食管癌患者肿瘤体积对 T分期及预后的影响分析

目的：探讨食管癌患者大体肿瘤体积（ gross tumor volume,GTV）对T分期及预后的影响。方法收集198例行根治性切除治疗的食管癌患者的临床资料,观察不同GTV分级的病理T分期分布情况、5年生存情况以及局部区域复发率和远处转移率。结果 GTV 分级与T 分期总符合率为72．16％,一致性分析显示两者存在一致性（ Kappa ＝0．402,P＜0．01）。随着GTV分级升高T分期逐渐增加,且相邻T分期GTV分布有重叠现象;随着GTV分级升高食管癌患者整体生存率呈逐渐下降趋势,差异具有统计学意义（χ2＝21．900,P＝0．000）。 GTVⅠ级组和Ⅱ级组1、3、5年生存率及平均生存时间均显著高于GTVⅢ级组,差异具有统计学意义（P＜0．05）。随着GTV分级升高食管癌患者整体局部区域复发率先下降后升高,差异具有统计学意义（χ2＝7．58,P＝0．023）;随着GTV分级升高食管癌患者整体远处转移率呈现逐渐升高趋势,但差异无统计学意义（χ2＝0．579,P＝0．797）。结论随着食管癌患者GTV增大T分期逐渐增加,5年生存率逐渐下降,局部复发率和远处转移率逐渐增加。     

A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications

Background and purpose

The gross tumour volume (GTV) is predictive of clinical outcome and consequently features in many machine-learned models. 4D-planning, however, has prompted substitution of the GTV with the internal gross target volume (iGTV). We present and validate a method to synthesise GTV data from the iGTV, allowing the combination of 3D and 4D planned patient cohorts for modelling.

乳腺疾病近红外扫描图像特征和临床应用

本文通过对630例乳腺疾病红外图像的研究，提出了标准光照条件和红外图像GTV特征概念。介绍了灰度值测定方法。从图像灰度变化规律及形态特征两方面概要介绍了红外图像GTV特征和图像分析四要点。通过对乳腺癌和良性病GTV特征统计学对比，介绍了GTV特征在乳腺红外扫描检查中图像分析、诊断、鉴别诊断的实用价值。     

A contrast-oriented algorithm for FDG-PET-based delineation of tumour volumes for the radiotherapy of lung cancer: derivation from phantom measurements and validation in patient data

Purpose  An easily applicable algorithm for the FDG-PET-based delineation of tumour volumes for the radiotherapy of lung cancer was developed by phantom measurements and validated in patient data. Methods  PET scans were performed (ECAT-ART tomograph) on two cylindrical phantoms (phan1, phan2) containing glass spheres of different volumes (7.4–258 ml) which were filled with identical FDG concentrations. Gradually increasing the activity of the fillable background, signal-to-background ratios from 33:1 to 2.5:1 were realised. The mean standardised uptake value (SUV) of the region-of-interest (ROI) surrounded by a 70% isocontour (mSUV₇₀) was used to represent the FDG accumulation of each sphere (or tumour). Image contrast was defined as: where BG is the mean background − SUV. For the spheres of phan1, the threshold SUVs (TS) best matching the known sphere volumes were determined. A regression function representing the relationship between TS/(mSUV₇₀ − BG) and C was calculated and used for delineation of the spheres in phan2 and the gross tumour volumes (GTVs) of eight primary lung tumours. These GTVs were compared to those defined using CT. Results  The relationship between TS/(mSUV₇₀ − BG) and C is best described by an inverse regression function which can be converted to the linear relationship . Using this algorithm, the volumes delineated in phan2 differed by only −0.4 to +0.7 mm in radius from the true ones, whilst the PET-GTVs differed by only −0.7 to +1.2 mm compared with the values determined by CT. Conclusion  By the contrast-oriented algorithm presented in this study, a PET-based delineation of GTVs for primary tumours of lung cancer patients is feasible.     

18F-FDG PET/CT在子宫颈癌放疗靶区勾画中的价值

目的 探讨18F-FDG PET/CT在子宫颈癌放疗靶区勾画中的价值.方法 收集2015年3月至2016年10月经病理学检查证实为子宫颈鳞癌Ⅲb期患者33例,由3名放疗医师分别基于单纯CT和PET/CT融合图像勾画原发病灶大体肿瘤靶区体积(gross target volume,GTV),比较不同医师所勾画靶区的差异.结果 3名医师在单纯CT和PET/CT融合图像下定义的GTV比较差异均有统计学意义(P＜0.001).不同医师定义GTVCr差异有统计学意义(F=4.28,P＜0.001),但GTVPERT-CT差异无统计学意义(F=0.21,P=0.81).3位医师应用PET/CT图像勾画的肿瘤靶区体积变异减小(7.75 cm3 vs 24.50 cm3).结论 PET/CT融合图像可以提高靶区勾画的准确性.     

Effects of respiration-averaged computed tomography on positron emission tomography/computed tomography quantification and its potential impact on gross tumor volume delineation

PURPOSE: Patient respiratory motion can cause image artifacts in positron emission tomography (PET) from PET/computed tomography (CT) and change the quantification of PET for thoracic patients. In this study, respiration-averaged CT (ACT) was used to remove the artifacts, and the changes in standardized uptake value (SUV) and gross tumor volume (GTV) were quantified. METHODS AND MATERIALS: We incorporated the ACT acquisition in a PET/CT session for 216 lung patients, generating two PET/CT data sets for each patient. The first data set (PET(HCT)/HCT) contained the clinical PET/CT in which PET was attenuation corrected with a helical CT (HCT). The second data set (PET(ACT)/ACT) contained the PET/CT in which PET was corrected with ACT. We quantified the differences between the two datasets in image alignment, maximum SUV (SUV(max)), and GTV contours. RESULTS: Of the patients, 68% demonstrated respiratory artifacts in the PET(HCT), and for all patients the artifact was removed or reduced in the corresponding PET(ACT). The impact of respiration artifact was the worst for lesions less than 50 cm(3) and located below the dome of the diaphragm. For lesions in this group, the mean SUV(max) difference, GTV volume change, shift in GTV centroid location, and concordance index were 21%, 154%, 2.4 mm, and 0.61, respectively. CONCLUSION: This study benchmarked the differences between the PET data with and without artifacts. It is important to pay attention to the potential existence of these artifacts during GTV contouring, as such artifacts may increase the uncertainties in the lesion volume and the centroid location.