Overall Survival Prediction by Tumor Microenvironment Lymphocyte Distribution in Hepatocellular Carcinoma After Liver Transplantation.

INTRODUCTION: Our previous research demonstrated that CD8+ cell density profiling using a hexagonal grid-based digital image analysis method provides predictors of patient outcomes after liver resection due to hepatocellular carcinoma (HCC). Continuing our study, we have further investigated the applicability of the methodology to patients receiving a liver transplant for HCC. METHODS: The retrospective study enrolled patients with HCC who underwent liver transplantation (LT) at the Vilnius University Hospital Santaros Clinics between 2007 and 2020. We determined the density profiles of CD8+ lymphocytes at the interface between HCC and stroma and the interface between the perineoplastic liver parenchyma and stroma. Both digital image analysis and the hexagonal grid-based immunogradient method were applied to CD8+ immunohistochemistry images. Survival statistics based on clinicopathological, peripheral blood analysis, and surgical data determined the prognostic value of these indicators. RESULTS: Univariate clinicopathological predictors of worse OS after LT included: patient's age at the time of the transplantation, a higher number of HCC nodules, lower platelet count, longer activated thromboplastin time, lower serum albumin, higher serum total bilirubin, and lower serum creatinine levels. The two independent predictors of overall survival were mean CD8+ cell density at the epithelial edge of the explanted liver parenchyma-stroma interface and peripheral blood platelet count. CONCLUSIONS: Our model discloses that preoperative peripheral blood platelet count and mean CD8+ cell density at the epithelial edge of nonmalignant interface in the explanted liver parenchyma are independent predictors of OS for HCC after LT.

Spatial Distribution of Macrophage and Lymphocyte Subtypes within Tumor Microenvironment to Predict Recurrence of Non-Muscle-Invasive Papillary Urothelial Carcinoma after BCG Immunotherapy.

Non-muscle-invasive papillary urothelial carcinoma (NMIPUC) of the urinary bladder is the most common type of bladder cancer. Intravesical Bacille Calmette-Guerin (BCG) immunotherapy is applied in patients with a high risk of recurrence and progression of NMIPUC to muscle-invasive disease. However, the tumor relapses in about 30% of patients despite the treatment, raising the need for better risk stratification. We explored the potential of spatial distributions of immune cell subtypes (CD20, CD11c, CD163, ICOS, and CD8) within the tumor microenvironment to predict NMIPUC recurrence following BCG immunotherapy. Based on analyses of digital whole-slide images, we assessed the densities of the immune cells in the epithelial-stromal interface zone compartments and their distribution, represented by an epithelial-stromal interface density ratio (IDR). While the densities of any cell type did not predict recurrence, a higher IDR of CD11c (HR: 0.0012, p-value = 0.0002), CD8 (HR: 0.0379, p-value = 0.005), and ICOS (HR: 0.0768, p-value = 0.0388) was associated with longer recurrence-free survival (RFS) based on the univariate Cox regression. The history of positive repeated TUR (re-TUR) (HR: 4.93, p-value = 0.0001) and T1 tumor stage (HR: 2.04, p-value = 0.0159) were associated with shorter RFS, while G3 tumor grade according to the 1973 WHO classification showed borderline significance (HR: 1.83, p-value = 0.0522). In a multivariate analysis, the two models with a concordance index exceeding 0.7 included the CD11c IDR in combination with either a history of positive re-TUR or tumor stage. We conclude that the CD11c IDR is the most informative predictor of NMIPUC recurrence after BCG immunotherapy. Our findings highlight the importance of assessment of the spatial distribution of immune cells in the tumor microenvironment.

The academic impact and value of an international online surgery lecture series.

Due to the worldwide travel restrictions caused by the 2019 coronavirus disease pandemic, many universities and students lost opportunities to engage in international exchange over the past 2 years. Teleconferencing systems have thus been developed to compensate for severe travel restrictions. Kansai Medical University in Japan and Vilnius University in Lithuania have a collaborative research and academic relationship. The two universities have been conducting an online joint international surgery lecture series for the medical students of both universities. Fifteen lectures were given from October 2021 to May 2022. The lectures focused on gastrointestinal surgery, gastroenterology, radiology, pathology, genetics, laboratory medicine, and organ transplantation. A survey of the attendees indicated that they were generally interested in the content and satisfied with attending this lecture series. Our efforts were successful in providing Japanese and Lithuanian medical students with the opportunity to engage in international exchange through lectures held in each other's countries.

Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study.

Ki67 has potential clinical importance in breast cancer but has yet to see broad acceptance due to inter-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) investigate the comparability of Ki67 measurement across corresponding core biopsy and resection specimen cases, and (ii) assess section to section differences in Ki67 scoring. Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding resection specimens from 30 estrogen receptor-positive breast cancer patients were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and immunohistochemically (IHC) stained for Ki67 (MIB-1 antibody). The QuPath platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed as in published studies. A guideline for building an automated Ki67 scoring algorithm was sent to participating labs. Very high correlation and no systematic error (p = 0.08) was found between consecutive Ki67 IHC sections. Ki67 scores were higher for core biopsy slides compared to paired whole sections from resections (p ≤ 0.001; median difference: 5.31%). The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation). Therefore, Ki67 IHC should be tested on core biopsy samples to best reflect the biological status of the tumor.

Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance.

Assessment of intratumoral heterogeneity and tumor-host interaction within the tumor microenvironment is becoming increasingly important for innovative cancer therapy decisions because of the unique information it can generate about the state of the disease. However, its assessment and quantification are limited by ambiguous definitions of the tumor-host interface and by human cognitive capacity in current pathology practice. Advances in machine learning and artificial intelligence have opened the field of digital pathology to novel tissue image analytics and feature extraction for generation of high-capacity computational disease management models. A particular benefit is expected from machine-learning applications that can perform extraction and quantification of subvisual features of both intratumoral heterogeneity and tumor microenvironment aspects. These methods generate information about cancer cell subpopulation heterogeneity, potential tumor-host interactions, and tissue microarchitecture, derived from morphologically resolved content using both explicit and implicit features. Several studies have achieved promising diagnostic, prognostic, and predictive artificial intelligence models that often outperform current clinical and pathology criteria. However, further effort is needed for clinical adoption of such methods through development of standardizable high-capacity workflows and proper validation studies.

Immunogradient Indicators for Antitumor Response Assessment by Automated Tumor-Stroma Interface Zone Detection.

The distribution of tumor-infiltrating lymphocytes (TILs) within the tumor microenvironment provides strong prognostic value, which is increasingly important with the arrival of new immunotherapy modalities. Both visual and image analysis-based assays are developed to assess the immune contexture of the tumors. We propose an automated method based on grid subsampling of microscopy image analysis data to extract the tumor-stroma interface zone (IZ) of controlled width. The IZ is a ranking of tissue areas by their distance to the tumor edge, which is determined by a set of explicit rules. TIL density profiles across the IZ are used to compute a set of novel immunogradient indicators that reflect TIL gradient towards the tumor. We applied this method on CD8 immunohistochemistry images of surgically excised hormone receptor-positive breast and colorectal cancers to predict overall patient survival. In both cohorts, the immunogradient indicators enabled strong and independent prognostic stratification, outperforming clinical and pathologic variables. Patients with breast cancer with low immunogradient levels had a prominent decrease in survival probability 5 years after surgery. Our study provides proof of concept that data-driven, automated, operator-independent IZ sampling enables spatial immune response measurement in the tumor-host interaction frontline for prediction of disease outcomes.

Associations between Physical Function, Bone Density, Muscle Mass and Muscle Morphology in Older Men with Sarcopenia: A Pilot Study.

