Impact of breast cancer grade discordance on prediction of outcome

U.S. payer budget impact of using an AI-augmented cancer risk discrimination digital histopathology platform to identify high-risk of recurrence in women with early-stage invasive breast cancer

AIMS

Use of gene expression signatures to predict adjuvant chemotherapy benefit in women with early-stage breast cancer is increasing. However, high cost, limited access, and eligibility for these tests results in the adoption of less precise assessment approaches. This study evaluates the cost impact of PreciseDx Breast (PDxBr), an AI-augmented histopathology platform that assesses the 6-year risk of recurrence in early-stage invasive breast cancer patients to help improve informed use of adjuvant chemotherapy.

MATERIALS AND METHODS

A decision-tree Markov model was developed to compare the costs of treatment guided by standard of care (SOC) risk assessment (i.e. clinical diagnostic workup with or without Oncotype DX) versus PDxBr with SOC in a hypothetical cohort of U.S. women with early-stage invasive breast cancer. A commercial payer perspective compares costs of testing, adjuvant therapy, recurrence, adverse events, surveillance, and end-of-life care.

RESULTS

PDxBr use in prognostic evaluation resulted in savings of $4 million (M) in year one compared to current SOC in 1 M females members. Over 6-years, savings increased to $12.5 M. The per-treated patient costs in year one amounted to $19.5 thousand (K) for SOC and $16.9K for PDxBr.

LIMITATIONS

For simplicity, recurrence was not specified. We performed scenario analyses to account for variations in rates for local, regional, and distant recurrence. Second, a recurrent patient incurs the total cost of treated recurrence in the first year and goes back to remission or death. Third, CDK4/6i treatment is only incorporated in the recurrence costs but not in the first line of treatment for early-stage breast cancer due to limited data.

CONCLUSIONS

Sensitivity analyses demonstrated robust overall savings to changes in all variables in the model. The use of PDxBr to assess breast cancer recurrence risk has the potential to fill gaps in care and reduce costs when gene expression signatures are not available.

Assessment of nuclear grade-based recurrence risk classification in patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative, node-positive high-risk early breast cancer

BACKGROUND

Histological grade (HG) has been used in the MonrachE trial to select patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, node-positive high-risk early breast cancer (EBC). Although nuclear grade (NG) is widely used in Japan, it is still unclear whether replacing HG with NG can appropriately select high-risk patients.

METHODS

We retrospectively reviewed 647 patients with HR-positive, HER2-negative, node-positive EBC and classified them into the following four groups: group 1: ≥ 4 positive axillary lymph nodes (pALNs) or 1-3 pALNs and either grade 3 of both grading systems or tumors ≥ 5 cm; group 2: 1-3 pALNs, grade < 3, tumor < 5 cm, and Ki-67 ≥ 20%; group 3: 1-3 pALNs, grade < 3, tumor < 5 cm, and Ki-67 < 20%; and group 4: group 2 or 3 by HG classification but group 1 by NG classification. We compared invasive disease-free survival (IDFS) and distant relapse-free survival (DRFS) among the four groups using the Kaplan-Meier method with the log-rank test.

RESULTS

Group 1 had a significantly worse 5-year IDFS and DRFS than groups 2 and 3 (IDFS 80.8% vs. 89.5%, P = 0.0319, 80.8% vs. 95.5%, P = 0.002; DRFS 85.2% vs. 95.3%, P = 0.0025, 85.2% vs. 98.4%, P < 0.001, respectively). Group 4 also had a significantly worse 5-year IDFS (78.0%) and DRFS (83.6%) than groups 2 and 3.

CONCLUSIONS

NG was useful for stratifying the risk of recurrence in patients with HR-positive, HER2-negative, node-positive EBC and was the appropriate risk assessment for patient groups not considered high-risk by HG classification.

Agreement on risk assessment and chemotherapy recommendations among breast cancer specialists: A survey within the MINDACT cohort

PURPOSE

Tailored recommendation for adjuvant chemotherapy in breast cancer patients is of great importance. This survey assessed agreement among oncologists on risk assessment and chemotherapy recommendation, the impact of adding the 70-gene signature to clinical-pathological characteristics, and changes over time.

