Evaluation Criteria for Chromosome Instability Detection by FISH to Predict Malignant Progression in Premalignant Glottic Laryngeal Lesions.

Background: The definition of objective, clinically applicable evaluation criteria for FISH 1c/7c in laryngeal precursor lesions for the detection of chromosome instability (CI). Copy Number Variations (CNV) for chromosomes 1 and 7 reflect the general ploidy status of premalignant head and neck lesions and can therefore be used as a marker for CI. Methods: We performed dual-target FISH for chromosomes 1 and 7 centromeres on 4 µm formalin-fixed, paraffin-embedded tissue sections of 87 laryngeal premalignancies to detect CNVs. Thirty-five normal head and neck squamous cell samples were used as a control. First, the chromosome 7:1 ratio (CR) was evaluated per lesion. The normal range of CRs (≥0.84 ≤ 1.16) was based on the mean CR +/− 3 x SD found in the normal population. Second, the percentage of aberrant nuclei, harboring > 2 chromosomes of chromosome 1 and/or 7 (PAN), was established (cut-off value for abnormal PAN ≥ 10%). Results: PAN showed a stronger correlation with malignant progression than CR (resp. OR 5.6, p = 0.001 and OR 3.8, p = 0.009). PAN combined with histopathology resulted in a prognostic model with an area under the ROC curve (AUC) of 0.75 (s.e. 0.061, sensitivity 71%, specificity 70%). Conclusions: evaluation criteria for FISH 1c/7c based on PAN ≥ 10% provide the best prognostic information on the risk of malignant progression of premalignant laryngeal lesions as compared with criteria based on the CR. FISH 1c/7c detection can be applied in combination with histopathological assessment.

Histological subtypes in triple negative breast cancer are associated with specific information on survival.

Much research has focused on finding novel prognostic biomarkers for triple negative breast cancer (TNBC), whereas only scattered information about the relation between histopathological features and survival in TNBC is available. This study aims to explore the prognostic value of histological subtypes in TNBC. A multicenter retrospective TNBC cohort was established from five Dutch hospitals. All non-neoadjuvantly treated, stage I-III patients with estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 negative breast cancer diagnosed between 2006 and 2014 were included. Clinical and follow-up data (overall survival; OS, relapse free survival; RFS) were retrieved and a central histopathological review was performed. Of 597 patients included (median follow up 62.8 months, median age at diagnosis 56.0 years), 19.4% developed a recurrence. The most prevalent histological subtypes were carcinoma of no special type (NST) (88.4%), metaplastic carcinoma (4.4%) and lobular carcinoma (3.4%). Collectively, tumors of special type were associated with a worse RFS and OS compared to carcinoma NST (RFS HR 1.89; 95% CI 1.18-3.03; p = 0.008; OS HR 1.94; 95% CI 1.28-2.92; p = 0.002). Substantial differences in survival, however, were present between the different histological subtypes. In the presented TNBC cohort, special histological subtype was in general associated with less favorable survival. However, within the group of tumors of special type there were differences in survival between the different subtypes. Accurate histological examination can provide specific prognostic information that may potentially enable more personalized treatment and surveillance regimes for TNBC patients.

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.

Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer.

PURPOSE: The prognostic value of mitotic count for invasive breast cancer is firmly established. As yet, however, limited studies have been aimed at assessing mitotic counts as a prognostic factor for triple negative breast cancers (TNBC). Here, we assessed the prognostic value of absolute mitotic counts for TNBC, using both deep learning and manual procedures. METHODS: A retrospective TNBC cohort (n = 298) was used. The absolute manual mitotic count was assessed by averaging counts from three independent observers. Deep learning was performed using a convolutional neural network on digitized H&E slides. Multivariable Cox regression models for relapse-free survival and overall survival served as baseline models. These were expanded with dichotomized mitotic counts, attempting every possible cut-off value, and evaluated by means of the c-statistic. RESULTS: We found that per 2 mm2 averaged manual mitotic counts ranged from 1 to 187 (mean 37.6, SD 23.4), whereas automatic counts ranged from 1 to 269 (mean 57.6; SD 42.2). None of the cut-off values improved the models' baseline c-statistic, for both manual and automatic assessments. CONCLUSIONS: Based on our results we conclude that the level of proliferation, as reflected by mitotic count, does not serve as a prognostic factor for TNBC. Therefore, TNBC patient management based on mitotic count should be discouraged.