Multimodal molecular imaging in drug discovery and development

In addition to individual imaging techniques, the combination and integration of several imaging techniques, so-called multimodal imaging, can provide large amounts of anatomical, functional, and molecular information accelerating drug discovery and development processes. Imaging technologies aid in understanding the disease mechanism, finding new pharmacological targets, and assessment of new potential drug candidates and treatment response. Here, we describe how different imaging techniques can be used in different phases of drug discovery and development and highlight their strengths, related innovations, and future potential with a focus on the implementation of artificial intelligence (AI) and radiomics for imaging technologies.     

CT-based radiomics nomogram for preoperative prediction of No.10 lymph nodes metastasis in advanced proximal gastric cancer

IntroductionPreoperative diagnosis of No.10 lymph nodes (LNs) metastases in advanced proximal gastric cancer (APGC) patients remains a challenge. The aim of this study was to develop a CT-based radiomics nomogram for identification of No.10 LNs status in APGCs.Materials and methodsA total of 515 patients with primary APGCs were retrospectively selected and divided into a training cohort (n = 340) and a validation cohort (n = 175). Total incidence of No.10 LNM was 12.4% (64/515). CT based radiomics nomogram combining with radiomic signature calculated from venous CT imaging features and CT-defined No.10 LNs status evaluated by radiologists was built and tested to predict the No.10 LNs status in APGCs.ResultsCT based radiomics nomogram yielded classification accuracy with areas under ROC curves, AUC = 0.896 and 0.814 in training and validation cohort, respectively, while radiomic signature and radiologist’ diagnosis based on contrast-enhanced CT images yielded lower AUCs ranging in 0.742–0.866 and 0.619–0.685, respectively. In the specificity higher than 80%, the sensitivity of using radiomics nomogram, radiomic signature and radiologists’ evaluation to detect No.10 LNs positive cases was 82.8% (53/64), 67.2% (43/64) and 39.1% (25/64), respectively.ConclusionsThe CT-based radiomics nomogram provides a promising and more effective method to yield high accuracy in identification of No.10 LNs metastases in APGC patients.     

基于IVIM‐DWI及影像组学的列线图预测宫颈癌同步放化疗后复发

目的 探讨基于体素内不相干运动弥散加权成像（IVIM‐DWI）及MRI影像组学的列线图模型在预测局部晚期宫颈癌（LACC）同步放化疗（CCRT）后复发中的价值。方法 回顾性分析2014年12月至2019年12月于安徽省肿瘤医院接受CCRT并持续随访的111例ⅠB‐ⅣA期宫颈癌患者的临床资料。测量所有患者疗前原发灶的IVIM‐DWI定量参数（ADC、D、D^*、f值）及疗前、疗后T₂WI序列的3D纹理特征,并采用最小绝对收缩和选择算子（LASSO）算法和logistic回归分析筛选纹理特征,计算影像组学评分Rad‐score。采用Cox比例风险模型分析LACC患者CCRT后复发的独立危险因素并构建列线图。结果 外照射剂量、f值、疗前Rad‐score及疗后Rad‐score是宫颈癌CCRT复发的独立预后因素（HR=0.204、3.253、2.544、7.576）并共同组成列线图模型。模型预测1、3、5年无病生存（DFS）期的曲线下面积分别为0.895、0.888和0.916,内部验证一致性指数分别为0.859、0.903和0.867。决策曲线表明,相较于其他模型,列线图具有更高的临床净收益,临床影响曲线进一步直观地展现了模型的预测精度。结论 基于IVIM‐DWI及影像组学的列线图对预测LACC患者CCRT后复发具有较高的临床价值,可为宫颈癌患者的预后评估及个体化治疗提供参考。     

基于多序列MRI与多体系影像组学模型预测子宫颈癌淋巴结转移的研究

背景与目的：术前寻找可早期用于准确评估淋巴结转移与否的生物标志物极具临床应用价值。探讨MRI影像组学参数预测子宫颈癌淋巴结转移的价值,建立和验证用于术前预测子宫颈癌淋巴结转移的影像组学模型。方法：回顾性分析2015年6月—2019年9月在复旦大学附属肿瘤医院经术后病理学检查证实的子宫颈癌非淋巴结转移患者和子宫颈癌淋巴结转移患者共202例的临床资料,所有患者均经过术前MRI检查。选用MRI图像分别为T2加权图像（T2 weighted image,T2WI）和T1增强图像（T1 contrast +,T1C+）。使用ITK-SNAP软件进行三维手动分割子宫颈癌肿瘤区域。通过开源的python包Pyradiomics和python编程平台jupyter notebook,经过10种图像类型体系和6种特征体系来提取影像组学特征,选取子宫颈癌患者202例,其中未发生淋巴结转移的104例,发生淋巴结转移的98例。T2WI序列和T1C+序列模型分别提取1 923个特征,T2WI联合T1C+序列提取3 846个特征。通过建立影像组学标签,经过机器学习模型验证影像组学标签。最后将训练集和测试集的曲线下面积（area under curve,AUC）、准确率、阳性预测值（positive predictive value,PPV）和阴性预测值（negative predictive value,NPV）作为评估影像组学标签的定量表现。结果：T2WI序列选取特征排序前14名的特征进行分类器训练,训练集AUC=0.810,测试集AUC=0.773。对于T1C+序列选取了特征排序前16名的特征进行分类器训练,训练集AUC=0.819,测试集AUC=0.781。在T2WI联合T1C+序列中选取了特征排序前16名的特征进行分类器训练,训练集AUC=0.841,测试集AUC=0.803。结论：T2WI联合T1C+序列影像组学模型对早期子宫颈癌淋巴结转移有较好的预测能力。     

