Objectives: To determine whether texture analysis for magnetic resonance imaging (MRI) can predict recurrence in patients with breast cancer treated with neoadjuvant chemotherapy (NAC).
Methods: This retrospective study included 130 women who received NAC and underwent subsequent surgery for breast cancer between January 2012 and August 2017. We assessed common features, including standard morphologic MRI features and clinicopathologic features. We used a commercial software and analyzed texture features from pretreatment and midtreatment MRI. A random forest (RF) method was performed to build a model for predicting recurrence. The diagnostic performance of this model for predicting recurrence was assessed and compared with those of five other machine learning classifiers using the Wald test.
Results: Of the 130 women, 21 (16.2%) developed recurrence at a median follow-up of 35.4 months. The RF classifier with common features including clinicopathologic and morphologic MRI features showed the lowest diagnostic performance (area under the receiver operating characteristic curve [AUC], 0.83). The texture analysis with the RF method showed the highest diagnostic performances for pretreatment T2-weighted images and midtreatment DWI and ADC maps showed better diagnostic performance than that of an analysis of common features (AUC, 0.94 vs. 0.83, p < 0.05). The RF model based on all sequences showed a better diagnostic performance for predicting recurrence than did the five other machine learning classifiers.
Conclusions: Texture analysis using an RF model for pretreatment and midtreatment MRI may provide valuable prognostic information for predicting recurrence in patients with breast cancer treated with NAC and surgery.
Key Points: • RF model-based texture analysis showed a superior diagnostic performance than traditional MRI and clinicopathologic features (AUC, 0.94 vs.0.83, p < 0.05) for predicting recurrence in breast cancer after NAC. • Texture analysis using RF classifier showed the highest diagnostic performances (AUC, 0.94) for pretreatment T2-weighted images and midtreatment DWI and ADC maps. • RF model showed a better diagnostic performance for predicting recurrence than did the five other machine learning classifiers.
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http://dx.doi.org/10.1007/s00330-021-07816-x | DOI Listing |
BMC Med Genomics
January 2025
Department of Oncology, The First People's Hospital of Yibin, No.65, Wenxing Street, Cuiping District, Yibin, 644000, China.
Background: Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC.
View Article and Find Full Text PDFWorld J Surg
January 2025
Division of Pathology, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Japan.
Background: Pathological regression grade after chemotherapy evaluated by surgically resected specimens is closely related with prognosis. Since usefulness of measuring the area of the residual tumor (ART) has been reported, this study aimed to evaluate the utility of ART in predicting the prognosis of patients with gastric cancer (GC) who received preoperative chemotherapy.
Methods: This single-center retrospective study examined the relationship between ART and survival outcomes.
Mol Cancer
January 2025
Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, via Campi, 287, Modena, 41125, Italy.
B cells have emerged as central players in the tumor microenvironment (TME) of non-small cell lung cancer (NSCLC). However, although there is clear evidence for their involvement in cancer immunity, scanty data exist on the characterization of B cell phenotypes, bioenergetic profiles and possible interactions with T cells in the context of NSCLC. In this study, using polychromatic flow cytometry, mass cytometry, and spatial transcriptomics we explored the intricate landscape of B cell phenotypes, bioenergetics, and their interaction with T cells in NSCLC.
View Article and Find Full Text PDFBMC Genom Data
January 2025
Department of Management Information Systems, National Chung Hsing University, Taichung, 402, Taiwan.
Background: miRNAs (microRNAs) are endogenous RNAs with lengths of 18 to 24 nucleotides and play critical roles in gene regulation and disease progression. Although traditional wet-lab experiments provide direct evidence for miRNA-disease associations, they are often time-consuming and complicated to analyze by current bioinformatics tools. In recent years, machine learning (ML) and deep learning (DL) techniques are powerful tools to analyze large-scale biological data.
View Article and Find Full Text PDFSci Rep
January 2025
CIBER Cardiovascular, Madrid, Spain.
Soluble ST2 (sST2) is released in response to vascular congestion, inflammation, and pro-fibrotic stimuli. In heart failure (HF), elevated levels of sST2 are associated with a higher risk of adverse clinical outcomes. Emerging evidence suggests that carbohydrate antigen 125 (CA125) may act as a ligand that modulates the inflammatory response.
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