Background and Objectives: It is thought that muscle and bone interact only on a biomechanical level, however, some research is now emerging that links bone and muscle on a cellular level. The aim of this study was to explore associations between physical function, muscle mass and bone density in community-dwelling elderly men with sarcopenia. A secondary goal was to analyze if muscle morphology was associated with bone density and physical functioning. Materials and Methods: Body composition was measured by dual-energy X-ray absorptiometry (DXA). Bone density was evaluated according to WHO criteria. Sarcopenia was diagnosed according to European Working Group on Sarcopenia in Older People (EWGSOP) criteria: low muscle mass and low muscle strength or low physical performance. Microbiopsy of musculus vastus lateralis was performed with a disposable muscle microbiopsy system. The perimeter and cross-sectional area of muscle fibers were calculated using image analysis software in whole slide images; type of fibers and their distribution were evaluated as well. Results: A total of 151 men, 60 years or older were included in this study. Mean age of the subjects was 72.9 ± 8.02 years. Sarcopenia was diagnosed in 45 (29.8%) men. Multiple significant correlations were found between bone mineral density, lean mass, appendicular lean mass, arm and leg lean mass, gait speed, balance test and handgrip strength in sarcopenic men. Lean mass was associated with femoral neck BMD (bone mineral density; r = 0.418, p = 0.006) and handgrip strength (r = 0.553, p < 0.001). In the sarcopenia group, 25 muscle biopsies were examined. In 9 sarcopenic men with T-scores equal or below -2.5, the muscle fiber area had a significant correlation with the balance test (r = 0.73, p = 0.025). Conclusions: In men with sarcopenia, low lean muscle mass was associated with low femoral neck BMD and low muscle strength. In sarcopenic men with osteoporosis, low muscle fiber area was associated with low scores in a balance test.

An international multicenter study to evaluate reproducibility of automated scoring for assessment of Ki67 in breast cancer.

The nuclear proliferation biomarker Ki67 has potential prognostic, predictive, and monitoring roles in breast cancer. Unacceptable between-laboratory variability has limited its clinical value. The International Ki67 in Breast Cancer Working Group investigated whether Ki67 immunohistochemistry can be analytically validated and standardized across laboratories using automated machine-based scoring. Sets of pre-stained core-cut biopsy sections of 30 breast tumors were circulated to 14 laboratories for scanning and automated assessment of the average and maximum percentage of tumor cells positive for Ki67. Seven unique scanners and 10 software platforms were involved in this study. Pre-specified analyses included evaluation of reproducibility between all laboratories (primary) as well as among those using scanners from a single vendor (secondary). The primary reproducibility metric was intraclass correlation coefficient between laboratories, with success considered to be intraclass correlation coefficient >0.80. Intraclass correlation coefficient for automated average scores across 16 operators was 0.83 (95% credible interval: 0.73-0.91) and intraclass correlation coefficient for maximum scores across 10 operators was 0.63 (95% credible interval: 0.44-0.80). For the laboratories using scanners from a single vendor (8 score sets), intraclass correlation coefficient for average automated scores was 0.89 (95% credible interval: 0.81-0.96), which was similar to the intraclass correlation coefficient of 0.87 (95% credible interval: 0.81-0.93) achieved using these same slides in a prior visual-reading reproducibility study. Automated machine assessment of average Ki67 has the potential to achieve between-laboratory reproducibility similar to that for a rigorously standardized pathologist-based visual assessment of Ki67. The observed intraclass correlation coefficient was worse for maximum compared to average scoring methods, suggesting that maximum score methods may be suboptimal for consistent measurement of proliferation. Automated average scoring methods show promise for assessment of Ki67 scoring, but requires further standardization and subsequent clinical validation.

Tumour parcellation and quantification (TuPaQ): a tool for refining biomarker analysis through rapid and automated segmentation of tumour epithelium.

BACKGROUND AND AIMS: Immunohistochemistry (IHC) is an essential component of biomarker research in cancer. Automated biomarker quantification is hampered by the failure of computational algorithms to discriminate 'negative' tumour cells from 'negative' stromal cells. We sought to develop an algorithm for segmentation of tumour epithelium in colorectal cancer (CRC), irrespective of the biomarker expression in the cells. METHODS AND RESULTS: We developed tumour parcellation and quantification (TuPaQ) to segment tumour epithelium and parcellate sections into 'epithelium' and 'non-epithelium'. TuPaQ comprises image pre-processing, extraction of regions of interest (ROIs) and quantification of tumour epithelium (total area occupied by epithelium and number of nuclei in the occupied area). A total of 286 TMA cores from CRC were manually annotated and analysed using the commercial halo software to provide ground truth. The performance of TuPaQ was evaluated against the ground truth using a variety of metrics. The image size of each core was 7000 × 7000 pixels and each core was analysed in a matter of seconds. Pixel × pixel analysis showed a sensitivity of 84% and specificity of 95% in detecting epithelium. The mean tumour area obtained by TuPaQ was very close to the area quantified after manual annotation (r = 0.956, P < 0.001). Moreover, quantification of tumour nuclei by TuPaQ correlated very strongly with that of halo (r = 0.891, P < 0.001). CONCLUSION: TuPaQ is a very rapid and accurate method of separating the epithelial and stromal compartments of colorectal tumours. This will allow more accurate and objective analysis of immunohistochemistry.

