Updates in Histologic Grading of Urologic Neoplasms

Evaluation of Fibroblast Growth Factor Receptor 3 (FGFR3) and Tumor Protein P53 (TP53) as Independent Prognostic Biomarkers in High-Grade Non-muscle Invasive Bladder Cancer

Introduction Bladder cancer is a significant health issue with an increased recurrence and progression rate, requiring invasive follow-up, which shows a poor prognosis. In addition, the prognostic role of mutant fibroblast growth factor receptor 3 (FGFR3) and tumor protein P53 (TP53) is controversial; therefore, we investigated the methylation status and their altered gene expression in low- and high-grade non-muscle-invasive bladder cancer (NMIBC) subjects. Materials and methods This case-control study was conducted between 2020 and 2023, in which n = 115 tumor tissues (NMIBC n = 85) and (controls n = 30) were examined for FGFR3 and FGFR promoter methylation and expression using methylation-specific PCR (MSP) and real-time PCR. The multivariate regression analysis and Kaplan-Meier (KM) plots were used to establish the association of FGFR3 and TP53 with clinicopathological features and survival outcomes of NMIBC patients. Results High-grade NMIBC tumors showed substantial methylation patterns, with TP53 hypomethylated (p = 0.034) and FGFR3 hypermethylated (p = 0.046), as well as significant mRNA expression of Tp53 and FGFR3 (p = 0.001). The multivariate analysis shows FGFR3 and Tp53 were associated with recurrence-free survival with sensitivity (p = 0.045 (78%); 0.034 (70.7%)) and progression-free survival (p = 0.022(61.5%); 0.038 (69.2%)).  Conclusion The findings of this investigation indicate that FGFR3 hypermethylation and TP53 hypomethylation are independent prognostic indicators that aid in the evaluation of disease outcomes in high-grade NMIBC tumors.

Multi-Scale Digital Pathology Patch-Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset

Prostate cancer remains a prevalent health concern, emphasizing the critical need for early diagnosis and precise treatment strategies to mitigate mortality rates. The accurate prediction of cancer grade is paramount for timely interventions. This paper introduces an approach to prostate cancer grading, framing it as a classification problem. Leveraging ResNet models on multi-scale patch-level digital pathology and the Diagset dataset, the proposed method demonstrates notable success, achieving an accuracy of 0.999 in identifying clinically significant prostate cancer. The study contributes to the evolving landscape of cancer diagnostics, offering a promising avenue for improved grading accuracy and, consequently, more effective treatment planning. By integrating innovative deep learning techniques with comprehensive datasets, our approach represents a step forward in the pursuit of personalized and targeted cancer care.

PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study

PURPOSE

No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction.

METHODS

A total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups.

RESULTS

The DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS).

CONCLUSION

The DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.

Intravoxel Incoherent Motion Diffusion-Weighted MR Imaging and Venous Tumor Thrombus Consistency in Renal Cell Carcinoma

BACKGROUND

Venous tumor thrombus (VTT) consistency of renal cell carcinoma (RCC) is an important consideration in nephrectomy plus thrombectomy. However, evaluation of VTT consistency through preoperative MR imaging is lacking.

PURPOSE

To evaluate VTT consistency of RCC through intravoxel incoherent motion-diffusion weighted imaging (IVIM-DWI) derived parameters (Dt , Dp , f, and ADC) and the apparent diffusion coefficient (ADC) value.

STUDY TYPE

Retrospective.

POPULATION

One hundred and nineteen patients (aged 55.8 ± 11.5 years, 85 male) with histologically-proven RCC and VTT who underwent radical resection.

FIELD STRENGTH/SEQUENCES

3.0-T; two-dimensional single-shot diffusion-weighted echo planar imaging sequence at 9 b-values (0-800 s/mm2 ).

ASSESSMENT

IVIM parameters and ADC values of the primary tumor and the VTT were calculated. The VTT consistency (friable vs. solid) was determined through intraoperative findings of two urologists. The accuracy of VTT consistency classification based on the individual IVIM parameters of primary tumors and of VTT, and based on models combining parameters, was assessed. Type of operation, intra-operative blood loss, and operation length were recorded.

STATISTICAL TESTS

Shapiro-Wilk test; Mann-Whitney U test; Student's t-test; Chi-square test; Receiver operating characteristic (ROC) analysis. Statistical significance level was P < 0.05.

