Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study

Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer

OBJECTIVE

This study aimed to evaluate the predictive value of implementing machine learning models based on ultrasound radiomics and clinicopathological features in the survival analysis of triple-negative breast cancer (TNBC) patients.

METHODS AND MATERIALS

All patients, including retrospective cohort (training cohort, n = 306; internal validation cohort, n = 77) and prospective external validation cohort (n = 82), were diagnosed as locoregional TNBC and underwent pre-intervention sonographic evaluation in this multi-center study. A thorough chart review was conducted for each patient to collect clinicopathological and sonographic features, and ultrasound radiomics features were obtained by PyRadiomics. Deep learning algorithms were utilized to delineate ROIs on ultrasound images. Radiomics analysis pipeline modules were developed for analyzing features. Radiomic scores, clinical scores, and combined nomograms were analyzed to predict 2-year, 3-year, and 5-year overall survival (OS) and disease-free survival (DFS). Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were used to evaluate the prediction performance.

FINDINGS

Both clinical and radiomic scores showed good performance for overall survival and disease-free survival prediction in internal (median AUC of 0.82 and 0.72 respectively) and external validation (median AUC of 0.70 and 0.74 respectively). The combined nomograms had AUCs of 0.80-0.93 and 0.73-0.89 in the internal and external validation, which had best predictive performance in all tasks (p < 0.05), especially for 5-year OS (p < 0.01). For the overall evaluation of six tasks, combined models obtained better performance than clinical and radiomic scores [AUCs of 0.83 (0.73,0.93), 0.81 (0.72,0.93), and 0.70 (0.61,0.85) respectively].

INTERPRETATION

The combined nomograms based on pre-intervention ultrasound radiomics and clinicopathological features demonstrated exemplary performance in survival analysis. The new models may allow us to non-invasively classify TNBC patients with various disease outcome.

Mortality Prediction Modeling for Patients with Breast Cancer Based on Explainable Machine Learning

Background/Objectives: Breast cancer is the most common cancer in women worldwide, requiring strategic efforts to reduce its mortality. This study aimed to develop a predictive classification model for breast cancer mortality using real-world data, including various clinical features. Methods: A total of 11,286 patients with breast cancer from the National Cancer Center were included in this study. The mortality rate of the total sample was approximately 6.2%. Propensity score matching was used to reduce bias. Several machine learning models, including extreme gradient boosting, were applied to 31 clinical features. To enhance model interpretability, we used the SHapley Additive exPlanations method. ML analyses were also performed on the samples, excluding patients who developed other cancers after breast cancer. Results: Among the ML models, the XGB model exhibited the highest discriminatory power, with an area under the curve of 0.8722 and a specificity of 0.9472. Key predictors of the mortality classification model included occurrence in other organs, age at diagnosis, N stage, T stage, curative radiation treatment, and Ki-67(%). Even after excluding patients who developed other cancers after breast cancer, the XGB model remained the best-performing, with an AUC of 0.8518 and a specificity of 0.9766. Additionally, the top predictors from SHAP were similar to the results for the overall sample. Conclusions: Our models provided excellent predictions of breast cancer mortality using real-world data from South Korea. Explainable artificial intelligence, such as SHAP, validated the clinical applicability and interpretability of these models.

Public health implications of computer-aided diagnosis and treatment technologies in breast cancer care

Breast cancer remains a significant public health issue, being a leading cause of cancer-related mortality among women globally. Timely diagnosis and efficient treatment are crucial for enhancing patient outcomes, reducing healthcare burdens and advancing community health. This systematic review, following the PRISMA guidelines, aims to comprehensively synthesize the recent advancements in computer-aided diagnosis and treatment for breast cancer. The study covers the latest developments in image analysis and processing, machine learning and deep learning algorithms, multimodal fusion techniques and radiation therapy planning and simulation. The results of the review suggest that machine learning, augmented and virtual reality and data mining are the three major research hotspots in breast cancer management. Moreover, this paper discusses the challenges and opportunities for future research in this field. The conclusion highlights the importance of computer-aided techniques in the management of breast cancer and summarizes the key findings of the review.

Analyzing breast cancer invasive disease event classification through explainable artificial intelligence

Introduction

Recently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable.

Methods

Thus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis.

Results

Age, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames.

Discussion

Thus, our framework aims at shortening the distance between AI and clinical practice.

The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review

BACKGROUND

The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity.

OBJECTIVE

We aimed to conduct a scoping review of the literature to address the question, "What are the current and potential impacts of AI technologies on health equity in oncology?"

METHODS

Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology.

RESULTS

Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes.

CONCLUSIONS

Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI's ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.