METHODS

A survey consisting of 37 discordant patient cases from the MINDACT trial (T1-3N0-1M0) was sent to European breast cancer specialists for assessment of risk (high or low) and chemotherapy administration (yes or no). In 2015 the survey was sent twice (survey 1 and 2), several weeks apart, and in 2021 a third time (survey 3). Only the second and third surveys included the 70-gene signature result.

RESULTS

41 breast cancer specialists participated in all three surveys. Overall agreement between respondents decreased slightly between survey 1 and 2, but increased again in survey 3. Over time there was an increase in agreement with the 70-gene signature result on risk assessment, 23% in survey 2 versus 1 and 11% in survey 3 versus 2. With information available indicating a low risk 70-gene signature (n = 25 cases), 20% of risk assessments changed from high to low and 19% of recommendations changed from yes to no chemotherapy in survey 2 versus 1, further increasing with 18% and 21%, respectively, in survey 3 versus 2.

CONCLUSION

There is a variability in risk assessment of early breast cancer patients among breast cancer specialists. The 70-gene signature provided valuable information, resulting in fewer patients being assessed as high risk and fewer recommendations for chemotherapy, increasing over time.

Web-grading-a tool to test personal grading of renal and prostate cancer

Only a few pathologists have the opportunity to verify their personal grading through objective assessment. This study introduces a web-based grading platform to facilitate and validate the grading of renal cell carcinoma and prostate cancer. Two representative images of two clinically annotated cohorts of 100 cases each of prostate and renal cell carcinoma were used. Each participant was asked to grade a tumor series utilizing a three tiered grading system. Finally, a Kaplan-Meier curve was drawn, and the log-rank test was used for statistical testing of the p-value. The grading of 22 participants (68%) achieved prognostic significance. Further analysis highlighted that only two pathologists were able to reliably separate low- and high-grade tumors from intermediate grades. The limitations of this study are the low number of participants in each of the cohorts and the potential selection bias of the tumor images. This web-based grading portal facilitates the assessment of the validity of grading by individual pathologists. The observation that most participants can only successfully identify high- or low-grade tumors but cannot discriminate between more subtle intermediate grades does indicate that there is a need for the development of more formal training programs for tumor grading.

Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years

BACKGROUND

Breast cancer (BC) grading plays a critical role in patient management despite the considerable inter- and intra-observer variability, highlighting the need for decision support tools to improve reproducibility and prognostic accuracy for use in clinical practice. The objective was to evaluate the ability of a digital artificial intelligence (AI) assay (PDxBr) to enrich BC grading and improve risk categorization for predicting recurrence.

METHODS

In our population-based longitudinal clinical development and validation study, we enrolled 2075 patients from Mount Sinai Hospital with infiltrating ductal carcinoma of the breast. With 3:1 balanced training and validation cohorts, patients were retrospectively followed for a median of 6 years. The main outcome was to validate an automated BC phenotyping system combined with clinical features to produce a binomial risk score predicting BC recurrence at diagnosis.

RESULTS

The PDxBr training model (n = 1559 patients) had a C-index of 0.78 (95% CI, 0.76-0.81) versus clinical 0.71 (95% CI, 0.67-0.74) and image feature models 0.72 (95% CI, 0.70-0.74). A risk score of 58 (scale 0-100) stratified patients as low or high risk, hazard ratio (HR) 5.5 (95% CI 4.19-7.2, p < 0.001), with a sensitivity 0.71, specificity 0.77, NPV 0.95, and PPV 0.32 for predicting BC recurrence within 6 years. In the validation cohort (n = 516), the C-index was 0.75 (95% CI, 0.72-0.79) versus clinical 0.71 (95% CI 0.66-0.75) versus image feature models 0.67 (95% CI, 0.63-071). The validation cohort had an HR of 4.4 (95% CI 2.7-7.1, p < 0.001), sensitivity of 0.60, specificity 0.77, NPV 0.94, and PPV 0.24 for predicting BC recurrence within 6 years. PDxBr also improved Oncotype Recurrence Score (RS) performance: RS 31 cutoff, C-index of 0.36 (95% CI 0.26-0.45), sensitivity 37%, specificity 48%, HR 0.48, p = 0.04 versus Oncotype RS plus AI-grade C-index 0.72 (95% CI 0.67-0.79), sensitivity 78%, specificity 49%, HR 4.6, p < 0.001 versus Oncotype RS plus PDxBr, C-index 0.76 (95% CI 0.70-0.82), sensitivity 67%, specificity 80%, HR 6.1, p < 0.001.