基于黑质铁沉积的QSM影像组学在帕金森病诊断中的价值研究

目的 应用影像组学纹理分析探讨定量磁敏感图(QSM)在帕金森病(PD)诊断中的价值.方法 58例PD确诊患者及28名健康对照者(HC)均进行QSM检查,将QSM数据进行后处理,然后导入软件并手动勾画感兴趣区(ROI),再对ROI进行纹理特征提取.每例受检者提取1132个纹理特征,降维后筛选出对PD诊断贡献较大的少数特征...     

Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives

Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor in China. Preoperative diagnosis of HCC is challenging because of atypical imaging manifestations and the diversity of focal liver lesions. Artificial intelligence (AI), such as machine learning (ML) and deep learning, has recently gained attention for its capability to reveal quantitative information on images. Currently, AI is used throughout the entire radiomics process and plays a critical role in multiple fields of medicine. This review summarizes the applications of AI in various aspects of preoperative imaging of HCC, including segmentation, differential diagnosis, prediction of histopathology, early detection of recurrence after curative treatment, and evaluation of treatment response. We also review the limitations of previous studies and discuss future directions for diagnostic imaging of HCC.     

肾嫌色细胞癌与不典型透明细胞癌的CT纹理分析研究

目的研究影像组学方法在肾嫌色细胞癌和强化方式不典型的透明细胞癌二者中的应用。方法搜集行肾动脉CTA扫描的108例肾细胞癌患者的临床资料及影像学图像。应用影像组学中的Lasso回归统计方法和机器学习中的随机森林算法提取病例的CTA图像特征并使计算机学习,通过20次重复试验得到平均诊断准确率。患者的临床特征处理采用SPSS 20.0软件,计量资料用t检验,计数资料用χ^2检验,P<0.05为差异具有统计学意义。结果108例肾细胞癌中,透明细胞癌57例,嫌色细胞癌51例。两组病例临床特征中的性别和吸烟史差异具有统计学意义(P<0.05),透明细胞癌更多见于吸烟的男性患者。放射科医师对两组病例诊断的平均准确性为(45.42±3.32)%,低于Lasso回归(76.5±12.26)%和随机森林算法(78.5±6.3)%。在两组病例中,随机森林算法给出的总准确性、对嫌色细胞癌诊断的特异性要高于Lasso回归,Lasso回归对透明细胞癌诊断的敏感性高于随机森林算法。结论影像组学方法可以对肾嫌色细胞癌及透明细胞癌做出有效的鉴别诊断,且诊断能力高于放射科医师的能力。影像组学作为一种新兴的研究方法,有望为医学发展带来重要变革。     

Use of radiomics in the radiation oncology setting: Where do we stand and what do we need?

Radiomics is a field that has been growing rapidly for the past ten years in medical imaging and more particularly in oncology where the primary objective is to contribute to personalised and predictive medicine. This short review aimed at providing some insights regarding the potential value of radiomics for cancer patients treated with radiotherapy. Radiomics may contribute to each stage of the patients’ management: diagnosis, planning, treatment monitoring and post-treatment follow-up (toxicity and response). However, its applicability in clinical routine is currently hindered by several factors, including lack of automation, standardisation and harmonisation. A major effort must be carried out to automate the workflow, standardise radiomics good practices and carry out large-scale studies before any transfer to daily clinical practice.     

Radiomics for classification of bone mineral loss: A machine learning study

PurposeThe purpose of this study was to develop predictive models to classify osteoporosis, osteopenia and normal patients using radiomics and machine learning approaches.Materials and methodsA total of 147 patients were included in this retrospective single-center study. There were 12 men and 135 women with a mean age of 56.88 ± 10.6 (SD) years (range: 28–87 years). For each patient, seven regions including four lumbar and three femoral including trochanteric, intertrochanteric and neck were segmented on bone mineral densitometry images and 54 texture features were extracted from the regions. The performance of four feature selection methods, including classifier attribute evaluation (CLAE), one rule attribute evaluation (ORAE), gain ratio attribute evaluation (GRAE) and principal components analysis (PRCA) along with four classification methods, including random forest (RF), random committee (RC), K-nearest neighbor (KN) and logit-boost (LB) were evaluated. Four classification categories, including osteopenia vs. normal, osteoporosis vs. normal, osteopenia vs. osteoporosis and osteoporosis + osteopenia vs. osteoporosis were examined for the defined seven regions. The classification model performances were evaluated using the area under the receiver operator characteristic curve (AUC).ResultsThe AUC values ranged from 0.50 to 0.78. The combination of methods RF + CLAE, RF + ORAE and RC + ORAE yielded highest performance (AUC = 0.78) in discriminating between osteoporosis and normal state in the trochanteric region. The combinations of RF + PRCA and LB + PRCA had the highest performance (AUC = 0.76) in discriminating between osteoporosis and normal state in the neck region.ConclusionThe machine learning radiomic approach can be considered as a new method for bone mineral deficiency disease classification using bone mineral densitometry image features.