the same as that of the prostatectomy specimen in 54% of the patients. The most common discordance was the upgrading of well-differentiated tumours in 75% of the patients. When the biopsy grade was compared with the surgical pathological stage, 49% of low- and 82% of high-grade lesions in the biopsy had capsular penetration by tumour or locally advanced disease (Stage C and D1). Conclusion Well-differentiated tumours on the biopsy core are not predictive of organ-confined disease, but a poorly differentiated lesion is a good indicator of extracapsular extension of the cancer. [ABSTRACT FROM AUTHOR]

In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: n = 125; validation dataset, n = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.

Objective: To investigate the value of spectral computed tomography quantitative parameters in the assessment of pathological grade of esophageal squamous cell carcinoma before operation. Methods: The imaging findings of 52 patients with confirmed esophageal squamous cell carcinoma by surgery and pathology were prospectively analyzed in Henan Provincial People's Hospital from June 2016 to May 2017.There were 43 males and 9 females, aged 49-76 years, with an average age of (66±8) years.All the patients were divided into three groups based on the pathological finding: well-differentiated group ( n =12), moderately-differentiated group ( n =20), poorly-differentiated group ( n =20). All the patients received chest plain scan and double phase enhanced scan of gemstone spectral computed tomography.The enhancement attenuation (HU), the average of the slope of the spectral Hounsfield Unit curve (λ(HU)), normalized iodine concentration (NIC), normalized effective atomic number (Z(eff-a)) were measured and calculated.The difference in HU, λ(HU), NIC, Z(eff-a) among different grades were statistically analyzed.The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of single and combined parameters in the differentiation of poorly-differentiated and well-moderately differentiated esophageal squamous cell carcinoma. Results: There were significant differences in HU, λ(HU), NIC, Z(eff-a) among different pathological grading of the esophageal squamous cell carcinoma in arterial phase and venous phase ( F =4.496-9.056, H =23.204, 20.724, all P <0.05). The best single parameter to differentiate poorly-differentiated from well-moderately differentiated esophageal squamous cell carcinoma was NIC in arterial phase with areas under the ROC curve (AUC), the cutoff value, sensitivity, specificity, accuracy of 0.860, 0.197, 65.0%, 96.9%, 84.6%, respectively; the best combination of parameters was HU+ NIC+ λ(HU) in arterial phase with AUC, the threshold of predicted probability, sensitivity, specificity, accuracy of 0.913, 0.380, 85.0%, 81.3%, 82.7%, respectively. Conclusion: Gemstone spectral imaging quantitative parameters can be used to evaluate the pathological grading of esophageal squamous cell carcinoma, the NIC and HU+ NIC+ λ(HU) in arterial phase have the highest differential diagnostic efficiency.