CONCLUSIONS

PDxBr is a digital BC test combining automated AI-BC prognostic grade with clinical-pathologic features to predict the risk of early-stage BC recurrence. With future validation studies, we anticipate the PDxBr model will enrich current gene expression assays and enhance treatment decision-making.

Development and validation of nomograms for predicting overall survival and cancer specific survival in locally advanced breast cancer patients: A SEER population-based study

Background

For patients with locally advanced breast cancer (LABC), conventional TNM staging is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making.

Methods

A retrospective analysis of 22,842 LABC patients was performed from 2010 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. An additional cohort of 200 patients from the Binzhou Medical University Hospital (BMUH) was analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to screen for variables. The identified variables were used to build a survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).

Results

The LASSO analysis identified 9 variables in patients with LABC, including age, marital status, Grade, histological type, T-stage, N-stage, surgery, radiotherapy, and chemotherapy. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.767 [95% confidence intervals (95% CI): 0.751-0.775], cancer specific survival (CSS) was 0.765 (95% CI: 0.756-0.774). In the external validation cohort, the C-index of the nomogram in predicting the OS was 0.858 (95% CI: 0.812-0.904), the CSS was 0.866 (95% CI: 0.817-0.915). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.836 (95% CI: 0.821-0.851), 0.769 (95% CI: 0.759-0.780), and 0.750 (95% CI: 0.738-0.762), respectively. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.829 (95% CI: 0.811-0.847), 0.769 (95% CI: 0.757-0.780), and 0.745 (95% CI: 0.732-0.758), respectively. Results of the C-index, ROC curve, and DCA demonstrated that the nomogram was more accurate in predicting the OS and CSS of patients compared with conventional TNM staging.

Conclusion

Two prediction models were developed and validated in this study which provided more accurate prediction of the OS and CSS in LABC patients than the TNM staging. The constructed models can be used for predicting survival outcomes and guide treatment plans for LABC patients.

Quantitative Evaluation of Inflammatory Markers in Peri-Implantitis and Periodontitis Tissues: Digital vs. Manual Analysis—A Proof of Concept Study

Background and Objectives: In dentistry, the assessment of the histomorphometric features of periodontal (PD) and peri-implant (PI) lesions is important to evaluate their underlying pathogenic mechanism. The present study aimed to compare manual and digital methods of analysis in the evaluation of the inflammatory biomarkers in PI and PD lesions. Materials and Methods: PD and PI inflamed soft tissues were excised and processed for histological and immunohistochemical analyses for CD3+, CD4+, CD8+, CD15+, CD20+, CD68+, and CD138+. The obtained slides were acquired using a digital scanner. For each marker, 4 pictures per sample were extracted and the area fraction of the stained tissue was computed both manually using a 594-point counting grid (MC) and digitally using a dedicated image analysis software (DC). To assess the concordance between MC and DC, two blinded observers analysed a total of 200 pictures either with good quality of staining or with non-specific background noise. The inter and intraobserver concordance was evaluated using the intraclass coefficient and the agreement between MC and DC was assessed using the Bland–Altman plot. The time spent analysing each picture using the two methodologies by both observers was recorded. Further, the amount of each marker was compared between PI and PD with both methodologies. Results: The inter- and intraobserver concordance was excellent, except for images with background noise analysed using DC. MC and DC showed a satisfying concordance. DC was performed in half the time compared to MC. The morphological analysis showed a larger inflammatory infiltrate in PI than PD lesions. The comparison between PI and PD showed differences for CD68+ and CD138+ expression. Conclusions: DC could be used as a reliable and time-saving procedure for the immunohistochemical analysis of PD and PI soft tissues. When non-specific background noise is present, the experience of the pathologist may be still required.

