Impact of time to local recurrence on the occurrence of metastasis in breast cancer patients treated with neoadjuvant chemotherapy: A random forest survival approach

Laas, Enora; Hamy, Anne-Sophie; Michel, Anne-Sophie; Panchbhaya, Nabilah; Faron, Matthieu; Lam, Thanh; Carrez, S.; Pierga, Jean‐Yves; Rouzier, Roman; Lerebours, Florence; Féron, Jean-Guillaume; Reyal, Fabien

doi:10.1371/journal.pone.0208807

Cited by 11 publications

(10 citation statements)

References 35 publications

(46 reference statements)

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“…The role of TLR on survival of a variety of cancers, including breast cancer, rectal cancer, and gastric cancer [13,[19][20][21], has long been debated, whereas there is no research on this subject in RPS. To the best of our knowledge, this is the first study to investigate the potential impact of TLR on the outcomes of patients with STS of the retroperitoneum.…”

Section: Discussionmentioning

confidence: 99%

Impact of time to first relapse on long-term outcome in adult retroperitoneal sarcoma patients after radical resection

et al. 2022

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Background Local recurrence of primary retroperitoneal sarcoma (RPS) is one of the major causes of treatment failure and death. We attempted to assess the effects of time to local recurrence (TLR) on the survival after recurrence (SAR) and overall survival (OS) of RPS. Methods Included in this study were 224 patients who underwent R0 resection for primary RPS at our institution between January 2000 and December 2020, 118 of whom had local recurrence. Based on the median TLR (19.8 months), patients were divided into two groups: early local recurrence (ELR < 20 months) and late local recurrence (LLR > 20 months). The Kaplan–Meier method was employed to calculate the local recurrence-free survival (LRFS), SAR and OS. Univariate and multivariate analyses were conducted to explore the prognostic value of TLR. Results The median follow-up time was 60.5 months for the entire cohort and 58.5 months for the recurrence cohort. There were 60 (50.8%) patients in the ELR group and 58 (49.2%) in the LLR group. The ELR group exhibited a worse SAR (29.2 months vs. 73.4 months, P < 0.001), OS (41.8 months vs. 120.9 months, P < 0.001), and a lower 5-year OS rate (35.9% vs. 73.2%, P = 0.004) than the LLR group. Furthermore, multivariate analysis indicated that TLR was an independent prognostic indicator for SAR (P = 0.014) and OS (P < 0.001). Conclusions In patients with RPS, ELR after R0 resection presents adverse effects on OS and SAR than those with LLR, and TLR could serve as a promising predictor for OS and SAR.

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Section: Discussionmentioning

confidence: 99%

Impact of time to first relapse on long-term outcome in adult retroperitoneal sarcoma patients after radical resection

et al. 2022

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“…Many computational methods have been developed to predict the survival of breast cancer patients receiving NAC treatment based on the information recorded during the clinical treatment processes. Recently, Lai et al proposed a prognostic nomogram model to predict disease-free survival (DFS) (21), Laas et al used a random survival forest method to evaluate distant metastasis-free survival (DMFS) (22), and Tahmassebic et al compared eight machine learning algorithms for the early prediction of pCR, including the support vector machine (SVM), linear discriminant analysis (LDA), extreme gradient boosting (XGBoost), where the XGBoost algorithms attained the best performance (23). The above methods achieved good prediction accuracy, and demonstrated that the computational approach is a practical contribution to evaluating NAC.…”

Section: Introductionmentioning

confidence: 99%

A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy

et al. 2021

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Neoadjuvant chemotherapy (NAC) may increase the resection rate of breast cancer and shows promising effects on patient prognosis. It has become a necessary treatment choice and is widely used in the clinical setting. Benefitting from the clinical information obtained during NAC treatment, computational methods can improve decision-making by evaluating and predicting treatment responses using a multidisciplinary approach, as there are no uniformly accepted protocols for all institutions for adopting different treatment regiments. In this study, 166 Chinese breast cancer cases were collected from patients who received NAC treatment at the First Bethune Hospital of Jilin University. The Miller–Payne grading system was used to evaluate the treatment response. Four machine learning multiple classifiers were constructed to predict the treatment response against the 26 features extracted from the patients’ clinical data, including Random Forest (RF) model, Convolution Neural Network (CNN) model, Support Vector Machine (SVM) model, and Logistic Regression (LR) model, where the RF model achieved the best performance using our data. To allow a more general application, the models were reconstructed using only six selected features, and the RF model achieved the highest performance with 54.26% accuracy. This work can efficiently guide optimal treatment planning for breast cancer patients.

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“…Some large-scale clinical data indicate that systemic adjuvant chemotherapy should generally not be recommended for most patients with early BC following surgery or radiotherapy, since chemotherapy would result in far greater toxicity relative to the survival benefit of the patients [ 4 – 6 ]. However, patients with a low likelihood of survival who do not undergo chemotherapy will quickly relapse, which results in the invasion of adjacent tissues and distant metastasis [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…radiotherapy, since chemotherapy would result in far greater toxicity relative to the survival benefit of the patients [4][5][6]. However, patients with a low likelihood of survival who do not undergo chemotherapy will quickly relapse, which results in the invasion of adjacent tissues and distant metastasis [7].…”

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confidence: 99%

Immune-related gene data-based molecular subtyping related to the prognosis of breast cancer patients

et al. 2020

Breast Cancer

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Background Breast cancer (BC), which is the most common malignant tumor in females, is associated with increasing morbidity and mortality. Effective treatments include surgery, chemotherapy, radiotherapy, endocrinotherapy and molecular-targeted therapy. With the development of molecular biology, immunology and pharmacogenomics, an increasing amount of evidence has shown that the infiltration of immune cells into the tumor microenvironment, coupled with the immune phenotype of tumor cells, will significantly affect tumor development and malignancy. Consequently, immunotherapy has become a promising treatment for BC prevention and as a modality that can influence patient prognosis. Methods In this study, samples collected from The Cancer Genome Atlas (TCGA) and ImmPort databases were analyzed to investigate specific immune-related genes that affect the prognosis of BC patients. In all, 64 immune-related genes related to prognosis were screened, and the 17 most representative genes were finally selected to establish the prognostic prediction model of BC (the RiskScore model) using the Lasso and StepAIC methods. By establishing a training set and a test set, the efficiency, accuracy and stability of the model in predicting and classifying the prognosis of patients were evaluated. Finally, the 17 immune-related genes were functionally annotated, and GO and KEGG signal pathway enrichment analyses were performed. Results We found that these 17 genes were enriched in numerous BC- and immune microenvironment-related pathways. The relationship between the RiskScore and the clinical characteristics of the sample and signaling pathways was also analyzed. Conclusions Our findings indicate that the prognostic prediction model based on the expression profiles of 17 immune-related genes has demonstrated high predictive accuracy and stability in identifying immune features, which can guide clinicians in the diagnosis and prognostic prediction of BC patients with different immunophenotypes.

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Impact of time to local recurrence on the occurrence of metastasis in breast cancer patients treated with neoadjuvant chemotherapy: A random forest survival approach

Cited by 11 publications

References 35 publications

Impact of time to first relapse on long-term outcome in adult retroperitoneal sarcoma patients after radical resection

Impact of time to first relapse on long-term outcome in adult retroperitoneal sarcoma patients after radical resection

A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy

Immune-related gene data-based molecular subtyping related to the prognosis of breast cancer patients

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