Assisted computer and imaging system improve accuracy of breast tumor size assessment after neoadjuvant chemotherapy

High Dynamic Range Dual-Modal White Light Imaging Improves the Accuracy of Tumor Bed Sampling After Neoadjuvant Therapy for Breast Cancer

OBJECTIVES

Accurate evaluation of residual cancer burden remains challenging because of the lack of appropriate techniques for tumor bed sampling. This study evaluated the application of a white light imaging system to help pathologists differentiate the components and location of tumor bed in specimens.

METHODS

The high dynamic range dual-mode white light imaging (HDR-DWI) system was developed to capture antiglare reflection and multiexposure HDR transmission images. It was tested in 60 specimens of modified radical mastectomy after neoadjuvant therapy. We observed the differential transmittance among tumor tissue, fibrosis tissue, and adipose tissue.

RESULTS

The sensitivity and specificity of HDR-DWI were compared with x-ray or visual examination to determine whether HDR-DWI was superior in identifying tumor beds. We found that tumor tissue had lower transmittance (0.12 ± 0.03) than fibers (0.15 ± 0.04) and fats (0.27 ± 0.07) (P < .01).

CONCLUSIONS

HDR-DWI was more sensitive in identifying fiber and tumor tissues than cabinet x-ray and visual observation (P < .01). In addition, HDR-DWI could identify more fibrosis areas than the currently used whole slide imaging did in 12 samples (12/60). We have determined that HDR-DWI can provide more in-depth tumor bed information than x-ray and visual examination do, which will help prevent diagnostic errors in tumor bed sampling.

Reconstructing virtual large slides can improve the accuracy and consistency of tumor bed evaluation for breast cancer after neoadjuvant therapy

BACKGROUND

To explore whether the "WSI Stitcher", a program we developed for reconstructing virtual large slide through whole slide imaging fragments stitching, can improve the efficiency and consistency of pathologists in evaluating the tumor bed after neoadjuvant treatment of breast cancer compared with the conventional methods through stack splicing of physical slides.

METHODS

This study analyzed the advantages of using software-assisted methods to evaluate the tumor bed after neoadjuvant treatment of breast cancer. This new method is to use "WSI Stitcher" to stitch all the WSI fragments together to reconstruct a virtual large slide and evaluate the tumor bed with the help of the built-in ruler and tumor proportion calculation functions.

RESULTS

Compared with the conventional method, the evaluation time of the software-assisted method was shortened by 35%(P < 0.001). In the process of tumor bed assessment after neoadjuvant treatment of breast cancer, the software-assisted method has higher intraclass correlation coefficient when measuring the length (0.994 versus 0.934), width (0.992 versus 0.927), percentage of residual tumor cells (0.947 versus 0.878), percentage of carcinoma in situ (0.983 versus 0.881) and RCB index(0.997 versus 0.772). The software-assisted method has higher kappa values when evaluating tumor staging(0.901 versus 0.687) and RCB grading (0.963 versus 0.857).

CONCLUSION

The "WSI Stitcher" is an effective tool to help pathologists with the assessment of breast cancer after neoadjuvant treatment.

Progress on deep learning in digital pathology of breast cancer: a narrative review

BACKGROUND AND OBJECTIVE

Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis. However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology.

METHODS

A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied.

KEY CONTENT AND FINDINGS

DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches.

CONCLUSIONS

Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.