Atypia in breast pathology: what pathologists need to know

Despite the importance of atypia in diagnosing and classifying breast lesions, the definition of atypia varies depending on the context, with a lack of consistent and objective criteria for assessment. Atypia in breast pathology may be cytonuclear and/or architectural with different applications and implications. Cytonuclear atypia is used to assist the distinction of various intraductal epithelial proliferative lesions including usual ductal hyperplasia (UDH) versus atypical ductal hyperplasia (ADH) or ductal carcinoma in situ (DCIS), and to grade DCIS. In invasive carcinoma, nuclear atypia (i.e., nuclear pleomorphism) is a component of the histological grading system. Stromal cell cytonuclear atypia is one of the key features used to distinguish fibroadenoma from phyllodes tumour (PT) and to classify PT as benign, borderline or malignant. Similarly, cytonuclear atypia is used in the evaluation of myoepithelial cell alterations in the breast. Architectural atypia is used to differentiate flat epithelial atypia (FEA) from ADH or DCIS. In addition to the inherent subjectivity in the interpretation of atypia, which presents as a morphological continuum reflecting a biological spectrum, the lack of standardisation in defining atypia augments diagnostic discordance in breast pathology, with potential implications for patient management. Evidence to date suggests that the traditional criteria used to assess atypia may require modification in the era of digital pathology primary diagnosis. This review aims to provide a comprehensive review of atypia in breast pathology with reference to inconsistencies, challenges and limitations.

Forkhead Box I1 in Breast Carcinoma as a Potent Prognostic Factor

Forkhead box (FOX) proteins are family of transcriptional factors and regulate cell growth and differentiation as well as embryogenesis and longevity. Previous studies have demonstrated that several FOX members regulate growth or metastasis of breast carcinoma, but clinical significance of total FOX members remains unclear. We first examined associations between expression of 40 FOX genes and TNM status of 19 breast carcinoma using microarray data. Subsequently, we immunolocalized FOXI1 in 140 breast carcinomas and evaluated its clinicopathological significance. In the microarray analysis, we newly identified that gene expression of FOXI1 was most pronouncedly linked to metastasis of the breast carcinoma among the FOX members examined. However, clinicopathological significance of FOXI1 has not been examined in the breast carcinoma. FOXI1 immunoreactivity was positive in 44 out of 140 (31%) of breast carcinomas, and it was significantly associated with stage, lymph node metastasis and distant metastasis. The FOXI1 status was significantly associated with worse prognosis of the breast cancer patients, and it turned out to be an independent prognostic factor for both distant disease-free survival and breast cancer-specific survival. These findings suggest that FOXI1 plays important roles in the metastasis of breast carcinoma and immunohistochemical FOXI1 status is a potent prognostic factor.

Artificial intelligence grading of breast cancer: a promising method to refine prognostic classification for management precision

AIM

Artificial intelligence (AI)-based breast cancer grading may help to overcome perceived limitations of human assessment. Here, the potential value of AI grade was evaluated at the molecular level and in predicting patient outcome.

METHODS AND RESULTS

A supervised convolutional neural network (CNN) model was trained on images of 612 breast cancers from The Cancer Genome Atlas (TCGA). The test set, obtained from the Cooperative Human Tissue Network (CHTN), comprised 1058 cancers with corresponding survival data. Upon reversal, a CNN was trained from images of 1537 CHTN cancers and tested on 397 TCGA cancers. In TCGA, mRNA models were trained using AI grade and Nottingham grade (NG) as labels. Performance of mRNA models in predicting patient outcome was evaluated using data from 1807 cancers from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. In selecting images for training, nucleolar prominence determined high- versus low-grade cancer cells. In CHTN, NG corresponded to significant survival stratification in stages 1, 2 and 3 cancers, while AI grade showed significance in stages 1 and 2 and borderline in stage 3 tumours. In METABRIC, the mRNA model trained from AI grade was not significantly different to the NG-based model. The gene which best described AI grade was TRIP13, a gene involved with mitotic spindle assembly.

CONCLUSION

An AI grade trained from the morphologically distinctive feature of nucleolar prominence could transmit significant patient outcome information across three independent patient cohorts. AI grade shows promise in gene discovery and for second opinions.