Genotype⁻Phenotype Correlation in a New Fabry-Disease-Causing Mutation.

Background: Fabry disease (FD) is a rare X-linked inherited lysosomal storage disorder caused by α-galactosidase A deficiency leading to intracellular glycosphingolipid accumulation. FD manifestation is multisystem, and can differ depending on disease-related genetic variants. Currently, more than 700 different FD-causing mutations have been identified in the human GLA gene. We identified a novel mutation in a Lithuanian family with classical manifestations of Fabry disease, revealing severe effects to the cardiovascular systems of heterozygous women. Case presentation: A 49-year-old woman underwent echocardiography due to progressive dyspnea that lasted seven years, reduced physical activity, and periodic cardiac arrhythmia. Echocardiography revealed left ventricular hypertrophy with normal diastolic function. The patient had experienced acroparesthesia in her upper limbs and abdominal pain since childhood, and in the last decade had experienced mild proteinuria without renal failure. Her renal biopsy was typical for Fabry disease. The patient's brain magnetic resonance imaging (MRI) (T2 flair) showed white matter hyperintensities lesions. DNA sequencing of the proband, her mother and one of her sons showed a novel GLA gene exon 2 mutation, c.270C>G (p.Cys90Trp). All three patients had decreased α-galactosidase A activity and specific FD manifestations. Conclusion: A novel GLA mutation, c.270C>G (p.Cys90Trp), was found in a Lithuanian family with a classical form of Fabry disease in heterozygous women with predominant cardiac involvement. However, the exact manifestation of this mutation is still unclear as it is newly reported and further research must be done.

Significance of Notch and Wnt signaling for chemoresistance of colorectal cancer cells HCT116.

5-fluorouracil (5-FU) and oxaliplatin (OxaPt) are the main chemotherapeutics for colorectal cancer (CRC). Chemotherapy response rates for advanced CRC remain low, primarily due to intrinsic or acquired chemoresistance. The importance of Notch and Wnt signaling for carcinogenesis of CRC as well as crosstalk of Notch and Wnt signaling with many oncogenic signaling pathways suggest that Notch and Wnt pathways could be responsible for chemoresistance. In this study, we compared changes in Notch and Wnt signaling after 5-FU and OxaPt treatment in CRC cells HCT116 and its chemoresistant sublines HCT116/FU and HCT116/OXA. The levels of Notch1 receptor intracellular domain NICD1 and non-phosphorylated ß-catenin, the reporters of Notch and Wnt signaling, were upregulated in untreated chemoresistant HCT116/FU and HCT116/OXA cells. Our data suggest that Notch inhibitor RO4929097 (RO) and Wnt inhibitor XAV939 (XAV) enhance the survival potential of OxaPt-treated cells. The protein level of Notch target gene HES1 was significantly upregulated in chemoresistant HCT116/FU and HCT116/OXA cells, compared to HCT116. HES1 silencing increased viability of HCT116 and its chemoresistant sublines after 5-FU or OxaPt treatment. The results of HES1 downregulation coincide with RO and XAV effects on cell viability of OxaPt-treated cells.

The impact of IgM deposits on the outcome of Nephrotic syndrome in children.