Development and Validation of a New Multiparametric Random Survival Forest Predictive Model for Breast Cancer Recurrence with a Potential Benefit to Individual Outcomes

Purpose

Breast cancer (BC) is a multi-factorial disease. Its individual prognosis varies; thus, individualized patient profiling is instrumental to improving BC management and individual outcomes. An economical, multiparametric, and practical model to predict BC recurrence is needed.

Patients and Methods

We retrospectively investigated the clinical data of BC patients treated at the Third Affiliated Hospital of Sun Yat-sen University and Liuzhou Women and Children’s Medical Center from January 2013 to December 2020. Random forest-recursive feature elimination (run by R caret package) was used to determine the best variable set, and the random survival forest method was used to develop a predictive model for BC recurrence.

Results

The training and validations sets included 623 and 151 patients, respectively. We selected 14 variables, the pathological (TNM) stage, gamma-glutamyl transpeptidase, total cholesterol, Ki-67, lymphocyte count, low-density lipoprotein, age, apolipoprotein B, high-density lipoprotein, globulin, neutrophil count to lymphocyte count ratio, alanine aminotransferase, triglyceride, and albumin to globulin ratio, using random survival forest (RSF)-recursive feature elimination. We developed a recurrence prediction model using RSF. Using area under the receiver operating characteristic curve and Kaplan–Meier survival analyses, the model performance was determined to be accurate. C-indexes were 0.997 and 0.936 for the training and validation sets, respectively.

Conclusion

The model could accurately predict BC recurrence. It aids clinicians in identifying high-risk patients and making treatment decisions for Breast cancer patients in China. This new multiparametric RSF model is instrumental for breast cancer recurrence prediction and potentially improves individual outcomes.

Comparison of dyslipidemia incidence in Chinese early-stage breast cancer patients following different endocrine therapies: A population-based cohort study

BACKGROUND

There is lack of large-scale real-world research evidence showing the impact of endocrine therapy on blood lipids in Chinese breast cancer patients, especially those with premenopausal breast cancer. Based on a large breast cancer cohort at West China Hospital, we aimed to compare the risk of dyslipidemia between premenopausal and postmenopausal women based on the endocrine therapy used.

METHODS

A total of 1,883 early-stage breast cancer (EBC) patients who received endocrine monotherapy [selective estrogen receptor modulator (SERM) and aromatase inhibitor (AI), with or without ovarian function suppression] with normal blood lipid levels at baseline were retrospectively included between October 2008 and April 2017. Dyslipidemia was defined as an abnormality in cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein, and total cholesterol (TC) levels. The risk accumulation function was used to calculate the incidence of dyslipidemia in order to assess the absolute risk, while the multivariate Cox regression model was used to calculate the relative risk of dyslipidemia between the groups.

RESULTS

Patients with EBC were followed up for 60 months to monitor their blood lipid levels. The accumulated 5-year incidence of dyslipidemia in postmenopausal patients was higher than that in premenopausal patients (adjusted HR [95% confidence interval], 1.25 [1.01-1.56], 41.7% vs. 31.2%, p = 0.045). In premenopausal patients, the risk of abnormal TC was significantly higher in the OFS+AI group compared with that in the SERM group (adjusted HR [95% CI], 6.24 [3.19-12.20], p < 0.001, 5-year abnormal rates: 21.5% vs. 2.4%), and that of abnormal LDL-C level also increased (adjusted HR [95% CI], 10.54 [3.86-28.77], p < 0.001, 5-year abnormal rates: 11.1% vs. 0.9%). In postmenopausal patients, the risk of abnormal TC or LDL-C levels showed a similar trend in the AI and SERM groups.

CONCLUSIONS

In addition to postmenopausal patients, dyslipidemia is also common in premenopausal Chinese patients with EBC who received endocrine therapy. Irrespective of menopausal status, AI treatment increases the risk of TC/LDL-C dyslipidemia than SERM treatment.

A Retrospective Analysis of the Effect of Irinotecan-Based Regimens in Patients With Metastatic Breast Cancer Previously Treated With Anthracyclines and Taxanes

Background

At present, patients with metastatic breast cancer (MBC) have few treatment options after receiving anthracyclines and taxanes. Studies have shown that irinotecan has modest systemic activity in some patients previously treated with anthracyclines and taxanes. This study aimed to evaluate the efficacy of irinotecan-based chemotherapy for breast cancer patients in a metastatic setting.

Methods

We retrospectively collected the clinical information and survival data of 51 patients with MBC who received irinotecan at West China Hospital of Sichuan University. The primary endpoints were the progression free survival (PFS) and overall survival (OS), and the secondary endpoint was the objective response rate (ORR). To minimize potential confounding factors, we matched 51 patients who received third-line chemotherapy without irinotecan through propensity score matching (PSM) based on age, hormone receptor (HR), and human epidermal growth factor receptor 2 (HER2), compared their OS and PFS rates to those treated with irinotecan.