Histologic grading of breast carcinoma: a multi-institution study of interobserver variation using virtual microscopy

Breast carcinoma grading is an important prognostic feature recently incorporated into the AJCC Cancer Staging Manual. There is increased interest in applying virtual microscopy (VM) using digital whole slide imaging (WSI) more broadly. Little is known regarding concordance in grading using VM and how such variability might affect AJCC prognostic staging (PS). We evaluated interobserver variability amongst a multi-institutional group of breast pathologists using digital WSI and how discrepancies in grading would affect PS. A digitally scanned slide from 143 invasive carcinomas was independently reviewed by 6 pathologists and assigned grades based on established criteria for tubule formation (TF), nuclear pleomorphism (NP), and mitotic count (MC). Statistical analysis was performed. Interobserver agreement for grade was moderate (κ = 0.497). Agreement was fair (κ = 0.375), moderate (κ = 0.491), and good (κ = 0.705) for grades 2, 3, and 1, respectively. Observer pair concordance ranged from fair to good (κ = 0.354-0.684) Perfect agreement was observed in 43 cases (30%). Interobserver agreement for the individual components was best for TF (κ = 0.503) and worst for MC (κ = 0.281). Seventeen of 86 (19.8%) discrepant cases would have resulted in changes in PS and discrepancies most frequently resulted in a PS change from IA to IB (n = 9). For two of these nine cases, Oncotype DX results would have led to a PS of 1A regardless of grade. Using VM, a multi-institutional cohort of pathologists showed moderate concordance for breast cancer grading, similar to studies using light microscopy. Agreement was the best at the extremes of grade and for evaluation of TF. Whether the higher variability noted for MC is a consequence of VM grading warrants further investigation. Discordance in grading infrequently leads to clinically meaningful changes in the prognostic stage.

Common plasma protein marker LCAT in aggressive human breast cancer and canine mammary tumor

Breast cancer is one of the most frequently diagnosed cancers. Although biomarkers are continuously being discovered, few specific markers, rather than classification markers, representing the aggressiveness and invasiveness of breast cancer are known. In this study, we used samples from canine mammary tumors in a comparative approach. We subjected 36 fractions of both canine normal and mammary tumor plasmas to high-performance quantitative proteomics analysis. Among the identified proteins, LCAT was selectively expressed in mixed tumor samples. With further MRM and Western blot validation, we discovered that the LCAT protein is an indicator of aggressive mammary tumors, an advanced stage of cancer, possibly highly metastatic. Interestingly, we also found that LCAT is overexpressed in high-grade and lymphnode-positive breast cancer in silico data. We also demonstrated that LCAT is highly expressed in the sera of advanced-stage human breast cancers within the same classification. In conclusion, we identified a possible common plasma protein biomarker, LCAT, that is highly expressed in aggressive human breast cancer and canine mammary tumor.

lncRNA TTN-AS1 facilitates proliferation, invasion, and epithelial-mesenchymal transition of breast cancer cells by regulating miR-139-5p/ZEB1 axis

Breast cancer is a common malignant tumor suffered predominantly by women worldwide, which results in serious levels of morbidity and mortality. To control the effects of the cancer, it is critically important to elucidate the pathophysiological processes by which it occurs and develops. Reports have demonstrated that long noncoding RNAs perform a critical role in the development and metastasis of cancers. The lncRNA TTN-AS1 is considered carcinogenic. Nevertheless, the importance and biological functions of TTN-AS1 in breast cancer require greater exploration. In the current paper, we observed that TTN-AS1 expression was significantly upregulated in breast cancer tissues/cells compared with those that are healthy. TTN-AS1 enhanced the proliferation, migration, invasion, and epithelial-mesenchymal transformation of breast cancer cells. Furthermore, a direct target of TTN-AS1, miR-139-5p was negatively regulated. In addition, zinc finger E-box binding homeobox 1 (ZEB1) is an important nuclear transcription factor, the expression of which is increased in multiple tumors. Here, we also found that ZEB1 is a target of miR-139-5p, of which TTN-AS1 could regulate the expression through competition with miR-139-5p. That is, TTN-AS1 promoted proliferation and invasion of breast cancer cells by interaction with the miR-139-5p/ZEB1 axis. In conclusion, the present study aimed to illustrate the significance of TTN-AS1 in breast cancer metastasis and contribute to potentially innovative strategies for its treatment.

Interobserver Variability in Ductal Carcinoma In Situ of the Breast

OBJECTIVES

Since most patients with ductal carcinoma in situ (DCIS) of the breast are treated upon diagnosis, evidence on its natural progression to invasive carcinoma is limited. It is estimated that around half of the screen-detected DCIS lesions would have remained indolent if they had never been detected. Many patients with DCIS are therefore probably overtreated. Four ongoing randomized noninferiority trials explore active surveillance as a treatment option. Eligibility for these trials is mainly based on histopathologic features. Hence, the call for reproducible histopathologic assessment has never sounded louder.