BACKGROUND: The significance of IgM deposits in glomerular mesangium has been controversial since they were first described due to the variations in the both the definitions used and described impact on clinical outcome. The aim of our study was to evaluate the significance of the IgM deposits in the glomerular mesangium for outcomes of nephrotic syndrome (NS) in children. METHODS: Forty-five children with NS who underwent renal biopsy at tertiary pediatric hospital from January 1st, 2000 to December 31st, 2015 and the pathology diagnosis of minimal change disease, focal segmental glomerulosclerosis and mesangial hypercellularity (MH) were retrospectively analyzed. IgM positivity was defined as ≥1+ imunofluorescence with predominantly mesangial distribution. The patients were stratified into IgM-positive (n = 18) and IgM-negative (n = 27). RESULTS: At the end of the median follow-up 4.5 years (range 0.17-13.14), the IgM-positive group was represented by 11 patients (61.1%) in remission, 3 patients (16.7%) with active disease and normal kidney function, 2 (11.1%) patients with active disease and impaired kidney function, 2 (11.1%) patients on renal replacement therapy. Accordingly, the IgM-negative group included 13 patients (48.1%) in remission, 12 (44.4%) with active disease and normal kidney function, 1 (3.7%) with active disease and impaired kidney function, 1 (3.7%) on renal replacement therapy, with no statistical significance between groups (p = 0.186). CONCLUSIONS: This study did not reveal significant differences of the disease outcomes between IgM-positive and IgM-negative groups.

Comprehensive Immunohistochemistry: Digital, Analytical and Integrated.

Immunohistochemistry (IHC) is widely used in contemporary pathology as a diagnostic and, increasingly, as a prognostic and predictive tool. The main value of the method today comes from a sensitive and specific detection of a protein of interest in the context of tissue architecture and cell populations. One of the major limitations of conventional IHC is related to the fact that the results are usually obtained by visual qualitative or semiquantitative evaluation. While this is sufficient for diagnostic purposes, measurement of prognostic and predictive biomarkers requires better accuracy and reproducibility. Also, objective evaluation of the spatial heterogeneity of biomarker expression as well as the development of combined/integrated biomarkers are in great demand. On the other end of the scale, the rapid development of tissue proteomics accounting for 2D spatial aspects has led to a disruptive concept of next-generation IHC, promising high multiplexing and broad dynamic range quantitative/spatial data on tissue protein expression. This 'evolutionary gap' between conventional and next-generation IHC can be filled by comprehensive IHC based on digital technologies (empowered by quantification and spatial and multiparametric analytics) and integrated into the pathology workflow and information systems. In this paper, we share our perspectives on a comprehensive IHC road map as a multistep development process.

IgA nephropathy clinicopathologic study following the Oxford classification: Progression peculiarities and gender-related differences.

BACKGROUND AND AIM: Immunoglobulin A nephropathy (IgAN) is the most frequent glomerular disease worldwide and one of the main causes of chronic kidney disease. We aimed to investigate clinicopathological correlations in IgAN patients by gender. MATERIALS AND METHODS: The study was based on a retrospective analysis of renal biopsy data and clinical manifestations of the disease. Consecutive 73 biopsy-proven IgAN cases of male (62%) and female (38%) patients were investigated. Renal biopsies were reviewed using the new Oxford classification assessing the MEST (mesangial hypercellularity, endocapillary hypercellularity, segmental sclerosis/adhesion, tubular atrophy/interstitial fibrosis) score. The most powerful IgAN prognostic risk factors, morphological (segmental glomerulosclerosis and tubular atrophy/interstitial fibrosis) as well as clinical (proteinuria and hypertension) were taken into account in the correlation analysis. The mean rate of renal function decline was expressed as a slope of eGFR during the follow-up (FU) dividing delta GFR with the FU years. RESULTS: The mean age of the patients was 33.7 years (range, 16-76). Follow-up data were available for 64 patients with the mean follow-up of 4.1 years. The mean proteinuria at biopsy was 0.79g/24h. The mean arterial pressure (MAP) was 94.5±16.7mmHg and 7% of the patients were hypertensive. The initial mean estimated glomerular filtration rate (eGFR) was 94.9±30.7mL/min, at the end of the follow-up it was 86.2±27.1mL/min. The mean rate of renal function decline was -3.4±11.9mL/min/1.73m2 per year in males (P<0.05) and -0.7±5.3mL/min/1.73m2 per year in females. The Spearman correlation analysis confirmed a higher MEST score in the whole cohort and in males correlated with disease progression. In patients with proteinuria below 1.0g/24h, disease progression was faster in males. CONCLUSIONS: According to the correlation analysis of the main prognostic risk factors, affecting the progression of IgAN, we can conclude that IgA nephropathy in males progresses more rapidly compared to females.

A methodology to ensure and improve accuracy of Ki67 labelling index estimation by automated digital image analysis in breast cancer tissue.

