Retyping of triple‐negative breast cancer based on clustering method

Liu, Bo; Li, Xingrui; Wang, Huina; Zhao, Shuangtao; Li, Jianqiang; Qu, Guangzhi; Wang, Fei

doi:10.1111/exsy.12583

Cited by 3 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, Montazeri et al (25) employed spatial cluster analysis to examine the patterns of occurrence of potentially high-risk breast and prostate cancer clusters in southern Iran. The data of patients with triple-negative breast cancer were reclassified by Liu et al using spectral, hierarchical agglomerative, and K-means clustering, respectively (27). This gave the associated research on the prognosis of breast cancer a new direction.…”

Section: Introductionmentioning

confidence: 99%

“…The method that has been widely used in cancer clustering, the cluster analysis, can be used to study the epidemiological characteristics of cancers, the different types of cancer, and the symptom clusters of patients ( 25 – 27 ). In order to make objects within a class as homogeneous as feasible and those between classes as heterogeneous as possible, the data is divided into various classes in accordance with predetermined criteria ( 28 ).…”

Section: Introductionmentioning

confidence: 99%

“…The data of patients with triple-negative breast cancer were reclassified by Liu et al. using spectral, hierarchical agglomerative, and K-means clustering, respectively ( 27 ). This gave the associated research on the prognosis of breast cancer a new direction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prognostic analysis of breast cancer in Xinjiang based on Cox proportional hazards model and two−step cluster method

Zhao²,

Zhang³

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

ObjectiveTo examine the factors that affect the prognosis and survival of breast cancer patients who were diagnosed at the Affiliated Cancer Hospital of Xinjiang Medical University between 2015 and 2021, forecast the overall survival (OS), and assess the clinicopathological traits and risk level of prognosis of patients in various subgroups.MethodFirst, nomogram model was constructed using the Cox proportional hazards models to identify the independent prognostic factors of breast cancer patients. In order to assess the discrimination, calibration, and clinical utility of the model, additional tools such as the receiver operating characteristic (ROC) curve, calibration curve, and clinical decision curve analysis (DCA) were used. Finally, using two-step cluster analysis (TCA), the patients were grouped in accordance with the independent prognostic factors. Kaplan-Meier survival analysis was employed to compare prognostic risk among various subgroups.ResultT-stage, N-stage, M-stage, molecular subtyping, type of operation, and involvement in postoperative chemotherapy were identified as the independent prognostic factors. The nomogram was subsequently constructed and confirmed. The area under the ROC curve used to predict 1-, 3-, 5- and 7-year OS were 0.848, 0.820, 0.813, and 0.791 in the training group and 0.970, 0.898, 0.863, and 0.798 in the validation group, respectively. The calibration curves of both groups were relatively near to the 45° reference line. And the DCA curve further demonstrated that the nomogram has a higher clinical utility. Furthermore, using the TCA, the patients were divided into two subgroups. Additionally, the two groups’ survival curves were substantially different. In particular, in the group with the worse prognosis (the majority of patients did not undergo surgical therapy or postoperative chemotherapy treatment), the T-, N-, and M-stage were more prevalent in the advanced, and the total points were likewise distributed in the high score side.ConclusionFor the survival and prognosis of breast cancer patients in Xinjiang, the nomogram constructed in this paper has a good prediction value, and the clustering results further demonstrated that the selected factors were important. This conclusion can give a scientific basis for tailored treatment and is conducive to the formulation of focused treatment regimens for patients in practical practice.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prognostic analysis of breast cancer in Xinjiang based on Cox proportional hazards model and two−step cluster method

Zhao²,

Zhang³

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…According to the survey of the International Agency for Research on Cancer and the World Health Organization, in 2012, breast cancer killed 8.2 million people with an average of 450,000 deaths worldwide each year. Approximately, 10%–20.8% of cases are due to triple‐negative breast cancer (Liu et al, 2020). In the age group of 30–60, one out of eight women in the USA and four out of 1000 in India (Gaudin, 2013) are diagnosed with breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of malignant tumours in mammogram images: A hybrid approach using convolutional neural networks and connected component analysis

et al. 2021

View full text Add to dashboard Cite

The segmentation of breast lesions is an important step in the computer-aided analysis of the mammogram. The presence of noise in mammograms makes lesion detection challenging particularly for complex malignant lesions. Pre-processing techniques can deal with the noise issue but distorts the important shape features. This motivates us to propose a novel hybrid approach by combining a convolution neural network (CNN) with connected component analysis (CCA) to segment malignant breast lesions without any pre-processing to avoid any distortion in image sharpness at the initial stages. Two well-known segmentation techniques namely, K-means (KM) and Fuzzy c-means (FCM) are also used to compare the results. From a pool of 1045 mammographic cancer images acquired from the Digital Database for Screening Mammography (DDSM), 1016 are used for training and validation, and 29 are used for testing. All three results (Hybrid, KM and FCM) are compared against the results by the expert Radiologist. The results indicate that, among various segmentation techniques, the proposed hybrid approach achieves the highest accuracy (90%), Matthew's correlation coefficient (0.79), Jaccard index (0.73) and the Dice similarity coefficient (0.84). Other performance evaluation techniques such as; precision, sensitivity, specificity, false-positive rate, false discovery rate, negative predictive value and false-negative rate also show the superior performance of the proposed hybrid approach. Statistical analysis (Mann-Whitney U test, T-test, Chi-square test, Kolmogorov-Smirnov test and Wilcoxon test), graphical analysis (Regression and Bland-Altman plots) and receiver operating characteristic curve further demonstrate the stability and consistency of the results.

show abstract

“…*Author for correspondence Data pruning plays a pivotal role in data mining algorithms, greatly facilitating the identification of meaningful patterns. In the pursuit of knowledge exploration, both data mining and knowledge discovery in databases (KDD) hold pivotal roles [6][7][8][9]. The DM approach, which entails the autonomous discovery of recurrent patterns within data, serves as the nucleus of the learning and discovery process.…”

mentioning

confidence: 99%

Enhancing data analysis through k-means with foggy centroid selection

2023

IJACR

View full text Add to dashboard Cite

Clustering algorithms have emerged as indispensable tools across diverse sectors such as engineering, ecommerce, and healthcare in the contemporary era. Among the prominently utilized methods, k-means, fuzzy c-means, and hierarchical clustering have garnered substantial attention [1,2]. The effectiveness of these algorithms hinges on their ability to discern data clusters through inter-centroid calculations [2].

show abstract

Retyping of triple‐negative breast cancer based on clustering method

Cited by 3 publications

References 25 publications

Prognostic analysis of breast cancer in Xinjiang based on Cox proportional hazards model and two−step cluster method

Prognostic analysis of breast cancer in Xinjiang based on Cox proportional hazards model and two−step cluster method

Segmentation of malignant tumours in mammogram images: A hybrid approach using convolutional neural networks and connected component analysis

Enhancing data analysis through k-means with foggy centroid selection

Contact Info

Product

Resources

About