METHODS

Here, the available classification systems for DCIS are discussed in depth.

RESULTS

This comprehensive review illustrates that histopathologic evaluation of DCIS is characterized by significant interobserver variability. Future digitalization of pathology, combined with development of deep learning algorithms or so-called artificial intelligence, may be an innovative solution to tackle this problem. However, implementation of digital pathology is not within reach for each laboratory worldwide. An alternative classification system could reduce the disagreement among histopathologists who use "conventional" light microscopy: the introduction of dichotomous histopathologic assessment is likely to increase interobserver concordance.

CONCLUSIONS

Reproducible histopathologic assessment is a prerequisite for robust risk stratification and adequate clinical decision-making. Two-tier histopathologic assessment might enhance the quality of care.

Visual histological assessment of morphological features reflects the underlying molecular profile in invasive breast cancer: a morphomolecular study

AIMS

Tumour genotype and phenotype are related and can predict outcome. In this study, we hypothesised that the visual assessment of breast cancer (BC) morphological features can provide valuable insight into underlying molecular profiles.

METHODS AND RESULTS

The Cancer Genome Atlas (TCGA) BC cohort was used (n = 743) and morphological features, including Nottingham grade and its components and nucleolar prominence, were assessed utilising whole-slide images (WSIs). Two independent scores were assigned, and discordant cases were utilised to represent cases with intermediate morphological features. Differentially expressed genes (DEGs) were identified for each feature, compared among concordant/discordant cases and tested for specific pathways. Concordant grading was observed in 467 of 743 (63%) of cases. Among concordant case groups, eight common DEGs (UGT8, DDC, RGR, RLBP1, SPRR1B, CXorf49B, PSAPL1 and SPRR2G) were associated with overall tumour grade and its components. These genes are related mainly to cellular proliferation, differentiation and metabolism. The number of DEGs in cases with discordant grading was larger than those identified in concordant cases. The largest number of DEGs was observed in discordant grade 1:3 cases (n = 1185). DEGs were identified for each discordant component. Some DEGs were uniquely associated with well-defined specific morphological features, whereas expression/co-expression of other genes was identified across multiple features and underlined intermediate morphological features.

CONCLUSION

Morphological features are probably related to distinct underlying molecular profiles that drive both morphology and behaviour. This study provides further evidence to support the use of image-based analysis of WSIs, including artificial intelligence algorithms, to predict tumour molecular profiles and outcome.

Prognostic significance of nucleolar assessment in invasive breast cancer

AIMS

Nucleolar morphometric features have a potential role in the assessment of the aggressiveness of many cancers. However, the role of nucleoli in invasive breast cancer (BC) is still unclear. The aims of this study were to investigate the optimal method for scoring nucleoli in IBC and their prognostic significance, and to refine the grading of breast cancer (BC) by incorporating nucleolar score.

METHODS AND RESULTS

Digital images acquired from haematoxylin and eosin-stained sections from a large BC cohort were divided into training (n = 400) and validation (n = 1200) sets for use in this study. Four different assessment methods were evaluated in the training set to identify the optimal method associated with the best performance and significant prognostic value. These were: (i) a modified Helpap method; (ii) counting prominent nucleoli (size ≥2.5 µm) in 10 field views (FVs); (iii) counting prominent nucleoli in five FVs; and (iv) counting prominent nucleoli in one FV. The optimal method was applied to the validation set and to an external validation set, i.e. data from The Cancer Genome Atlas (n = 743). Scoring prominent nucleoli in five FVs showed the highest interobserver concordance rate (intraclass correlation coefficient of 0.8) and a significant association with BC-specific survival (P < 0.0001). A high nucleolar score was associated with younger age, larger tumour size, and higher grade. Incorporation of nucleolar score in the Nottingham grading system resulted in a higher significant association with survival than the conventional grade.

CONCLUSIONS

Quantification of nucleolar prominence in five FVs is a cost-efficient and reproducible morphological feature that can predict BC behaviour and can provide an alternative to pleomorphism to improve BC grading performance.