INTRODUCTION: Immunohistochemical Ki67 labelling index (Ki67 LI) reflects proliferative activity and is a potential prognostic/predictive marker of breast cancer. However, its clinical utility is hindered by the lack of standardized measurement methodologies. Besides tissue heterogeneity aspects, the key element of methodology remains accurate estimation of Ki67-stained/counterstained tumour cell profiles. We aimed to develop a methodology to ensure and improve accuracy of the digital image analysis (DIA) approach. METHODS: Tissue microarrays (one 1-mm spot per patient, n = 164) from invasive ductal breast carcinoma were stained for Ki67 and scanned. Criterion standard (Ki67-Count) was obtained by counting positive and negative tumour cell profiles using a stereology grid overlaid on a spot image. DIA was performed with Aperio Genie/Nuclear algorithms. A bias was estimated by ANOVA, correlation and regression analyses. Calibration steps of the DIA by adjusting the algorithm settings were performed: first, by subjective DIA quality assessment (DIA-1), and second, to compensate the bias established (DIA-2). Visual estimate (Ki67-VE) on the same images was performed by five pathologists independently. RESULTS: ANOVA revealed significant underestimation bias (P < 0.05) for DIA-0, DIA-1 and two pathologists' VE, while DIA-2, VE-median and three other VEs were within the same range. Regression analyses revealed best accuracy for the DIA-2 (R-square = 0.90) exceeding that of VE-median, individual VEs and other DIA settings. Bidirectional bias for the DIA-2 with overestimation at low, and underestimation at high ends of the scale was detected. Measurement error correction by inverse regression was applied to improve DIA-2-based prediction of the Ki67-Count, in particularfor the clinically relevant interval of Ki67-Count < 40%. Potential clinical impact of the prediction was tested by dichotomising the cases at the cut-off values of 10, 15, and 20%. Misclassification rate of 5-7% was achieved, compared to that of 11-18% for the VE-median-based prediction. CONCLUSIONS: Our experiments provide methodology to achieve accurate Ki67-LI estimation by DIA, based on proper validation, calibration, and measurement error correction procedures, guided by quantified bias from reference values obtained by stereology grid count. This basic validation step is an important prerequisite for high-throughput automated DIA applications to investigate tissue heterogeneity and clinical utility aspects of Ki67 and other immunohistochemistry (IHC) biomarkers.

Epigenetic markers of prostate cancer in plasma circulating DNA.

Epigenetic differences are a common feature of many diseases, including cancer, and disease-associated changes have even been detected in bodily fluids. DNA modification studies in circulating DNA (cirDNA) may lead to the development of specific non-invasive biomarkers. To test this hypothesis, we investigated cirDNA modifications in prostate cancer patients with locally confined disease (n = 19), in patients with benign prostate hyperplasias (n = 20) and in men without any known prostate disease (n = 20). This initial discovery screen identified 39 disease-associated changes in cirDNA modification, and seven of these were validated using the sodium bisulfite-based mapping of modified cytosines in both the discovery cohort and an independent 38-patient validation cohort. In particular, we showed that the DNA modification of regions adjacent to the gene encoding ring finger protein 219 distinguished prostate cancer from benign hyperplasias with good sensitivity (61%) and specificity (71%). We also showed that repetitive sequences detected in this study were meaningful, as they indicated a highly statistically significant loss of DNA at the pericentromeric region of chromosome 10 in prostate cancer patients (p = 1.8 × 10(-6)). Based on these strong univariate results, we applied machine-learning techniques to develop a multi-locus biomarker that correctly distinguished prostate cancer samples from unaffected controls with 72% accuracy. Lastly, we used systems biology techniques to integrate our data with publicly available DNA modification and transcriptomic data from primary prostate tumors, thereby prioritizing genes for further studies. These data suggest that cirDNA epigenomics are promising source for non-invasive biomarkers.

Prognostic value of RASSF1 promoter methylation in prostate cancer.

