Neoplastic cell percentage estimation in tissue samples for molecular oncology: recommendations from a modified Delphi study

Histopathologic Grading of Residual Tumor Predicts Survival of Intrahepatic Cholangiocarcinoma Patients Treated With Neoadjuvant Therapy: Major Pathologic Response and Its Clinical Significance

Neoadjuvant therapy (NAT) is increasingly used to treat patients with initially unresectable intrahepatic cholangiocarcinoma (iCCA). A histopathologic grading system for residual tumors that can predict patient survival is lacking in the literature. This retrospective study enrolled 151 iCCA patients who received NAT. The percentage of residual viable tumor (%RVT) extent was calculated by RVT surface area/total tumor bed area ×100 and scored in 5% increments. Kaplan-Meier and Cox regression analyses were used to investigate its correlations with recurrence-free survival (RFS) and overall survival (OS). Tumor regression grading by the College of American Pathologists (CAP) and MD Anderson (MDA) methodologies were also validated. A 10% RVT-based tumor regression score (TRS) showed a significant correlation with both OS and RFS. TRS and major pathologic response (mPR) were therefore defined as follows: TRS 1/mPR, tumor with 0 to 10% RVT; TRS 2, more than 10% RVT. Patients graded as TRS 1/mPR had superior OS ( P =0.006) and RFS ( P <0.001) compared with those with TRS 2 in univariate analysis. In a multivariate analysis including ypTNM stages, lymphovascular invasion, and perineural invasion, TRS 1/mPR was also found to be an independent prognostic factor for both OS (hazard ratio [HR]: 0.226; 95% CI: 0.053-0.966, P =0.045) and RFS (HR: 0.474; 95% CI: 0.231-0.974, P =0.042). As for the CAP and MDA grading methodologies, they were found to correlate with RFS (CAP: P =0.002; MDA: P =0.001), but not with OS (CAP: P =0.181; MDA: P =0.09). Our study revealed that a TRS of ≤10% RVT significantly correlates with longer OS and RFS and can be suggested as an mPR in iCCA. This indicator is easily applicable, prognostically relevant, and could be further validated in future prospective clinical trials.

Is there a minimum percentage of sarcomatoid component required to affect outcomes of localised renal cell carcinoma?

OBJECTIVE

To evaluate and compare the outcomes of patients with localised renal cell carcinoma (RCC) with and without sarcomatoid features and the impact of this on cancer recurrence and survival.

MATERIAL AND METHODS

The Canadian Kidney Cancer information system database was used to identify patients diagnosed with localised RCC between January 2011 and December 2022. Patients with pT1-T3, n Nx-N0N1, M0 stage and documented sarcomatoid status were included. Patients with sarcomatoid RCC were categorised according to the sarcomatoid component percentage (%Sarc). Inverse probability of treatment weighting scores were used to balance the groups. Cox proportional hazards models were used to assess the impact of sarcomatoid status and %Sarc on recurrence-free and overall survival.

RESULTS

A total of 6660 patients (201 with and 6459 without sarcomatoid features) with non-metastatic RCC were included. %Sarc data were available in 155 patients, and the median value was 10%. The weighted analysis revealed that the presence of sarcomatoid features was associated with an increased risk of developing metastasis and increased risk of mortality compared to absence of sarcomatoid features. A %Sarc value >10 was associated with an increased risk of developing metastasis and of mortality compared to a %Sarc value ≤10.

CONCLUSIONS

Patients with a %Sarc >10 have an increased risk of recurrence and mortality. These patients may benefit from a more stringent follow-up and %Sarc could represent an important criterion in the risk assessment for adjuvant therapy.

Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis

Tumour content plays a pivotal role in directing the bioinformatic analysis of molecular profiles such as copy number variation (CNV). In clinical application, tumour purity estimation (TPE) is achieved either through visual pathological review [conventional pathology (CP)] or the deconvolution of molecular data. While CP provides a direct measurement, it demonstrates modest reproducibility and lacks standardisation. Conversely, deconvolution methods offer an indirect assessment with uncertain accuracy, underscoring the necessity for innovative approaches. SoftCTM is an open-source, multiorgan deep-learning (DL) model for the detection of tumour and non-tumour cells in H&E-stained slides, developed within the Overlapped Cell on Tissue Dataset for Histopathology (OCELOT) Challenge 2023. Here, using three large multicentre colorectal cancer (CRC) cohorts (N = 1,097 patients) with digital pathology and multi-omic data, we compare the utility and accuracy of TPE with SoftCTM versus CP and bioinformatic deconvolution methods (RNA expression, DNA methylation) for downstream molecular analysis, including CNV profiling. SoftCTM showed technical repeatability when applied twice on the same slide (r = 1.0) and excellent correlations in paired H&E slides (r > 0.9). TPEs profiled by SoftCTM correlated highly with RNA expression (r = 0.59) and DNA methylation (r = 0.40), while TPEs by CP showed a lower correlation with RNA expression (r = 0.41) and DNA methylation (r = 0.29). We show that CP and deconvolution methods respectively underestimate and overestimate tumour content compared to SoftCTM, resulting in 6-13% differing CNV calls. In summary, TPE with SoftCTM enables reproducibility, automation, and standardisation at single-cell resolution. SoftCTM estimates (M = 58.9%, SD ±16.3%) reconcile the overestimation by molecular data extrapolation (RNA expression: M = 79.2%, SD ±10.5, DNA methylation: M = 62.7%, SD ±11.8%) and underestimation by CP (M = 35.9%, SD ±13.1%), providing a more reliable middle ground. A fully integrated computational pathology solution could therefore be used to improve downstream molecular analyses for research and clinics. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Concordance in the estimation of tumor percentage in non-small cell lung cancer using digital pathology

The incorporation of digital pathology in clinical practice will require the training of pathologists in digital skills. Our study aimed to assess the reliability among pathologists in determining tumor percentage in whole slide images (WSI) of non-small cell lung cancer (NSCLC) using digital image analysis, and study how the results correlate with the molecular findings. Pathologists from nine centers were trained to quantify epithelial tumor cells, tumor-associated stromal cells, and non-neoplastic cells from NSCLC WSI using QuPath. Then, we conducted two consecutive ring trials. In the first trial, analyzing four WSI, reliability between pathologists in the assessment of tumor cell percentage was poor (intraclass correlation coefficient (ICC) 0.09). After performing the first ring trial pathologists received feedback. The second trial, comprising 10 WSI with paired next-generation sequencing results, also showed poor reliability (ICC 0.24). Cases near the recommended 20% visual threshold for molecular techniques exhibited higher values with digital analysis. In the second ring trial reliability slightly improved and human errors were reduced from 5.6% to 1.25%. Most discrepancies arose from subjective tasks, such as the annotation process, suggesting potential improvement with future artificial intelligence solutions.

Artificial intelligence algorithm for neoplastic cell percentage estimation and its application to copy number variation in urinary tract cancer

BACKGROUND

Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable.

METHODS

To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed.

RESULTS

Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%.

CONCLUSIONS

These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.

Pathologist Computer-Aided Diagnostic Scoring of Tumor Cell Fraction: A Swiss National Study

Tumor cell fraction (TCF) estimation is a common clinical task with well-established large interobserver variability. It thus provides an ideal test bed to evaluate potential impacts of employing a tumor cell fraction computer-aided diagnostic (TCFCAD) tool to support pathologists' evaluation. During a National Slide Seminar event, pathologists (n = 69) were asked to visually estimate TCF in 10 regions of interest (ROIs) from hematoxylin and eosin colorectal cancer images intentionally curated for diverse tissue compositions, cellularity, and stain intensities. Next, they re-evaluated the same ROIs while being provided a TCFCAD-created overlay highlighting predicted tumor vs nontumor cells, together with the corresponding TCF percentage. Participants also reported confidence levels in their assessments using a 5-tier scale, indicating no confidence to high confidence, respectively. The TCF ground truth (GT) was defined by manual cell-counting by experts. When assisted, interobserver variability significantly decreased, showing estimates converging to the GT. This improvement remained even when TCFCAD predictions deviated slightly from the GT. The standard deviation (SD) of the estimated TCF to the GT across ROIs was 9.9% vs 5.8% with TCFCAD (P < .0001). The intraclass correlation coefficient increased from 0.8 to 0.93 (95% CI, 0.65-0.93 vs 0.86-0.98), and pathologists stated feeling more confident when aided (3.67 ± 0.81 vs 4.17 ± 0.82 with the computer-aided diagnostic [CAD] tool). TCFCAD estimation support demonstrated improved scoring accuracy, interpathologist agreement, and scoring confidence. Interestingly, pathologists also expressed more willingness to use such a CAD tool at the end of the survey, highlighting the importance of training/education to increase adoption of CAD systems.