Results

From July 2012 to October 2020, 51 patients were treated with an irinotecan-containing regimen. The median number of previous treatment lines was 4, and a median of two previous chemotherapy cycles (ranging from 1–14 cycles) were given in a salvage line setting. The ORR was 15.7%, and the disease control rate (DCR) was 37.3%. For the irinotecan group, the median PFS was 3.2 months (95% CI 2.7–3.7), while the median OS was 33.1 months (95% CI 27.9–38.3). Univariate analysis results suggested that irinotecan could improve PFS in patients with visceral metastasis (P=0.031), which was 0.7 months longer than patients without visceral metastasis (3.5 months vs. 2.8 months). Compared to the patients who received third-line non-irinotecan chemotherapy, the irinotecan group showed a longer trend of PFS without statistical significance (3.2 months vs 2.1 months, P = 0.052). Similarly, the OS of the irinotecan group was longer than the third-line survival without irinotecan, but it was not statistically significant (33.1 months vs 18.0 months, P = 0.072).

Conclusions

For MBC patients who were previously treated with anthracyclines and/or taxanes, an irinotecan-containing regimen achieved moderate objective response and showed a trend of survival benefit, which deserves further study.

Machine Learning Models for the Prediction of Breast Cancer Prognostic: Application and Comparison Based on a Retrospective Cohort Study (Preprint)

Background

Over the recent years, machine learning methods have been increasingly explored in cancer prognosis because of the appearance of improved machine learning algorithms. These algorithms can use censored data for modeling, such as support vector machines for survival analysis and random survival forest (RSF). However, it is still debated whether traditional (Cox proportional hazard regression) or machine learning-based prognostic models have better predictive performance.

Objective

This study aimed to compare the performance of breast cancer prognostic prediction models based on machine learning and Cox regression.

Methods

This retrospective cohort study included all patients diagnosed with breast cancer and subsequently hospitalized in Fudan University Shanghai Cancer Center between January 1, 2008, and December 31, 2016. After all exclusions, a total of 22,176 cases with 21 features were eligible for model development. The data set was randomly split into a training set (15,523 cases, 70%) and a test set (6653 cases, 30%) for developing 4 models and predicting the overall survival of patients diagnosed with breast cancer. The discriminative ability of models was evaluated by the concordance index (C-index), the time-dependent area under the curve, and D-index; the calibration ability of models was evaluated by the Brier score.

Results

The RSF model revealed the best discriminative performance among the 4 models with 3-year, 5-year, and 10-year time-dependent area under the curve of 0.857, 0.838, and 0.781, a D-index of 7.643 (95% CI 6.542, 8.930) and a C-index of 0.827 (95% CI 0.809, 0.845). The statistical difference of the C-index was tested, and the RSF model significantly outperformed the Cox-EN (elastic net) model (C-index 0.816, 95% CI 0.796, 0.836; P=.01), the Cox model (C-index 0.814, 95% CI 0.794, 0.835; P=.003), and the support vector machine model (C-index 0.812, 95% CI 0.793, 0.832; P<.001). The 4 models’ 3-year, 5-year, and 10-year Brier scores were very close, ranging from 0.027 to 0.094 and less than 0.1, which meant all models had good calibration. In the context of feature importance, elastic net and RSF both indicated that TNM staging, neoadjuvant therapy, number of lymph node metastases, age, and tumor diameter were the top 5 important features for predicting the prognosis of breast cancer. A final online tool was developed to predict the overall survival of patients with breast cancer.

Conclusions

The RSF model slightly outperformed the other models on discriminative ability, revealing the potential of the RSF method as an effective approach to building prognostic prediction models in the context of survival analysis.

Research on the Construction and Application of Breast Cancer-Specific Database System Based on Full Data Lifecycle

Relying on the Biomedical Big Data Center of West China Hospital, this paper makes an in-depth research on the construction method and application of breast cancer-specific database system based on full data lifecycle, including the establishment of data standards, data fusion and governance, multi-modal knowledge graph, data security sharing and value application of breast cancer-specific database. The research was developed by establishing the breast cancer master data and metadata standards, then collecting, mapping and governing the structured and unstructured clinical data, and parsing and processing the electronic medical records with NLP natural language processing method or other applicable methods, as well as constructing the breast cancer-specific database system to support the application of data in clinical practices, scientific research, and teaching in hospitals, giving full play to the value of medical big data of the Biomedical Big Data Center of West China Hospital.