iDermatoPath - a novel software tool for mitosis detection in H&E-stained tissue sections of malignant melanoma.

BACKGROUND: Malignant Melanoma (MM) is characterized by a growing incidence and a high malignant potential. Besides well-defined prognostic factors such as tumour thickness and ulceration, the Mitotic Rate (MR) was included in the AJCC recommendations for diagnosis and treatment of MM. In daily routine, the identification of a single mitosis can be difficult on haematoxylin and eosin slides alone. Several studies showed a big inter- and intra-individual variability in detecting the MR in MM even by very experienced investigators, thus raising the question for a computer-assisted method. OBJECTIVE: The objective was to develop a software system for mitosis detection in MM on H&E slides based on machine learning for diagnostic support. METHODS: We developed a computer-aided staging support system based on image analysis and machine learning on the basis of 59 MM specimens. Our approach automatically detects tumour regions, identifies mitotic nuclei and classifies them with respect to their diagnostic relevance. A convenient user interface enables the investigator to browse through the proposed mitoses for fast and efficient diagnosing. RESULTS: A quantitative evaluation on manually labelled ground truth data revealed that the tumour region detection yields a medium spatial overlap index (dice coefficient) of 0.72. For the mitosis detection, we obtained high accuracies of above 83%. CONCLUSION: On the technical side, the developed iDermatoPath software tool provides a novel approach for mitosis detection in MM, which can be further improved using more training data such as dermatopathologist annotations. On the practical side, a first evaluation of the clinical utility was positive, albeit this approach provides most benefit for difficult cases in a research setting. Assuming all slides to be digitally processed and reported in the near future, this method could become a helpful additional tool for the pathologist.

In-silico insights on the prognostic potential of immune cell infiltration patterns in the breast lobular epithelium.

Scattered inflammatory cells are commonly observed in mammary gland tissue, most likely in response to normal cell turnover by proliferation and apoptosis, or as part of immunosurveillance. In contrast, lymphocytic lobulitis (LLO) is a recurrent inflammation pattern, characterized by lymphoid cells infiltrating lobular structures, that has been associated with increased familial breast cancer risk and immune responses to clinically manifest cancer. The mechanisms and pathogenic implications related to the inflammatory microenvironment in breast tissue are still poorly understood. Currently, the definition of inflammation is mainly descriptive, not allowing a clear distinction of LLO from physiological immunological responses and its role in oncogenesis remains unclear. To gain insights into the prognostic potential of inflammation, we developed an agent-based model of immune and epithelial cell interactions in breast lobular epithelium. Physiological parameters were calibrated from breast tissue samples of women who underwent reduction mammoplasty due to orthopedic or cosmetic reasons. The model allowed to investigate the impact of menstrual cycle length and hormone status on inflammatory responses to cell turnover in the breast tissue. Our findings suggested that the immunological context, defined by the immune cell density, functional orientation and spatial distribution, contains prognostic information previously not captured by conventional diagnostic approaches.