Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images

Romo-Bucheli, David; Janowczyk, Andrew; Romero, Eduardo; Gilmore, Hannah; Madabhushi, Anant

doi:10.1117/12.2211368

Cited by 21 publications

(26 citation statements)

References 23 publications

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“…The results showed that lymphocyte density, as measured in the pre-treatment biopsy, was an independent predictor of pathological complete response (pCR); defined as a complete disappearance of invasive cancer cells after treatment (odds ratio ¼ 2.92-4.46, P < .001) [22]. Other research from Romo-Bucheli et al (2016) [24] have used deep neural networks to identify tubule-nuclei structures from digitized whole-slide images (n ¼ 174) of ER-positive breast tumors. For this study, the deep neural network architecture was structured using a convolutional neural network, a rectifier linear unit (ReLu) and a maximum pooling (max pool) operator [24], and the output layer that consisted of a binary class label for (AE) tubule nuclei.…”

Section: Pathomics: Machine Learning Applications In Breast Oncologymentioning

confidence: 90%

“…Other research from Romo-Bucheli et al (2016) [24] have used deep neural networks to identify tubule-nuclei structures from digitized whole-slide images (n ¼ 174) of ER-positive breast tumors. For this study, the deep neural network architecture was structured using a convolutional neural network, a rectifier linear unit (ReLu) and a maximum pooling (max pool) operator [24], and the output layer that consisted of a binary class label for (AE) tubule nuclei. The ratio between tubule nuclei and the total number of nuclei was indexed as the tubule formation indicator (TFI).…”

Section: Pathomics: Machine Learning Applications In Breast Oncologymentioning

confidence: 99%

“…The ratio between tubule nuclei and the total number of nuclei was indexed as the tubule formation indicator (TFI). Their algorithm demonstrated modest classification results, showing an F-score of 0.59 AE 0.14 and a precision of 0.72 AE 0.12 [24]. The research group's secondary aim was to evaluate the association between the TFI score to the Oncotype DX prognostic risk score.…”

Section: Pathomics: Machine Learning Applications In Breast Oncologymentioning

confidence: 99%

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Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics

Tran

Jerzak

et al. 2019

Journal of Medical Imaging and Radiation Sciences

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Progress in computing power and advances in medical imaging over recent decades have culminated in new opportunities for artificial intelligence (AI), computer vision, and using radiomics to facilitate clinical decision-making. These opportunities are growing in medical specialties, such as radiology, pathology, and oncology. As medical imaging and pathology are becoming increasingly digitized, it is recently recognized that harnessing data from digital images can yield parameters that reflect the underlying biology and physiology of various malignancies. This greater understanding of the behaviour of cancer can potentially improve on therapeutic strategies. In addition, the use of AI is particularly appealing in oncology to facilitate the detection of malignancies, to predict the likelihood of tumor response to treatments, and to prognosticate the patients' risk of cancer-related mortality. AI will be critical for identifying candidate biomarkers from digital imaging and developing robust and reliable predictive models. These models will be used to personalize oncologic treatment strategies, and identify confounding variables that are related to the complex biology of tumors and diversity of patient-related factors (ie, mining ''big data''). This commentary describes the growing body of work focussed on AI for precision oncology. Advances in AI-driven

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Section: Pathomics: Machine Learning Applications In Breast Oncologymentioning

confidence: 90%

Section: Pathomics: Machine Learning Applications In Breast Oncologymentioning

confidence: 99%

Section: Pathomics: Machine Learning Applications In Breast Oncologymentioning

confidence: 99%

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Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics

Tran

Jerzak

et al. 2019

Journal of Medical Imaging and Radiation Sciences

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“…Computerized microscopy image classification approaches have been developed over the years with the aim to provide efficient and consistent image interpretation automatically. In the domain of digital pathology, approaches have been designed for grading or subtyping of various cancers including squamous cell carcinoma [1], prostate cancer [2], brain tumor [3], and breast cancer [4]. In these approaches, histological biomarkers are detected and quantified to encode the morphological characteristics that are critical for the histopathological analysis.…”

mentioning

confidence: 99%

“…In these approaches, histological biomarkers are detected and quantified to encode the morphological characteristics that are critical for the histopathological analysis. For example, to determine the aggressiveness of breast cancer, the ratio of tubule nuclei to overall number of nuclei is an important biomarker and a deep learning technique is designed to detect the tubule nuclei [4]. More reviews are provided in [5] as well.…”

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confidence: 99%

Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification

Song

Huang

et al. 2017

IEEE Trans. Med. Imaging

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Microscopy image classification is important in various biomedical applications, such as cancer subtype identification, and protein localization for high content screening. To achieve automated and effective microscopy image classification, the representative and discriminative capability of image feature descriptors is essential. To this end, in this paper, we propose a new feature representation algorithm to facilitate automated microscopy image classification. In particular, we incorporate Fisher vector (FV) encoding with multiple types of local features that are handcrafted or learned, and we design a separation-guided dimension reduction method to reduce the descriptor dimension while increasing its discriminative capability. Our method is evaluated on four publicly available microscopy image data sets of different imaging types and applications, including the UCSB breast cancer data set, MICCAI 2015 CBTC challenge data set, and IICBU malignant lymphoma, and RNAi data sets. Our experimental results demonstrate the advantage of the proposed low-dimensional FV representation, showing consistent performance improvement over the existing state of the art and the commonly used dimension reduction techniques.

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Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors

Kumar

Verma

Chen

et al. 2022

The Journal of Pathology

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We assessed the utility of quantitative features of colon cancer nuclei, extracted from digitized hematoxylin and eosin‐stained whole slide images (WSIs), to distinguish between stage II and stage IV colon cancers. Our discovery cohort comprised 100 stage II and stage IV colon cancer cases sourced from the University Hospitals Cleveland Medical Center (UHCMC). We performed initial (independent) model validation on 51 (143) stage II and 79 (54) stage IV colon cancer cases from UHCMC (The Cancer Genome Atlas's Colon Adenocarcinoma, TCGA‐COAD, cohort). Our approach comprised the following steps: (1) a fully convolutional deep neural network with VGG‐18 architecture was trained to locate cancer on WSIs; (2) another deep‐learning model based on Mask‐RCNN with Resnet‐50 architecture was used to segment all nuclei from within the identified cancer region; (3) a total of 26 641 quantitative morphometric features pertaining to nuclear shape, size, and texture were extracted from within and outside tumor nuclei; (4) a random forest classifier was trained to distinguish between stage II and stage IV colon cancers using the five most discriminatory features selected by the Wilcoxon rank‐sum test. Our trained classifier using these top five features yielded an AUC of 0.81 and 0.78, respectively, on the held‐out cases in the UHCMC and TCGA validation sets. For 197 TCGA‐COAD cases, the Cox proportional hazards model yielded a hazard ratio of 2.20 (95% CI 1.24–3.88) with a concordance index of 0.71, using only the top five features for risk stratification of overall survival. The Kaplan–Meier estimate also showed statistically significant separation between the low‐risk and high‐risk patients, with a log‐rank P value of 0.0097. Finally, unsupervised clustering of the top five features revealed that stage IV colon cancers with peritoneal spread were morphologically more similar to stage II colon cancers with no long‐term metastases than to stage IV colon cancers with hematogenous spread. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

show abstract

Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images

Cited by 21 publications

References 23 publications

Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics

Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics

Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification

Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors

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