Monitoring early responses to neoadjuvant chemotherapy and the factors affecting neoadjuvant chemotherapy responses in primary osteosarcoma

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.

Evaluation of the neoadjuvant chemotherapy response in osteosarcoma using the MRI DWI-based machine learning radiomics nomogram

“…Diffusion-weighted imaging (DWI) is a straightforward, noninvasive functional imaging technique that has been identified as promising in recent studies. The apparent diffusion coefficient (ADC) value, in particular, has shown significant potential in indicating NAC efficacy in cancers such as osteosarcoma (6)(7)(8)(9). However, the considerable heterogeneity of tumors may compromise the reliability of these findings, not meeting the rigorous standards required by precision medicine.…”