RESULTS

Of the enrolled 119 patients, 33 patients (27.7%) had friable VTT. Patients with friable VTT were significantly more likely to experience open surgery, have significantly more intraoperative blood loss, and significantly longer operative duration. The area under the ROC curve (AUC) values of Dt of the primary tumor and VTT in classifying VTT consistency were 0.758 (95% CI 0.671-0.832) and 0.712 (95% CI 0.622-0.792), respectively. The AUC value of the model combining Dp and Dt of VTT was 0.800 (95% CI 0.717-0.868). Furthermore, the AUC of the model combining Dp and Dt of VTT and Dt of the primary tumor was 0.886 (95% CI 0.814-0.937).

CONCLUSION

IVIM-derived parameters had the potential to predict VTT consistency of RCC.

EVIDENCE LEVEL

3 Technical Efficacy: Stage 2.

The Metastasis Pattern of Renal Cell Carcinoma Is Influenced by Histologic Subtype, Grade, and Sarcomatoid Differentiation

Background and Objectives: Metastasis is a major cause of death in renal cell carcinoma (RCC) patients; therefore, a better understanding of the metastatic process and the ability to predict metastasis in advance is important for treating patients with RCC. This study aimed to investigate whether histological subtypes of RCC and other factors, such as nuclear grade and sarcomatoid differentiation, could predict the probability and location of metastases in patients with RCC. Materials and Methods: Cases of clear-cell, papillary, chromophobe, and sarcomatoid RCC were retrieved and analyzed from the Surveillance, Epidemiology, and End Results databases. Results: When comparing the metastatic patterns among the three histologic subtypes, patients with clear-cell RCC were significantly more likely to have brain and lung metastases. Moreover, patients with papillary RCC were significantly less likely to develop bone metastases and more likely to develop lymph node metastases. Patients with chromophobe RCC are significantly more likely to develop liver metastases. As the nuclear grade increased, there was also a significantly increased tendency for clear-cell RCC to metastasize to the lungs. Patients with sarcomatoid RCC had a higher rate of metastasis, with a significantly higher probability of metastasis to the bone and lungs, than those with all three histological subtypes did. Conclusions: Histological subtype, nuclear grade, and sarcomatoid differentiation were significant predictors of metastasis in patients with RCC.

A CT-Based Radiomics Nomogram Combined with Clinic-Radiological Characteristics for Preoperative Prediction of the Novel IASLC Grading of Invasive Pulmonary Adenocarcinoma

RATIONALE AND OBJECTIVES

The novel International Association for the Study of Lung Cancer (IASLC) grading system of invasive lung adenocarcinoma (ADC) demonstrated a remarkable prognostic effect and enabled numerous patients to benefit from adjuvant chemotherapy. We sought to build a CT-based nomogram for preoperative prediction of the IASLC grading.

MATERIALS AND METHODS

This work retrospectively analyzed the CT images and clinical data of 303 patients with pathologically confirmed invasive ADC. The histological subtypes and radiological characteristics of the patients were re-evaluated. Radiomics features were extracted, and the optimal subset of features was established by ANOVA, spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses identified the independent clinical and radiological variables. Finally, multivariate logistic regression analysis incorporated clinical, radiological, and optimal radiomics features into the nomogram. Receiver operating characteristic (ROC) curve, and accuracy were applied to assess the model's performance. Decision curve analysis (DCA), and calibration curve were applied to assess the clinical usefulness.

RESULTS

Nine selected CT image features were used to develop the radiomics model. The accuracy, precision, sensitivity, and specificity of the radiomics model outperformed the clinic-radiological model in the training and testing sets. Integrating Radscore with independent radiological characteristics showed higher prediction performance than clinic-radiological characteristics alone in the training (AUC, 0.915 vs. 0.882; DeLong, p < 0.05) and testing (AUC, 0.838 vs. 0.782; DeLong, p < 0.05) sets. Good calibration and decision curve analysis demonstrated the clinical usefulness of the nomogram.

CONCLUSION

Radiomics features effectively predict high-grade ADC. The combined nomogram may facilitate selecting patients who benefit from adjuvant treatment.

The prognostic impact of lung adenocarcinoma predominance classification relating to pathological factors in lobectomy, the Japanese Joint Committee of Lung Cancer Registry Database in 2010

Objective

We studied the prognosis and clinicopathological background of lung adenocarcinoma predominance among patients who underwent lobectomy using data from the Japanese Joint Committee of Lung Cancer Registry.

Methods

Two thousand eight hundred sixty-three cases were extracted. Recurrence free survival (RFS) rates, overall survival (OS) rates and clinicopathological factors and epidermal growth factor receptor (EGFR) mutation status were examined.

