Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling

Ductal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and although it has high accuracy (~88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240 DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08±1.22%, a specificity of 93.58±1.49 and an accuracy of 93.83±0.96. The proposed method gives superior performance than eight state-of-theart approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve diagnostic accuracy.British Heart Foundation Accelerator Award, UKRoyal Society International Exchanges Cost Share Award, UK RP202G0230Hope Foundation for Cancer Research, UK RM60G0680Medical Research Council Confidence in Concept Award, UK MC_PC_17171MINECO/FEDER, Spain/Europe RTI2018-098913-B100 A-TIC-080-UGR1

Lessons from a breast cell annotation competition series for school pupils

Due to COVID-19 outbreaks, most school pupils have had to be home-schooled for long periods of time. Two editions of a web-based competition “Beat the Pathologists” for school age participants in the UK ran to fill up pupils’ spare time after home-schooling and evaluate their ability on contributing to AI annotation. The two editions asked the participants to annotate different types of cells on Ki67 stained breast cancer images. The Main competition was at four levels with different level of complexity. We obtained annotations of four kinds of cells entered by school pupils and ground truth from expert pathologists. In this paper, we analyse school pupils’ performance on differentiating different kinds of cells and compare their performance with two neural networks (AlexNet and VGG16). It was observed that children tend to get very good performance in tumour cell annotation with the best F1 measure 0.81 which is a metrics taking both false positives and false negatives into account. Low accuracy was achieved with F1 score 0.75 on positive non-tumour cells and 0.59 on negative non-tumour cells. Superior performance on non-tumour cell detection was achieved by neural networks. VGG16 with training from scratch achieved an F1 score over 0.70 in all cell categories and 0.92 in tumour cell detection. We conclude that non-experts like school pupils have the potential to contribute to large-scale labelling for AI algorithm development if sufficient training activities are organised. We hope that competitions like this can promote public interest in pathology and encourage participation by more non-experts for annotation

The clinical and biological significance of HER2 over-expression in breast ductal carcinoma in situ: a large study from a single institution

BACKGROUND: Previous studies have reported up to 50% of ductal carcinoma in situ (DCIS), is HER2 positive, but the frequency of HER2-positive invasive breast cancer (IBC) is lower. The aim of this study is to characterise HER2 status in DCIS and assess its prognostic value. METHODS: HER2 status was evaluated in a large series of DCIS (n = 868), including pure DCIS and DCIS associated with IBC, prepared as tissue microarrays (TMAs). HER2 status was assessed using immunohistochemistry (IHC) and chromogenic in situ hybridisation (CISH). RESULTS: In pure DCIS, HER2 protein was over-expressed in 9% of DCIS (3+), whereas 15% were HER2 equivocal (2+). Using CISH, the final HER2 status was positive in 20%. In mixed DCIS, HER2 amplification of the DCIS component was detected in 15% with amplification in the invasive component of only 12%. HER2-positive DCIS was associated with features of aggressiveness (p < 0.0001) and more frequent local recurrence (p = 0.03). On multivariate analysis, combined HER2+/Ki67+ profile was an independent predictor of local recurrence (p = 0.006). CONCLUSIONS: The frequency of HER2 positivity in DCIS is comparable to IBC- and HER2-positive DCIS is associated with features of poor prognosis. The majority of HER2 over-expression in DCIS is driven by gene amplification

Prolyl-4-hydroxylase A subunit 2 (P4HA2) expression is a predictor of poor outcome in breast ductal carcinoma in situ (DCIS)

BACKGROUND: Extracellular matrix (ECM) plays a crucial role in tumour behaviour. Prolyl-4-hydroxlase-A2 (P4HA2) is a key enzyme in ECM remodelling. This study aims to evaluate the prognostic significance of P4HA2 in breast ductal carcinoma in situ (DCIS). METHODS: P4HA2 expression was assessed immunohistochemically in malignant cells and surrounding stroma of a large DCIS cohort comprising 481 pure DCIS and 196 mixed DCIS and invasive carcinomas. Outcome analysis was evaluated using local recurrence free interval (LRFI). RESULTS: High P4HA2 expression was detected in malignant cells of half of pure DCIS whereas its expression in stroma was seen in 25% of cases. Higher P4HA2 expression was observed in mixed DCIS cases compared to pure DCIS both in tumour cells and in stroma. High P4HA2 was associated with features of high risk DCIS including younger age, higher grade, comedo necrosis, triple negative and HER2-positive phenotypes. Interaction between P4HA2 and radiotherapy was also observed regarding the outcome. High P4HA2 expression was an independent prognostic factor in predicting shorter LRFI. CONCLUSION: P4HA2 plays a role in DCIS progression and can potentially be used to predict DCIS outcome. Incorporation of P4HA2 with other clinicopathological parameters could refine DCIS risk stratification that can potentially guide management decisions