Interobserver variability in upfront dichotomous histopathological assessment of ductal carcinoma in situ of the breast: the DCISion study

Histopathological assessment of ductal carcinoma in situ, a nonobligate precursor of invasive breast cancer, is characterized by considerable interobserver variability. Previously, post hoc dichotomization of multicategorical variables was used to determine the "ideal" cutoffs for dichotomous assessment. The present international multicenter study evaluated interobserver variability among 39 pathologists who performed upfront dichotomous evaluation of 149 consecutive ductal carcinomas in situ. All pathologists independently assessed nuclear atypia, necrosis, solid ductal carcinoma in situ architecture, calcifications, stromal architecture, and lobular cancerization in one digital slide per lesion. Stromal inflammation was assessed semiquantitatively. Tumor-infiltrating lymphocytes were quantified as percentages and dichotomously assessed with a cutoff at 50%. Krippendorff's alpha (KA), Cohen's kappa and intraclass correlation coefficient were calculated for the appropriate variables. Lobular cancerization (KA = 0.396), nuclear atypia (KA = 0.422), and stromal architecture (KA = 0.450) showed the highest interobserver variability. Stromal inflammation (KA = 0.564), dichotomously assessed tumor-infiltrating lymphocytes (KA = 0.520), and comedonecrosis (KA = 0.539) showed slightly lower interobserver disagreement. Solid ductal carcinoma in situ architecture (KA = 0.602) and calcifications (KA = 0.676) presented with the lowest interobserver variability. Semiquantitative assessment of stromal inflammation resulted in a slightly higher interobserver concordance than upfront dichotomous tumor-infiltrating lymphocytes assessment (KA = 0.564 versus KA = 0.520). High stromal inflammation corresponded best with dichotomously assessed tumor-infiltrating lymphocytes when the cutoff was set at 10% (kappa = 0.881). Nevertheless, a post hoc tumor-infiltrating lymphocytes cutoff set at 20% resulted in the highest interobserver agreement (KA = 0.669). Despite upfront dichotomous evaluation, the interobserver variability remains considerable and is at most acceptable, although it varies among the different histopathological features. Future studies should investigate its impact on ductal carcinoma in situ prognostication. Forthcoming machine learning algorithms may be useful to tackle this substantial diagnostic challenge.

Deep learning assisted mitotic counting for breast cancer

As part of routine histological grading, for every invasive breast cancer the mitotic count is assessed by counting mitoses in the (visually selected) region with the highest proliferative activity. Because this procedure is prone to subjectivity, the present study compares visual mitotic counting with deep learning based automated mitotic counting and fully automated hotspot selection. Two cohorts were used in this study. Cohort A comprised 90 prospectively included tumors which were selected based on the mitotic frequency scores given during routine glass slide diagnostics. This pathologist additionally assessed the mitotic count in these tumors in whole slide images (WSI) within a preselected hotspot. A second observer performed the same procedures on this cohort. The preselected hotspot was generated by a convolutional neural network (CNN) trained to detect all mitotic figures in digitized hematoxylin and eosin (H&E) sections. The second cohort comprised a multicenter, retrospective TNBC cohort (n = 298), of which the mitotic count was assessed by three independent observers on glass slides. The same CNN was applied on this cohort and the absolute number of mitotic figures in the hotspot was compared to the averaged mitotic count of the observers. Baseline interobserver agreement for glass slide assessment in cohort A was good (kappa 0.689; 95% CI 0.580-0.799). Using the CNN generated hotspot in WSI, the agreement score increased to 0.814 (95% CI 0.719-0.909). Automated counting by the CNN in comparison with observers counting in the predefined hotspot region yielded an average kappa of 0.724. We conclude that manual mitotic counting is not affected by assessment modality (glass slides, WSI) and that counting mitotic figures in WSI is feasible. Using a predefined hotspot area considerably improves reproducibility. Also, fully automated assessment of mitotic score appears to be feasible without introducing additional bias or variability.