Results

Median follow-up period was 65.5 months. Adenocarcinoma predominance was sub-grouped according to OS and RFS rate. In pathological stage I, 5-year RFS and OS rates were respectively 92.2% and 95.8% in group A (adenocarcinoma-in-situ + minimally invasive adenocarcinoma), 89.3% and 92.1% in group B (lepidic), 79.2% and 89.7% in group C (papillary + acinar + variants) and 69.0% and 79.0% in group D (solid + micropapillary). In pathological stage II + IIIA, they were, 43.6% and 72.4% in B, 39.5% and 66.9% in C and 31.0% and 53.7% in D. Group D showed significant worst outcome both in stage I and II + IIIA. Up stage rate from clinical stage I to pathological stage II + IIIA was 0.0%, 3.7%, 15.9% and 33.3%. The frequency of lymph-vessel, vascular, pleura invasion and positive EGFR mutation were 0.0%, 0.0%, 0.0% and 57.1% in group A, 15.6%, 10.0%, 12.1% and 55.1% in B, 36.6%, 31.8%, 29.7% and 44.9% in C, 50.2%, 57.8%, 38.9% and 21.3% in D. In group D, lymph-vessel, vascular and pleura invasion were most, EGFR mutation was least frequent not only in pathological stage I but also stage II + IIIA. In multivariate analysis, age, pathological stage, vascular invasion, and group D were independent factors affected RFS and OS.

Conclusion

Limited to lobectomy cases, solid + micropapillary was independent prognostic factor both in early and locally advanced stage. Its malignant degree was related to the frequency of pathological invasive factors and EGFR mutation status.

Updates in Grading of Renal Cell Carcinomas Beyond Clear Cell Renal Cell Carcinoma and Papillary Renal Cell Carcinoma

The World Health Organization (WHO) recommends grading of clear cell renal cell carcinoma (RCC) and papillary RCC using the WHO/International Society of Urological Pathology (ISUP) grade, which is primarily based on nuclear features. As the spectrum of RCC continues to evolve, with more recently described subtypes in the past decade, literature evidence on grading these subtypes is limited or not available for some tumor types. Herein, we outline a pragmatic approach to the topic of grading RCC, dividing the contemporarily described RCC subtypes into 7 categories based on the potential clinical applicability of grading as a useful prognostic parameter: (1) RCC subtypes that are reasonably validated and recommended for WHO/ISUP grading; (2) RCC subtypes where WHO/ISUP is not applicable; (3) RCC subtypes where WHO/ISUP grading is potentially clinically useful; (4) inherently aggressive RCC subtypes where histologic classification itself confers an aggressive biologic potential; (5) renal epithelial tumors where WHO/ISUP grading provides potentially misleading prognostic implication; (6) renal epithelial neoplasms where low WHO/ISUP grade features are a prerequisite for accurate histologic classification; and (7) renal epithelial neoplasms with no or limited data on grading or incomplete understanding of the biologic potential. Our aim in outlining this approach is 2-fold: (a) identify the gaps in understanding and application of grading in RCC subtypes so that researchers in the field may perform additional studies on the basis of which the important pathologic function of assignment of grade may be recommended to be performed as a meaningful exercise across a wider spectrum of RCC; and (b) to provide guidance in the interim to surgical pathologists in terms of providing clinically useful grading information in RCC based on currently available clinicopathologic information.

Upregulation of homeobox D10 expression suppresses invasion and migration of clear cell renal cell carcinoma through targeting of E-cadherin

Background

Clear cell renal cell carcinoma (CCRCC) is one of the most common types of renal cell carcinoma. Accumulating evidence indicates that homeobox D10 (HOXD10) acts as a tumor suppressor or oncogene in various carcinomas. However, the regulation and potential mechanisms of HOXD10 in CCRCC remain largely unknown.

Purpose

To explore the effect and potential mechanism of HOXD10 on the invasion and migration of CCRCC cells.

Methods

The expression of HOXD10, E-cadherin and other epithelial mesenchymal transition (EMT)-related proteins was assessed by reverse transcription-quantitative real-time PCR (qRT-PCR) and Western blots. A series of functional assays were performed in RCC cell lines to explore the function of HOXD10 in CCRCC progression. Bioinformatics analysis, ChIP assays, and dual luciferase reporter assays were utilized to identify the interaction between HOXD10 and E-cadherin.

