Advancement of prognostic models in breast cancer: a narrative review

Background: Triple-negative breast cancer (TNBC) is the subtype of breast cancer with the worst prognosis, and traditional survival analysis methods are biased when predicting mortality. To predict the risk of death in patients with triple-negative breast cancer more precisely, a competing risk model was developed. Methods:The clinicopathological data of the TNBC patients from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The data were assigned into a training set and testing set at a ratio of 7:3 in a randomized pattern. Univariate and multivariate competing risk models were applied to find the independent prognostic factors. A prediction nomogram for cancer-specific mortality (CSM) risk was constructed. The accuracy and discrimination of the nomogram were assessed using receiver operating characteristic (ROC) area under the curve (AUC), concordance index (C-index), and a calibration curve using the training and testing sets, respectively.Results: A total of 28,430 TNBC patients were randomly grouped into the training set (n=19,900) and the testing set (n=8,530). The median time for follow-up was 59 [1-107] months. A total of 7,014 (24.67%) patients died, among whom 4,801 (68.45%) died from breast cancer and 2,213 (31.55%) due to non-breast cancer events. The independent risk factors were primary site of tumor, grade, tumor-node-metastasis (TNM) stage, T stage, approach of surgery, chemotherapy, axillary lymph node metastases, brain metastases, and liver metastases. The prediction nomogram was constructed by using the aforementioned variables. The 1-, 3-, and 5-year AUC of CSM were predicted to be 0.856, 0.81, and 0.782, respectively, in the training set, and 0.856, 0.81, and 0.782 in the testing set, respectively. The C-index of the nomogram was 0.801 and 0.799 in the training and testing sets, respectively. As confirmed by the validation and training calibration curves, the nomogram was consistent with the results. Conclusions:We used competing risk models to identify risk factors for CSM and constructed a CSM risk prediction nomogram for TNBC patients, that may be utilized to predict CSM risk in TNBC patients clinically and assist in the creation of individualised clinical treatment options.

Section: Discussionmentioning

confidence: 99%

Section: Calculation Of Csm At Different Timesmentioning

confidence: 99%

Section: Calculation Of Csm At Different Timesmentioning

confidence: 99%

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Establishment and verification of a nomogram to predict tumor-specific mortality risk in triple-negative breast cancer: a competing risk model based on the SEER cohort study

Li¹,

Shi²,

Wu³

et al. 2022

“…Prognostic model refers to the use of statistical methods to determine the quantitative relationship between the risk factors and the probability of clinical outcomes based on the patient's disease state. Breast cancer prognostic models can help clinicians and healthcare providers make more informed medical decisions on chemotherapy exemption (127). In recent decades, the most popular model is the COX proportional hazards model, which has been extensively studied in the fields of statistical learning (128).…”

Section: Prognosis Predictionmentioning

confidence: 99%

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

Zhu¹,

Liu²,

Li³

2022

Self Cite

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 achievementsin 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.

“…Clinical prediction models can combine multiple factors to achieve the individualized assessment of patient prognosis (9,10). Tan et al reported on the use of a nomogram (stromal atypia, mitoses, overgrowth and surgical margins; AMOS) to predict the RFS of PT patients based on histopathological features (11).…”

Section: Introductionmentioning

confidence: 99%

Predicting the recurrence-free survival of phyllodes tumor of the breast: a nomogram based on clinicopathology features, treatment, and surgical margin

Wei¹,

Dai²,

Guan³

et al. 2023

Background: Grading based on histopathologic indicators cannot accurately assess the prognosis of phyllodes tumor (PT) of the breast. This article aimed to investigate the correlation between PT prognosis and clinicopathological features, treatment, and surgical margin. Methods:The clinicopathological data of patients with pathologically confirmed PT at our institution were retrospectively collected. Univariate and multivariate Cox proportional risk models were employed to test the effects of different variables on the prognosis of PT. A nomogram to predict the 1-, 3-, 5-, and 10-year recurrence-free survival (RFS) of PT was proposed, and its discriminative ability and calibration were tested using the concordance index (C-index), area under the curve (AUC), and calibration plots. All statistical analyses were performed using R.Results: A total of 342 PT patients were included, including 242 benign (70.8%), 75 borderline (21.9%) and 25 malignant (7.3%) cases. The median follow-up period was 64.5 months (range, 3-179 months), 66 PT patients had local recurrence (LR), and four patients had distant metastasis. The 1-, 3-, 5-, and 10-year RFS of the PT patients were 90.8%, 81.8%, 78%, and 76.7%, respectively. Age, fibroadenoma (FA) surgery history, treatment, mitotic activity, and surgical margin were selected as the independent factors for PT prognosis. The nomogram showed good discriminative ability and calibration, as indicated by the C-index [0.78, 95% confidence interval (CI): 0.75-0.11].Conclusions: Independent predictors related to PT prognosis were selected to establish a nomogram for predicting the RFS of PT. This nomogram was able to objectively stratify PT patients into prognostic groups and performed well in the internal validation.