Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence

Howard, Frederick M.; Dolezal, James M.; Kochanny, Sara; Khramtsova, Galina; Vickery, Jasmine; Srisuwananukorn, Andrew; Woodard, A.; Chen, Nan; Nanda, Rita; Perou, Charles M.; Olopade, Olufunmilayo I.; Huo, Dezheng; Pearson, Alexander T.

doi:10.1038/s41523-023-00530-5

Cited by 23 publications

(10 citation statements)

References 38 publications

(41 reference statements)

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“…According to previous studies, clinical nomograms have been applied to breast diseases in numerous aspects, such as predicting breast cancer, assessing the diagnostic performance of imaging methods in breast diseases ( 18 – 21 ), predicting axillary lymph node metastasis of breast cancer ( 22 ), and predicting the recurrence rate and risk of breast cancer ( 23 ), etc. In the ultrasonic differentiation between SA and malignant tumors, the findings of Liang’s study were consistent with ours ( 17 ).…”

Section: Discussionmentioning

confidence: 99%

A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram

Li,

Wei,

Pang

et al. 2023

Front. Oncol.

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ObjectiveTo analyze the clinical and ultrasonic characteristics of breast sclerosing adenosis (SA) and invasive ductal carcinoma (IDC), and construct a predictive nomogram for SA.Materials and methodsA total of 865 patients were recruited at the Second Hospital of Shandong University from January 2016 to November 2022. All patients underwent routine breast ultrasound examinations before surgery, and the diagnosis was confirmed by histopathological examination following the operation. Ultrasonic features were recorded using the Breast Imaging Data and Reporting System (BI-RADS). Of the 865 patients, 203 (252 nodules) were diagnosed as SA and 662 (731 nodules) as IDC. They were randomly divided into a training set and a validation set at a ratio of 6:4. Lastly, the difference in clinical characteristics and ultrasonic features were comparatively analyzed.ResultThere was a statistically significant difference in multiple clinical and ultrasonic features between SA and IDC (P<0.05). As age and lesion size increased, the probability of SA significantly decreased, with a cut-off value of 36 years old and 10 mm, respectively. In the logistic regression analysis of the training set, age, nodule size, menopausal status, clinical symptoms, palpability of lesions, margins, internal echo, color Doppler flow imaging (CDFI) grading, and resistance index (RI) were statistically significant (P<0.05). These indicators were included in the static and dynamic nomogram model, which showed high predictive performance, calibration and clinical value in both the training and validation sets.ConclusionSA should be suspected in asymptomatic young women, especially those younger than 36 years of age, who present with small-size lesions (especially less than 10 mm) with distinct margins, homogeneous internal echo, and lack of blood supply. The nomogram model can provide a more convenient tool for clinicians.

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Section: Discussionmentioning

confidence: 99%

A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram

Li,

Wei,

Pang

et al. 2023

Front. Oncol.

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“…We trained deep learning classification models based on an Xception architecture, using ImageNet pretrained weights and two hidden layers of width 1024, with dropout ( p = 0.1) after each hidden layer. Xception was chosen out of prior experience due to its fast convergence and high performance for histopathological applications 43 , 51 – 54 . Models were trained with Slideflow using the Tensorflow backend with a single set of hyperparameters and category-level mini-batch balancing (Supplementary Table 3 ).…”

Section: Methodsmentioning

confidence: 99%

Deep learning generates synthetic cancer histology for explainability and education

et al. 2023

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Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

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“…AI algorithms could not only detect and diagnose cancer cells but also predict the clinical outcome of patients based on the histopathology of various types of cancers [ 63 64 65 66 ]. Huang et al [ 30 ] recently developed a DL algorithm for the prognostic prediction of GC using 2,333 H&E images from real-world datasets and The Cancer Genome Atlas (TCGA) program data.…”

Section: Prediction Of the Clinical Outcome And Biomarker Detectionmentioning

confidence: 99%

Artificial Intelligence in the Pathology of Gastric Cancer

Choi

Kim

2023

J Gastric Cancer

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Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.

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Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence

Cited by 23 publications

References 38 publications

A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram

A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram

Deep learning generates synthetic cancer histology for explainability and education

Artificial Intelligence in the Pathology of Gastric Cancer

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