Results

Low expression of HOXD10 and E-cadherin was observed in CCRCC tissues and ACHN and 786-O cells. Downregulation of HOXD10 expression was correlated with the TNM stage of CCRCC patients. Functional experiments demonstrated that malignant biological ability was significantly inhibited by HOXD10 overexpression in RCC cells. Moreover, E-cadherin was a potential target gene of HOXD10, as evidenced by a series of assays. In addition, overexpression of HOXD10 inhibited the progression of CCRCC by regulating the expression of E-cadherin, vimentin, and β-catenin in vitro.

Conclusion

HOXD10 acts as a tumor suppressor and suppresses invasion and migration of CCRCC cells by regulating E-cadherin and EMT processes. Thus, targeting HOXD10 may be a therapeutic strategy for CCRCC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11033-021-06993-8.

Utility of Newly Proposed Grading System From International Association for the Study of Lung Cancer for Invasive Lung Adenocarcinoma

Introduction

The International Association for the Study of Lung Cancer proposed a new grading criteria for invasive adenocarcinoma. However, its utility has not been validated.

Methods

Patients who underwent complete resection of lung adenocarcinoma were included in this study. Then, they were divided into the following three groups on the basis of the criteria recently proposed by the International Association for the Study of Lung Cancer: grade 1, lepidic predominant tumor, with less than 20% of high-grade patterns; grade 2, acinar or papillary predominant tumor, with less than 20% of high-grade patterns; and grade 3, any tumor with greater than or equal to 20% of high-grade patterns.

Results

Recurrence-free survival (RFS) was significantly different among the proposed grades (p < 0.001). The RFS of patients upgrading from current grade 2 (papillary or acinar predominant tumor) to proposed grade 3 (5-y RFS, 65.2%) was significantly worse than that of patients with proposed grade 2 (77.1%, hazard ratio = 1.882, 95% confidence interval: 1.236–2.866) but not significantly different from that of patients with grade 3 in both the current (micropapillary or solid predominant tumor) and proposed criteria (53.2%, hazard ratio = 0.761, 95% confidence interval: 0.456–1.269). Among patients with pathologic stage 0 or I, RFS was well stratified by the new grading system (p < 0.001) but not among patients with stage II or III (p = 0.334). In the multivariable analysis, the new grading was not a predictive factor of RFS.

Conclusions

Although the proposed grading system well stratified RFS in patients with pathologic stage 0 or I lung adenocarcinoma, there is room for improvement.

An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study

BACKGROUND

There is high demand to develop computer-assisted diagnostic tools to evaluate prostate core needle biopsies (CNBs), but little clinical validation and a lack of clinical deployment of such tools. We report here on a blinded clinical validation study and deployment of an artificial intelligence (AI)-based algorithm in a pathology laboratory for routine clinical use to aid prostate diagnosis.

METHODS

An AI-based algorithm was developed using haematoxylin and eosin (H&E)-stained slides of prostate CNBs digitised with a Philips scanner, which were divided into training (1 357 480 image patches from 549 H&E-stained slides) and internal test (2501 H&E-stained slides) datasets. The algorithm provided slide-level scores for probability of cancer, Gleason score 7-10 (vs Gleason score 6 or atypical small acinar proliferation [ASAP]), Gleason pattern 5, and perineural invasion and calculation of cancer percentage present in CNB material. The algorithm was subsequently validated on an external dataset of 100 consecutive cases (1627 H&E-stained slides) digitised on an Aperio AT2 scanner. In addition, the AI tool was implemented in a pathology laboratory within routine clinical workflow as a second read system to review all prostate CNBs. Algorithm performance was assessed with area under the receiver operating characteristic curve (AUC), specificity, and sensitivity, as well as Pearson's correlation coefficient (Pearson's r) for cancer percentage.

FINDINGS

The algorithm achieved an AUC of 0·997 (95% CI 0·995 to 0·998) for cancer detection in the internal test set and 0·991 (0·979 to 1·00) in the external validation set. The AUC for distinguishing between a low-grade (Gleason score 6 or ASAP) and high-grade (Gleason score 7-10) cancer diagnosis was 0·941 (0·905 to 0·977) and the AUC for detecting Gleason pattern 5 was 0·971 (0·943 to 0·998) in the external validation set. Cancer percentage calculated by pathologists and the algorithm showed good agreement (r=0·882, 95% CI 0·834 to 0·915; p<0·0001) with a mean bias of -4·14% (-6·36 to -1·91). The algorithm achieved an AUC of 0·957 (0·930 to 0·985) for perineural invasion. In routine practice, the algorithm was used to assess 11 429 H&E-stained slides pertaining to 941 cases leading to 90 Gleason score 7-10 alerts and 560 cancer alerts. 51 (9%) cancer alerts led to additional cuts or stains being ordered, two (4%) of which led to a third opinion request. We report on the first case of missed cancer that was detected by the algorithm.

