David W Dodington1, Andrew Lagree2, Sami Tabbarah3, Majid Mohebpour2, Ali Sadeghi-Naini3,4, William T Tran2,3,5,6, Fang-I Lu7,8. 1. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada. 2. Biological Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada. 3. Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. 4. Department of Electrical Engineering and Computer Science, York University, Toronto, ON, Canada. 5. Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada. 6. Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada. 7. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada. fangi.lu@sunnybrook.ca. 8. Department of Laboratory Medicine and Molecular Diagnostics, Sunnybrook Health Sciences Centre, 2075 Bayview Ave., Rm E423a, Toronto, ON, M4N 3M5, Canada. fangi.lu@sunnybrook.ca.
Abstract
PURPOSE: Neoadjuvant chemotherapy (NAC) is used to treat patients with high-risk breast cancer. The tumor response to NAC can be classified as either a pathological partial response (pPR) or pathological complete response (pCR), defined as complete eradication of invasive tumor cells, with a pCR conferring a significantly lower risk of recurrence. Predicting the response to NAC, however, remains a significant clinical challenge. The objective of this study was to determine if analysis of nuclear features on core biopsies using artificial intelligence (AI) can predict response to NAC. METHODS: Fifty-eight HER2-positive or triple-negative breast cancer patients were included in this study (pCR n = 37, pPR n = 21). Multiple deep convolutional neural networks were developed to automate tumor detection and nuclear segmentation. Nuclear count, area, and circularity, as well as image-based first- and second-order features including mean pixel intensity and correlation of the gray-level co-occurrence matrix (GLCM-COR) were determined. RESULTS: In univariate analysis, the pCR group had fewer multifocal/multicentric tumors, higher nuclear intensity, and lower GLCM-COR compared to the pPR group. In multivariate binary logistic regression, tumor multifocality/multicentricity (OR = 0.14, p = 0.012), nuclear intensity (OR = 1.23, p = 0.018), and GLCM-COR (OR = 0.96, p = 0.043) were each independently associated with likelihood of achieving a pCR, and the model was able to successful classify 79% of cases (62% for pPR and 89% for pCR). CONCLUSION: Analysis of tumor nuclear features using digital pathology/AI can significantly improve models to predict pathological response to NAC.
PURPOSE: Neoadjuvant chemotherapy (NAC) is used to treat patients with high-risk breast cancer. The tumor response to NAC can be classified as either a pathological partial response (pPR) or pathological complete response (pCR), defined as complete eradication of invasive tumor cells, with a pCR conferring a significantly lower risk of recurrence. Predicting the response to NAC, however, remains a significant clinical challenge. The objective of this study was to determine if analysis of nuclear features on core biopsies using artificial intelligence (AI) can predict response to NAC. METHODS: Fifty-eight HER2-positive or triple-negative breast cancerpatients were included in this study (pCR n = 37, pPR n = 21). Multiple deep convolutional neural networks were developed to automate tumor detection and nuclear segmentation. Nuclear count, area, and circularity, as well as image-based first- and second-order features including mean pixel intensity and correlation of the gray-level co-occurrence matrix (GLCM-COR) were determined. RESULTS: In univariate analysis, the pCR group had fewer multifocal/multicentric tumors, higher nuclear intensity, and lower GLCM-COR compared to the pPR group. In multivariate binary logistic regression, tumor multifocality/multicentricity (OR = 0.14, p = 0.012), nuclear intensity (OR = 1.23, p = 0.018), and GLCM-COR (OR = 0.96, p = 0.043) were each independently associated with likelihood of achieving a pCR, and the model was able to successful classify 79% of cases (62% for pPR and 89% for pCR). CONCLUSION: Analysis of tumor nuclear features using digital pathology/AI can significantly improve models to predict pathological response to NAC.
Entities:
Keywords:
Artificial intelligence; Breast cancer; Digital pathology; Neoadjuvant chemotherapy
Authors: Andrew Lagree; Audrey Shiner; Marie Angeli Alera; Lauren Fleshner; Ethan Law; Brianna Law; Fang-I Lu; David Dodington; Sonal Gandhi; Elzbieta A Slodkowska; Alex Shenfield; Katarzyna J Jerzak; Ali Sadeghi-Naini; William T Tran Journal: Curr Oncol Date: 2021-10-27 Impact factor: 3.677
Authors: Lazaro Hiram Betancourt; Jeovanis Gil; Yonghyo Kim; Viktória Doma; Uğur Çakır; Aniel Sanchez; Jimmy Rodriguez Murillo; Magdalena Kuras; Indira Pla Parada; Yutaka Sugihara; Roger Appelqvist; Elisabet Wieslander; Charlotte Welinder; Erika Velasquez; Natália Pinto de Almeida; Nicole Woldmar; Matilda Marko-Varga; Krzysztof Pawłowski; Jonatan Eriksson; Beáta Szeitz; Bo Baldetorp; Christian Ingvar; Håkan Olsson; Lotta Lundgren; Henrik Lindberg; Henriett Oskolas; Boram Lee; Ethan Berge; Marie Sjögren; Carina Eriksson; Dasol Kim; Ho Jeong Kwon; Beatrice Knudsen; Melinda Rezeli; Runyu Hong; Peter Horvatovich; Tasso Miliotis; Toshihide Nishimura; Harubumi Kato; Erik Steinfelder; Madalina Oppermann; Ken Miller; Francesco Florindi; Qimin Zhou; Gilberto B Domont; Luciana Pizzatti; Fábio C S Nogueira; Peter Horvath; Leticia Szadai; József Tímár; Sarolta Kárpáti; A Marcell Szász; Johan Malm; David Fenyö; Henrik Ekedahl; István Balázs Németh; György Marko-Varga Journal: Clin Transl Med Date: 2021-07