Response Evaluation of Neoadjuvant Chemotherapy in Patients with Epithelial Malignancies of the Paranasal Sinus

Abstract Background To investigate the association between endoscopic response evaluation of neoadjuvant chemotherapy (NAC) with pathological response and survival in patients with esophageal squamous cell carcinoma (ESCC). Methods We retrospectively reviewed the medical records of patients with the aid of a prospectively entered database. One hundred and eleven consecutive patients with ESCC who underwent radical esophagectomy after NAC were included. All patients were divided into two groups according to endoscopic response after NAC: endoscopic non-responders in whom NAC was poorly or moderately effective, and endoscopic responders in whom NAC was highly effective or completely effective. The clinical response after NAC was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST). Results The pretreatment clinical stage was IB in 5 patients (5%), II in 18 (16%), III in 72 (65%), and IV in 16 (14%). All patients received two courses of chemotherapy. Chemotherapy consisted of docetaxel, cisplatin (CDDP), and 5-fluorouracil (5-FU; the DCF regimen) in 82 patients (74%), and 5-FU and CDDP (FP) in 29 (26%). All patients underwent radical esophagectomy with 2- or 3-field lymph node dissection. The postoperative mortality and morbidity rates were 0.9% and 26%, respectively. Pathological stage (ypStage) was 0 in 1 patient (1%), I in 16 (14%), II in 31 (28%), III in 48 (43%), and IV in 15 (13%). Twenty-two patients (20%) were pathological responders, and this group of patients had better overall survival than pathological non-responders (P = 0.02). Pathological response was significantly correlated with tumor depth (cT) (P < 0.01), protruding type of tumor (P = 0.01) before NAC, and clinical response (P < 0.01) and endoscopic response (P < 0.01) after NAC. Of these clinical factors, clinical response and endoscopic response were significantly correlated with prognosis. Conclusion Endoscopic response after NAC can predict the pathological response and prognosis of patients who received NAC followed by surgery. Endoscopic findings are clinically significant to assess the response of NAC in patients with ESCC. Disclosure All authors have declared no conflicts of interest.

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Feasibility of Multi-Parametric Magnetic Resonance Imaging Combined with Machine Learning in the Assessment of Necrosis of Osteosarcoma after Neoadjuvant Chemotherapy: A Preliminary Study

10.21203/rs.3.rs-16681/v2 ◽

2020 ◽

Author(s):

Bingsheng Huang ◽

Jifei Wang ◽

Meili Sun ◽

Xin Chen ◽

Danyang Xu ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Machine Learning ◽

Magnetic Resonance ◽

Neoadjuvant Chemotherapy ◽

Tumour Necrosis ◽

Bone Cyst ◽

Response Evaluation ◽

Resonance Imaging ◽

Tissue Samples ◽

Aneurismal Bone Cyst

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.Results For distinguishing non-cartilaginous tumour survival from tumour nonviable, the classifier constructed with ADC achieved an AUC of 0.93, while the classifier with multi-parametric MRI improved to 0.97 (P=0.0933). For distinguishing tumour survival from tumour nonviable, the classifier with ADC achieved an AUC of 0.83, while the classifier with multi-parametric MRI improved to 0.90 (P<0.05). For distinguishing cartilaginous tumour survival from tumour nonviable, the classifier with ADC achieved an AUC of 0.61, while the classifier with multi-parametric MRI parameters improved to 0.81(P<0.05).ConclusionsThe combination of multi-parametric MRI and machine learning significantly improved the discriminating ability of viable cartilaginous tumour components. Our study suggests that this method may provide an objective and accurate basis for NACT response evaluation in osteosarcoma.

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