PURPOSE: Patients with prostate cancer who have biochemical recurrence after curative therapy are at higher risk for distant metastasis and cancer specific death. Assessment of aberrant DNA methylation in urine might complement currently used clinical prognostic factors and serve as a noninvasive tool for early prediction of biochemical recurrence after radical prostatectomy. MATERIALS AND METHODS: Promoter methylation of 7 genes was evaluated by methylation sensitive polymerase chain reaction in 149 prostate cancer tissues, 37 noncancerous prostate tissues and 17 benign prostatic hyperplasia samples. Quantitative polymerase chain reaction was used for DNA methylation analysis of the urine of 253 patients with prostate cancer and 32 with benign prostatic hyperplasia. RESULTS: In prostate cancer tissue the most frequently methylated genes were RASSF1, GSTP1 and RARB, which combined were positively identified in 85% of cases. These genes were also methylated in the urine of 60% of patients with prostate cancer. RASSF1 was methylated in 45% of prostate cancer urine samples with methylation intensity significantly higher in prostate cancer than in benign prostatic hyperplasia cases (p = 0.018). In a univariate model RASSF1 methylation and the total number of methylated genes in prostate cancer tissue were predictive of time to biochemical recurrence (p = 0.019 and 0.043, respectively). On multivariate analysis RASSF1 methylation together with pathological stage was the most significant predictor of biochemical recurrence in patients with Gleason score 6 tumors when analyzed in tissue and urine (p ≤0.001). CONCLUSIONS: Hypermethylation of RASSF1 in cancerous tissue and urine from patients with prostate cancer correlated with biochemical recurrence after radical prostatectomy. The prognostic potential of this biomarker deserves further investigation.

The digital revolution in pathology: Towards a smarter approach to research and treatment.

Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. We discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.

Prediction of Non-Muscle Invasive Papillary Urothelial Carcinoma Relapse from Hematoxylin-Eosin Images Using Deep Multiple Instance Learning in Patients Treated with Bacille Calmette-Guérin Immunotherapy.

The limited reproducibility of the grading of non-muscle invasive papillary urothelial carcinoma (NMIPUC) necessitates the search for more robust image-based predictive factors. In a cohort of 157 NMIPUC patients treated with Bacille Calmette-Guérin (BCG) immunotherapy, we explored the multiple instance learning (MIL)-based classification approach for the prediction of 2-year and 5-year relapse-free survival and the multiple instance survival learning (MISL) framework for survival regression. We used features extracted from image patches sampled from whole slide images of hematoxylin-eosin-stained transurethral resection (TUR) NPMIPUC specimens and tested several patch sampling and feature extraction network variations to optimize the model performance. We selected the model showing the best patient survival stratification for further testing in the context of clinical and pathological variables. MISL with the multiresolution patch sampling technique achieved the best patient risk stratification (concordance index = 0.574, p = 0.010), followed by a 2-year MIL classification. The best-selected model revealed an independent prognostic value in the context of other clinical and pathologic variables (tumor stage, grade, and presence of tumor on the repeated TUR) with statistically significant patient risk stratification. Our findings suggest that MISL-based predictions can improve NMIPUC patient risk stratification, while validation studies are needed to test the generalizability of our models.

Computational pathology model to assess acute and chronic transformations of the tubulointerstitial compartment in renal allograft biopsies.

Managing patients with kidney allografts largely depends on biopsy diagnosis which is based on semiquantitative assessments of rejection features and extent of acute and chronic changes within the renal parenchyma. Current methods lack reproducibility while digital image data-driven computational models enable comprehensive and quantitative assays. In this study we aimed to develop a computational method for automated assessment of histopathology transformations within the tubulointerstitial compartment of the renal cortex. Whole slide images of modified Picrosirius red-stained biopsy slides were used for the training (n = 852) and both internal (n = 172) and external (n = 94) tests datasets. The pipeline utilizes deep learning segmentations of renal tubules, interstitium, and peritubular capillaries from which morphometry features were extracted. Seven indicators were selected for exploring the intrinsic spatial interactions within the tubulointerstitial compartment. A principal component analysis revealed two independent factors which can be interpreted as representing chronic and acute tubulointerstitial injury. A K-means clustering classified biopsies according to potential phenotypes of combined acute and chronic transformations of various degrees. We conclude that multivariate analyses of tubulointerstitial morphometry transformations enable extraction of and quantification of acute and chronic components of injury. The method is developed for renal allograft biopsies; however, the principle can be applied more broadly for kidney pathology assessment.