Improving care of melanoma patients through efficient, integrated cellular-molecular pathology workflows using tissue samples with low tumour nuclear content

AIMS

The aim of this quality improvement project was to improve the turnaround time of B-raf proto-oncogene (BRAF) mutation testing in patients with malignant melanoma to support oncologists in making timely treatment decisions.

METHODS

This is a prospective in-house verification of the Idylla BRAF test as compared with DNA panel next-generation sequencing (NGS) performed at an external laboratory.

RESULTS

The Idylla BRAF test had an overall concordance of 95% compared with NGS. This was considered sufficiently good for use in patients with a poor performance status who were at risk of rapid clinical deterioration. Reliable results can be generated using the Idylla BRAF test in tissue sections with tumour neoplastic cell content below 50%. We present a multidisciplinary clinical care algorithm to support dual testing.

CONCLUSIONS

The Idylla BRAF test has the potential to make a significant positive impact on progression-free survival of malignant melanoma patients due to its rapid turnaround time. The Idylla BRAF test can be used as an adjunct to NGS for timely management of patients, particularly those with a poor performance status at presentation.

Delphi: A Democratic and Cost-Effective Method of Consensus Generation in Transplantation

The Thrombotic Microangiopathy Banff Working Group (TMA-BWG) was formed in 2015 to survey current practices and develop minimum diagnostic criteria (MDC) for renal transplant TMA (Tx-TMA). To generate consensus among pathologists and nephrologists, the TMA BWG designed a 3-Phase study. Phase I of the study is presented here. Using the Delphi methodology, 23 panelists with >3 years of diagnostic experience with Tx-TMA pathology listed their MDC suggesting light, immunofluorescence, and electron microscopy lesions, clinical and laboratory information, and differential diagnoses. Nine rounds (R) of consensus resulted in MDC validated during two Rs using online evaluation of whole slide digital images of 37 biopsies (28 TMA, 9 non-TMA). Starting with 338 criteria the process resulted in 24 criteria and 8 differential diagnoses including 18 pathologic, 2 clinical, and 4 laboratory criteria. Results show that 3/4 of the panelists agreed on the diagnosis of 3/4 of cases. The process also allowed definition refinement for 4 light and 4 electron microscopy lesions. For the first time in Banff classification, the Delphi methodology was used to generate consensus. The study shows that Delphi is a democratic and cost-effective method allowing rapid consensus generation among numerous physicians dealing with large number of criteria in transplantation.

Thrombotic Microangiopathy in the Renal Allograft: Results of the TMA Banff Working Group Consensus on Pathologic Diagnostic Criteria

The Banff community summoned the TMA Banff Working Group to develop minimum diagnostic criteria (MDC) and recommendations for renal transplant TMA (Tx-TMA) diagnosis, which currently lacks standardized criteria. Using the Delphi method for consensus generation, 23 nephropathologists (panelists) with >3 years of diagnostic experience with Tx-TMA were asked to list light, immunofluorescence, and electron microscopic, clinical and laboratory criteria and differential diagnoses for Tx-TMA. Delphi was modified to include 2 validations rounds with histological evaluation of whole slide images of 37 transplant biopsies (28 TMA and 9 non-TMA). Starting with 338 criteria in R1, MDC were narrowed down to 24 in R8 generating 18 pathological, 2 clinical, 4 laboratory criteria, and 8 differential diagnoses. The panelists reached a good level of agreement (70%) on 76% of the validated cases. For the first time in Banff classification, Delphi was used to reach consensus on MDC for Tx-TMA. Phase I of the study (pathology phase) will be used as a model for Phase II (nephrology phase) for consensus regarding clinical and laboratory criteria. Eventually in Phase III (consensus of the consensus groups) and the final MDC for Tx-TMA will be reported to the transplantation community.

Tissue management in precision medicine: What the pathologist needs to know in the molecular era

Precision medicine is “an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.” Among many medical specialists involved in precision medicine, the pathologists play an important and key role in the implementation and development of molecular tests that are in the center of decision of many therapeutic choices. Besides many laboratory procedures directly involved in the molecular tests, is fundamental to guarantee that tissues and cells collected for analysis be managed correctly before the DNA/RNA extraction. In this paper we explore the pivotal and interconnected points that can influence molecular studies, such as pre-analytical issues (fixation and decalcification); diagnosis and material selection, including the calculation of nuclei neoplastic fraction. The standardization of sample processing and morphological control ensures the accuracy of the diagnosis. Tissue or cytological samples constitutes the main foundation for the determination of biomarkers and development of druggable targets. Pathology and precision oncology still have a long way to go in terms of research and clinical practice: improving the accuracy and dissemination of molecular tests, learning in molecular tumor boards for advanced disease, and knowledge about early disease. Precision medicine needs pathology to be precise.

