Breast tissue imaging atlas using ultra-fast confocal microscopy to identify cancer lesions.
Marie-Christine MathieuMoira RagazziMalek FerchiouPaul J van DiestOdile CasiraghiAicha Ben LakhdarNizar LabaiedAngelica ConversanoMuriel AbbaciPublished in: Virchows Archiv : an international journal of pathology (2024)
New generation ultra-fast fluorescence confocal microscopy (UFCM) allows to image histological architecture of fresh breast tissue and may be used for ex vivo intraoperative analysis for margin status. The criteria to identify breast tumoral and non-tumoral tissues in UFCM images are still objects of investigation. The objective of the study was to create an atlas of ex vivo UFCM images of breast tissues and breast carcinomas based on the first extensive collection of large field-of-view UFCM breast images. One hundred sixty patients who underwent conserving surgery for breast cancer were included. Their fresh surgical specimens were sliced, stained with acridine orange, and imaged at high resolution with large-field-of-view UFCM. The resulting images were digitally false colored to resemble frozen sections. Each UFCM image was correlated with the corresponding definitive histology. Representative images of normal tissue, inflammation, benign lesions, invasive carcinoma (IC), and ductal carcinoma in situ (DCIS) were collected. A total of 320 large-field images were recorded from 58 IC of no special type, 44 invasive lobular carcinomas, 1 invasive mucinous carcinoma, 47 DCIS, 2 lobular carcinomas in situ, and 8 specimens without cancer. Representative images of the main components of the normal breast and the main types of ICs and DCIS were annotated to establish an UFCM atlas. UFCM enables the imaging of the fresh breast tissue sections. Main morphological criteria defined in traditional histopathology such as tissue architecture and cell features can be applied to describe UFCM images content. The generated atlas of the main normal or tumoral tissue features will support the adoption of this optical technology for the intraoperative examination of breast specimens in clinical practice as it can be used to train physicians on UFCM images and develop artificial intelligence algorithms. Further studies are needed to document rare breast lesions.
Keyphrases
- deep learning
- high resolution
- convolutional neural network
- artificial intelligence
- optical coherence tomography
- single cell
- machine learning
- gene expression
- clinical practice
- minimally invasive
- squamous cell carcinoma
- end stage renal disease
- newly diagnosed
- oxidative stress
- cross sectional
- ejection fraction
- papillary thyroid
- big data
- single molecule
- neoadjuvant chemotherapy
- radiation therapy
- coronary artery bypass
- locally advanced