Development and validation of a prognostic model incorporating tumor thrombus grading for nonmetastatic clear cell renal cell carcinoma with tumor thrombus: A multicohort study

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.

Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient's Pathological Grades

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.

Predictive Value of the Texture Analysis of Enhanced Computed Tomographic Images for Preoperative Pancreatic Carcinoma Differentiation

Purpose

To assess the utility of texture analysis for predicting the pathological degree of differentiation of pancreatic carcinoma (PC).

Methods

Eighty-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.

Results

Twelve 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.

Conclusion

Texture 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.

[Quantitative analysis of hepatocellular carcinomas pathological grading in non-contrast magnetic resonance images]

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.

Automated discrimination of lower and higher grade gliomas based on histopathological image analysis

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.

Quantitative analysis of 3-Tesla magnetic resonance imaging in the differential diagnosis of breast lesions

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.