Developing consensus in Histopathology: the role of the Delphi method

The Delphi method is a well-established research tool, used for consensus building across a number of fields. Despite its widespread use, and popularity in many medical specialities, there is a paucity of literature on the use of the Delphi method in Histopathology. This literature review seeks to critique the Delphi methodology and explore its potential applications to histopathology-based clinical and research questions. We review those published studies that have utilized the Delphi methodology in Histopathology settings and specifically outline the advantages and limitations of this technique, highlighting situations where its application can be most effective.

The Significance of External Quality Assessment Schemes for Molecular Testing in Clinical Laboratories

Simple Summary

Patients and clinicians often rely on the outcome of laboratory tests, but can we really trust these test results? Good quality management is key for laboratories to guarantee reliable test results. This review focusses on external quality assessment (EQA) schemes which are a tool for laboratories to examine and improve the quality of their testing routines. In this review, an overview of the role and importance of EQA schemes for clinical laboratories is given, and different types of EQA schemes and EQA providers available on the market are discussed, as well as recent developments in the EQA landscape.

Abstract

External quality assessment (EQA) schemes are a tool for clinical laboratories to evaluate and manage the quality of laboratory practice with the support of an independent party (i.e., an EQA provider). Depending on the context, there are different types of EQA schemes available, as well as various EQA providers, each with its own field of expertise. In this review, an overview of the general requirements for EQA schemes and EQA providers based on international guidelines is provided. The clinical and scientific value of these kinds of schemes for clinical laboratories, clinicians and patients are highlighted, in addition to the support EQA can provide to other types of laboratories, e.g., laboratories affiliated to biotech companies. Finally, recent developments and challenges in laboratory medicine and quality management, for example, the introduction of artificial intelligence in the laboratory and the shift to a more individual-approach instead of a laboratory-focused approach, are discussed. EQA schemes should represent current laboratory practice as much as possible, which poses the need for EQA providers to introduce latest laboratory innovations in their schemes and to apply up-to-date guidelines. By incorporating these state-of-the-art techniques, EQA aims to contribute to continuous learning.

Conventional and semi-automatic histopathological analysis of tumor cell content for multigene sequencing of lung adenocarcinoma

Background

Targeted genetic profiling of tissue samples is paramount to detect druggable genetic aberrations in patients with non-squamous non-small cell lung cancer (NSCLC). Accurate upfront estimation of tumor cell content (TCC) is a crucial pre-analytical step for reliable testing and to avoid false-negative results. As of now, TCC is usually estimated on hematoxylin-eosin (H&E) stained tissue sections by a pathologist, a methodology that may be prone to substantial intra- and interobserver variability. Here we the investigate suitability of digital pathology for TCC estimation in a clinical setting by evaluating the concordance between semi-automatic and conventional TCC quantification.

Methods

TCC was analyzed in 120 H&E and thyroid transcription factor 1 (TTF-1) stained high-resolution images by 19 participants with different levels of pathological expertise as well as by applying two semi-automatic digital pathology image analysis tools (HALO and QuPath).

Results

Agreement of TCC estimations [intra-class correlation coefficients (ICC)] between the two software tools (H&E: 0.87; TTF-1: 0.93) was higher compared to that between conventional observers (0.48; 0.47). Digital TCC estimations were in good agreement with the average of human TCC estimations (0.78; 0.96). Conventional TCC estimators tended to overestimate TCC, especially in H&E stainings, in tumors with solid patterns and in tumors with an actual TCC close to 50%.

Conclusions

Our results determine factors that influence TCC estimation. Computer-assisted analysis can improve the accuracy of TCC estimates prior to molecular diagnostic workflows. In addition, we provide a free web application to support self-training and quality improvement initiatives at other institutions.

