Feasibility of multi-parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary study

Abstract

Background: Response evaluation of neoadjuvant chemotherapy (NACT) in patients with osteosarcoma is significant for the termination of ineffective treatment, the development of postoperative chemotherapy regimens, and the prediction of prognosis. However, histological response and tumour necrosis rate can currently be evaluated only in resected specimens after NACT. A preoperatively accurate, noninvasive, and reproducible method of response assessment to NACT is required. In this study, the value of multi-parametric magnetic resonance imaging (MRI) combined with machine learning for assessment of tumour necrosis after NACT for osteosarcoma was investigated. Methods: Twelve patients with primary osteosarcoma of limbs underwent NACT and received MRI examination before surgery. Postoperative tumour specimens were made corresponding to the transverse image of MRI. One hundred and two tissue samples were obtained and pathologically divided into tumour survival areas (non-cartilaginous and cartilaginous tumour viable areas) and tumour-nonviable areas (non-cartilaginous tumour necrosis areas, post-necrotic tumour collagen areas, and tumour necrotic cystic/haemorrhagic and secondary aneurismal bone cyst areas). The MRI parameters, including standardised apparent diffusion coefficient (ADC) values, signal intensity values of T2-weighted imaging (T2WI) and subtract-enhanced T1-weighted imaging (ST1WI) were used to train machine learning models based on the random forest algorithm. Three classification tasks of distinguishing tumour survival, non-cartilaginous tumour survival, and cartilaginous tumour survival from tumour nonviable were evaluated by five-fold cross-validation.

Effect of Paclitaxel Combined with Doxorubicin Hydrochloride Liposome Injection in the Treatment of Osteosarcoma and MRI Changes before and after Treatment

“…In the second stage (clearance stage), higher WOR values correspond to higher vascular permeability and indicate rapid excretion of contrast agents. SI max , SEE, PPE, and R values reflect the overall vascular density and permeability of the tumors, and higher values indicate richer blood flow [ 11 , 22 , 23 ].…”

An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics

“…However, it provided insights for the design and reporting radiomics studies. Second, our study only included AI research applying the radiomics approach, but overlooked those conducted with only deep learning for segmentation [60][61][62] or modeling [63]. However, the secondary aim of our study is to find out whether CLAIM can better identify disadvantages in radiomics studies than the currently recommended RQS and TRIPOD.…”

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

“…In 2020, a preliminary study conducted by Huang et al. applied ML to predict tumor necrosis rate on multiparametric MRI before and after chemotherapy in patients with osteosarcoma ( 103 ). This study is of great significance since it first explored the potential correlation between contrast-enhanced MRI and postoperative pathological features.…”

“…Early identification of lung metastasis is critical for alleviating prognosis and treatment of patients ( 127 ). Recently, deep learning approaches have been applied to detect and classify lung metastasis and build deep learning-based image reconstruction techniques ( 128 , 129 ) that can significantly reduce the radiation dose of CT ( 103 ). Due to the crucial role of lung metastasis detection for prognosis in malignant bone tumors, more efficient models are expected to be developed.…”