INTERPRETATION

This study reports the successful development, external clinical validation, and deployment in clinical practice of an AI-based algorithm to accurately detect, grade, and evaluate clinically relevant findings in digitised slides of prostate CNBs.

FUNDING

Ibex Medical Analytics.

Insights into Urological Cancer

The year the Covid-19 pandemic appeared has been quite prolific in urological cancer research, and the collection of articles, perspectives, and reviews on renal, prostate, and urinary tract tumors merged in this Urological Cancer 2020 issue is just a representative sample of this assertion [...].

A Grading System for Invasive Pulmonary Adenocarcinoma: A Proposal From the International Association for the Study of Lung Cancer Pathology Committee

INTRODUCTION

A grading system for pulmonary adenocarcinoma has not been established. The International Association for the Study of Lung Cancer pathology panel evaluated a set of histologic criteria associated with prognosis aimed at establishing a grading system for invasive pulmonary adenocarcinoma.

METHODS

A multi-institutional study involving multiple cohorts of invasive pulmonary adenocarcinomas was conducted. A cohort of 284 stage I pulmonary adenocarcinomas was used as a training set to identify histologic features associated with patient outcomes (recurrence-free survival [RFS] and overall survival [OS]). Receiver operating characteristic curve analysis was used to select the best model, which was validated (n = 212) and tested (n = 300, including stage I-III) in independent cohorts. Reproducibility of the model was assessed using kappa statistics.

RESULTS

The best model (area under the receiver operating characteristic curve [AUC] = 0.749 for RFS and 0.787 for OS) was composed of a combination of predominant plus high-grade histologic pattern with a cutoff of 20% for the latter. The model consists of the following: grade 1, lepidic predominant tumor; grade 2, acinar or papillary predominant tumor, both with no or less than 20% of high-grade patterns; and grade 3, any tumor with 20% or more of high-grade patterns (solid, micropapillary, or complex gland). Similar results were seen in the validation (AUC = 0.732 for RFS and 0.787 for OS) and test cohorts (AUC = 0.690 for RFS and 0.743 for OS), confirming the predictive value of the model. Interobserver reproducibility revealed good agreement (k = 0.617).

CONCLUSIONS

A grading system based on the predominant and high-grade patterns is practical and prognostic for invasive pulmonary adenocarcinoma.

Alterations in TP53 Are a Potential Biomarker of Bladder Cancer Patients Who Benefit From Immune Checkpoint Inhibition

In recent years, immune checkpoint inhibitors (ICIs) targeting CTLA-4 or PD1/PDL1 have achieved remarkable success in the treatment of bladder cancer (BLCA), but only a few patients have shown durable clinical benefits. The prognostic role of a mutant form of the tumor suppressor gene TP53 (TP53-MT) in predicting the efficacy of ICIs is highly controversial; therefore, in this study, we obtained data for 210 patients from an immunotherapy cohort, 412 patients from The Cancer Genome Atlas (TCGA)-BLCA cohort and 18 BLCA cell lines from Genomics of Drug Sensitivity in Cancer (GDSC), and we performed integrated bioinformatic analysis to explore the relationships between TP53-MT and clinical benefits derived from ICI treatment and the underlying mechanisms. We conclude that TP53-MT is a potential indicator of a relatively good response to ICIs and associated with prolonged overall survival (OS) (log-rank test, hazard ratio (HR) = 0.65 [95% confidence interval (CI), 0.44-0.99], p = 0.041). Through integrated analysis with several platforms, we found that TP53-MT patients were more likely to benefit from ICIs than wild-type P53 (TP53-WT) patients, which may be the result of 2 major mechanisms. First, the patients with TP53-MT showed stronger tumor antigenicity and tumor antigen presentation, as indicated by a higher tumor mutational load, a higher neoantigen load and increased expression of MHC; second, the antitumor immunity preexisting in tumors was stronger in samples with TP53-MT than in those with TP53-WT, including enrichment of interferon-gamma, positive regulation of TNF secretion pathways and increased expression of some immunostimulatory molecules, such as CXCL9 and CXCL10. This study provided some clues for identifying patients who would potentially benefit from ICIs at the somatic genomic level, developing new indications for targeted second-generation sequencing and promoting the development of precision medicine.