Rapid estimation of tumor cell percentage in brain tissue biopsy samples using inline cartridge extraction mass spectrometry

Tumor cell percentage (TCP) is an essential characteristic of biopsy samples that directly affects the sensitivity of molecular testing in clinical practice. Apart from clarifying diagnoses, rapid evaluation of TCP combined with various neuronavigation systems can be used to support decision making in neurosurgery. It is known that ambient mass spectrometry makes it possible to rapidly distinguish healthy from malignant tissues. In connection with this, here we demonstrate the possibility of using non-imaging ambient mass spectrometry to evaluate TCP in glial tumor tissues with a high degree of confidence. Molecular profiles of histologically annotated human glioblastoma tissue samples were obtained using the inline cartridge extraction ambient mass spectrometry approach. XGBoost regressors were trained to evaluate tumor cell percentage. Using cross-validation, it was estimated that the TCP was determined by the regressors with a precision of approximately 90% using only low-resolution data. This result demonstrates that ambient mass spectrometry provides an accurate method todetermine TCP in dissected tissues even without implementing mass spectrometry imaging. The application of such techniques offers the possibility to automate routine tissue screening and TCP evaluation to boost the throughput of pathology laboratories. Rapid estimation of tumor cell percentage during neurosurgery.

Genotyping data of routinely processed matched primary/metastatic tumor samples

Genotypic and phenotypic comparisons of tumors in multiple tissue samples from the same patient are important for understanding disease evolution and treatment possibilities. Panel NGS genotyping is currently widely used in this context, whereby NGS variant filtering and final evaluation constitute the basis for meaningful comparisons. Here, we present the genotype data used for genotype / phenotype comparisons between matched primary / metastatic colorectal tumors in the work by Chatzopoulos et al (doi: 10.1016/j.humpath.2020.10.009), as well as the process followed for obtaining these data. We describe key issues while processing routinely formalin-fixed paraffin-embedded (FFPE) tumors for genotyping, NGS application (Ion Torrent), a stringent variant filtering algorithm for genotype analyses in FFPE tissues and particularly in matched tumor samples, and provide the respective datasets. Apart from research, tumor NGS genotyping is currently applied for clinical diagnostic purposes in Oncology. The datasets and method description provided herein (a) are important for comprehending the peculiarities of FFPE tumor genotyping, which is still mostly based on principles of germline DNA genotyping; (b) can be used in pooled analyses, e.g., of primary / metastatic tumors for the investigation of tumor evolution.

Next-generation diagnostics for precision oncology: Preanalytical considerations, technical challenges, and available technologies

Molecular diagnostics as the centrepiece of precision oncology has gone through revolutionary developments over the last decade, becoming tremendously broad, deep and precise with still ongoing advancements. In the majority of scenarios, treatment selection for cancer patients without any type of molecular characterization is no longer conceivable. Considering the impact of sample quality on the reliability of molecular analyses and the importance of the results for the fate of an individual patient, it is surprising how sparsely preanalytical and analytical requirements are addressed scientifically. Standardization and rigorous quality assessment continue to play only a marginal role in the field. Within this review, we will systematically discuss influencing preanalytic parameters and technology setups affecting molecular test results. We will shed light on the specifics of different analytes, technical modalities, and analysis pipelines. The review will have a certain focus on broad molecular genetic tumour testing with next generation sequencing but will go beyond that including other molecular diagnostic modalities and will give a glimpse into the future of molecular testing.

Neoplastic cell percentage estimation in tissue samples for molecular oncology: recommendations from a modified Delphi study

AIMS

Results from external quality assessment revealed considerable variation in neoplastic cell percentages (NCP) estimation in samples for biomarker testing. As molecular biology tests require a minimal NCP, overestimations may lead to false negative test results. We aimed to develop recommendations to improve the NCP determination in a prototypical entity - colorectal carcinoma - that can be adapted for other cancer types.

METHODS AND RESULTS

A modified Delphi study was conducted to reach consensus by 10 pathologists from 10 countries with experience in determining the NCP for colorectal adenocarcinoma. This study included two online surveys and a decision-making meeting. Consensus was defined a priori as an agreement of > 80%. All pathologists completed both surveys. Consensus was reached for 8 out of 19 and 2 out of 13 questions in the first and second surveys, respectively. Remaining issues were resolved during the meeting. Twenty-four recommendations were formulated. Major recommendations resulted as follows: only pathologists should conduct the morphological evaluation; nevertheless molecular biologists/technicians may estimate the NCP, if specific training has been performed and a pathologist is available for feedback. The estimation should be determined in the area with the highest density of viable neoplastic cells and lowest density of inflammatory cells. Other recommendations concerned: the determination protocol itself, needs for micro- and macro-dissection, reporting and interpreting, referral practices and applicability to other cancer types.

CONCLUSION

We believe these recommendations may lead to more accurate NCP estimates, ensuring the correct interpretation of test results, and might help in validating digital algorithms in the future.