Interobserver variability in breast carcinoma grading results in prognostic stage differences

The AJCC Cancer Staging Manual 8th edition included tumor grade in the pathologic prognostic stage for breast carcinomas. Due to the known subjectivity of tumor grading, we aimed to assess the degree of interobserver agreement for invasive carcinoma grade among pathologists and determine its effect on pathologic prognostic stage. One hundred consecutive cases of invasive stage II carcinomas were independently graded twice, with an 4-week intervening wash-out period, by 6 breast pathologists utilizing established Nottingham grading criteria. Inter- and intra-observer variability was determined for overall grade and for each of the 3 scoring components. Interobserver variability was good to very good (κ range = 0.582-0.850) with even better intra-observer variability (mean κ = 0.766). Tubule score was the most reproducible element (κ = 0.588). Complete concordance was reached in 54 cases and 58 cases in rounds 1 and 2 respectively. In round 1 this resulted in different pathologic prognostic stage in only 25 of discordant cases, 18 of which were stage IA versus IB. In conclusion, grading agreement between pathologists was good to very good and discordant grades resulted in small changes to pathologic prognostic stage.

Dichotomous histopathological assessment of ductal carcinoma in situ of the breast results in substantial interobserver concordance

AIMS

Robust prognostic markers for ductal carcinoma in situ (DCIS) of the breast require high reproducibility and thus low interobserver variability. The aim of this study was to compare interobserver variability among 13 pathologists, in order to enable the identification of robust histopathological characteristics.

METHODS AND RESULTS

One representative haematoxylin and eosin-stained slide was selected for 153 DCIS cases. All pathologists independently assessed nuclear grade, intraductal calcifications, necrosis, solid growth, stromal changes, stromal inflammation, and apocrine differentiation. All characteristics were assessed categorically. Krippendorff's alpha was calculated to assess overall interobserver concordance. Cohen's kappa was calculated for every observer duo to further explore interobserver variability. The highest concordance was observed for necrosis, calcifications, and stromal inflammation. Assessment of solid growth, nuclear grade and stromal changes resulted in lower concordance. Poor concordance was observed for apocrine differentiation. Kappa values for each observer duo identified the 'ideal' cut-off for dichotomisation of multicategory variables. For instance, concordance was higher for 'non-high versus high' nuclear grade than for 'low versus non-low' nuclear grade. 'Absent/mild' versus 'moderate/extensive' stromal inflammation resulted in substantially higher concordance than other dichotomous cut-offs.

CONCLUSIONS

Dichotomous assessment of the histopathological features of DCIS resulted in moderate to substantial agreement among pathologists. Future studies on prognostic markers in DCIS should take into account this degree of interobserver variability to define cut-offs for categorically assessed histopathological features, as reproducibility is paramount for robust prognostic markers in daily clinical practice. A new prognostic index for DCIS might be considered, based on two-tier grading of histopathological features. Future research should explore the prognostic potential of such two-tier assessment.

OLFM4, LY6D and S100A7 as potent markers for distant metastasis in estrogen receptor-positive breast carcinoma

Metastatic breast cancer is a highly lethal disease, and it is very important to evaluate the biomarkers associated with distant metastasis. However, molecular features of distant metastasis remain largely unknown in breast cancer. Estrogens play an important role in the progression of breast cancer and the majority of stage IV breast carcinomas express estrogen receptor (ER). Therefore, in this study, we examined molecular markers associated with distant metastasis in ER-positive breast carcinoma by microarray and immunohistochemistry. When we examined the gene expression profile of ER-positive stage IV breast carcinoma tissues (n = 7) comparing ER-positive stage I-III cases (n = 11) by microarray analysis, we newly identified OLFM4, LY6D and S100A7, which were closely associated with the distant metastasis. Subsequently, we performed immunohistochemistry for OLFM4, LY6D and S100A7 in 168 ER-positive breast carcinomas. OLFM4, LY6D and S100A7 immunoreactivities were significantly associated with stage, pathological T factor, distant metastasis and Ki67 status in the ER-positive breast carcinomas. Moreover, these immunoreactivities were significantly associated with a worse prognostic factor for distant metastasis-free and breast cancer-specific survival in ER-positive stage I-III breast cancer patients. However, when we performed immunohistochemistry for OLFM4, LY6D and S100A7 in 40 ER-negative breast carcinomas, these immunoreactivities were not generally associated with the clinicopathological factors examined, including distant metastasis and prognosis of patients, in this study. These results suggest that OLFM4, LY6D and S100A7 immunoreactivity are associated with an aggressive phenotype of ER-positive breast carcinoma, and these are potent markers for distant metastasis of ER-positive breast cancer patients.