Electronic doping in organic materials has remained an elusive concept for several decades. It drew considerable attention in the early days in the quest for organic materials with high electrical conductivity, paving the way for the pioneering work on pristine organic semiconductors (OSCs) and their eventual use in a plethora of applications. Despite this early trend, however, recent strides in the field of organic electronics have been made hand in hand with the development and use of dopants to the point that are now ubiquitous. Here, we give an overview of all important advances in the area of doping of organic semiconductors and their applications. We first review the relevant literature with particular focus on the physical processes involved, discussing established mechanisms but also newly proposed theories. We then continue with a comprehensive summary of the most widely studied dopants to date, placing particular emphasis on the chemical strategies toward the synthesis of molecules with improved functionality. The processing routes toward doped organic films and the important doping−processing−nanostructure relationships, are also discussed. We conclude the review by highlighting how doping can enhance the operating characteristics of various organic devices.

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Purpose: Histological subtypes of non-small cell lung cancer (NSCLC) are crucial for systematic treatment decisions. However, the current studies which used noninvasive radiomic methods to classify NSCLC histology subtypes mainly focused on two main subtypes: squamous cell carcinoma (SCC) and adenocarcinoma (ADC), while multi-subtype classifications that included the other two subtypes of NSCLC: large cell carcinoma (LCC) and not otherwise specified (NOS), were very few in the previous studies. The aim of this work was to establish a multi-subtype classification model for the four main subtypes of NSCLC and improve the classification performance and generalization ability compared with previous studies. Methods: In this work, we extracted 1029 features from regions of interest in computed tomography (CT) images of 349 patients from two different datasets using radiomic methods. Based on "threein-one" concept, we proposed a model called SLS wrapping three algorithms, synthetic minority oversampling technique, '2,1-norm minimization, and support vector machines, into one hybrid technique to classify the four main subtypes of NSCLC: SCC, ADC, LCC, and NOS, which could cover the whole range of NSCLC. Results: We analyzed the 247 features obtained by dimension reduction, and found that the extracted features from three methods: first order statistics, gray level co-occurrence matrix, and gray level size zone matrix, were more conducive to the classification of NSCLC subtypes. The proposed SLS model achieved an average classification accuracy of 0.89 on the training set (95% confidence interval [CI]: 0.846 to 0.912) and a classification accuracy of 0.86 on the test set (95% CI: 0.779 to 0.941). Conclusions:The experiment results showed that the subtypes of NSCLC could be well classified by radiomic method. Our SLS model can accurately classify and diagnose the four subtypes of NSCLC based on CT images, and thus it has the potential to be used in the clinical practice to provide valuable information for lung cancer treatment and further promote the personalized medicine.