ArticleQuantitative imaging in medicine and surgery2023
Monitoring early responses to neoadjuvant chemotherapy and the factors affecting neoadjuvant chemotherapy responses in primary osteosarcoma.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Radiomics for predicting sensitivity to neoadjuvant chemotherapy in osteosarcoma: current status and advances.Oncology reviews · 2025Pooled it
- Minimal residual disease and relapse surveillance in osteosarcoma: an action-linked framework integrating liquid biopsy and imaging biomarkers.Journal of bone oncology · 2026Review
- Magnetic resonance imaging signal variation for the effective quantitative evaluation of treatment response to neoadjuvant immunotherapy of osteosarcoma: a comparative study with Response Evaluation Criteria in Solid Tumors version 1.1.Quantitative imaging in medicine and surgery · 2026Article
- Multiparametric MRI biomarkers in pediatric osteosarcoma: associations of ADC, necrosis, and tumor volume with histologic and clinical outcomes in a retrospective cohort study.Pediatric radiology · 2026Article
- Non-invasive MRI biomarkers for assessment of neoadjuvant chemotherapy response in osteosarcoma: Current techniques and clinical perspectives.Journal of bone oncology · 2026Review
- Neoadjuvant chemotherapy followed by limb-sparing surgery for osteosarcoma: a retrospective cohort study on efficacy, safety, and prognostic factors.American journal of cancer research · 2026Article
- Single-Cell Transcriptomic Analysis Suggests That JUN May Regulate BAMBI to Promote Osteosarcoma Cell Migration and Invasion.Human mutation · 2026Article
- Exploratory quantitative magnetic resonance imaging characterization of histopathologic subtype heterogeneity in osteosarcoma.Polish journal of radiology · 2026Article
- Prediction of tumor response to neoadjuvant chemotherapy in high-grade osteosarcoma using clustering-based analysis of magnetic resonance imaging: an exploratory study.La Radiologia medica · 2025Article
- Evaluation of the neoadjuvant chemotherapy response in osteosarcoma using the MRI DWI-based machine learning radiomics nomogram.Frontiers in oncology · 2024Article
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6 authors at 3 institutions in 1 country.
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Abstract
Background: This study sought to predict the early responses to neoadjuvant chemotherapy (NACT) of patients with primary conventional osteosarcoma (COS) using the apparent diffusion coefficient (ADC) and to evaluate the factors affecting the tumor necrosis rate (TNR). Methods: The data of 41 patients who underwent magnetic resonance imaging (MRI) and diffusion-weighted imaging sequence scans before NACT, 5 days after the end of the first phase of NACT, after the end of the whole course of chemotherapy, were prospectively collected. ADC1 refers to the ADC before chemotherapy, ADC2 refers to the ADC after the first phase of chemotherapy, and ADC3 refers to the ADC before surgery. The change in values before and after the first phase of chemotherapy was calculated as follows: ADC2-1 = ADC2 - ADC1. The change in values before and after the last phase of chemotherapy was calculated as follows: ADC3-1 = ADC3 - ADC1. The change in values after the first phase and the last phase of chemotherapy was calculated as follows: ADC3-2 = ADC3 - ADC2. We recorded the patient characteristics, including age, gender, pulmonary metastasis, alkaline phosphatase (ALP), and lactate dehydrogenase (LDH). The patients were divided into the following 2 groups based on their histological TNR after postoperative: (I) the good-response group (≥90% necrosis, n=13) and (II) the poor-response group (<90% necrosis, n=28). Changes in the ADCs were compared between the good-response and poor-response groups. The different ADCs between the 2 groups were compared, and a receiver operating characteristic analysis was performed. A correlation analysis was performed to assess the correlations of the clinical features, laboratory features, and different ADCs with patients' histopathological responses to NACT. Results: The ADC2 (P<0.001), ADC3 (P=0.004), ADC3-1 (P=0.008), ADC3-2 (P=0.047), and ALP before NACT (P=0.019) were significantly higher in the good-response group than in the poor-response group. The ADC2 [area under the curve (AUC) =0.723; P=0.023], ADC3 (AUC =0.747; P=0.012), and ADC3-1 (AUC =0.761; P=0.008) showed good diagnostic performance. Based on the univariate binary logistic regression analysis, the ADC2 (P=0.022), ADC3 (P=0.009), ADC2-1 (P=0.041), and ADC3-1 (P=0.014) were correlated with the TNR. However, based on the multivariate analysis, these parameters were not significantly correlated with the TNR. Conclusions: In patients with COS who are undergoing neoadjuvant chemotherapy, the ADC2 is a promisingindicator for predicting tumor response to chemotherapy in early.
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