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Progress toward targeting cancer cells is a multi-disciplinary endeavor. In addition to the surgical and oncology specialties, radiologists collaborate with mathematicians, computer scientists, and physicists, in a constant effort to incrementally improve upon the current imaging modalities. Recently, radiologists have formed collaborations with molecular biologists and chemists in order to develop molecular agents that target cancer cells via receptor-substrate or specific physiochemical interactions. In this review, we summarize selected efforts toward molecular targeting of the lymphovascular system. Standard imaging modalities, positron emission tomography, single photon emission tomography, and ultrasound, are reviewed as well as, the targeted introduction of substances for endolymphatic therapy. We also review the current status of sentinel lymph node mapping with radiocolloids and the application of molecular targeting for the development of a radiopharmaceutical specifically designed for sentinel lymph node mapping. [ABSTRACT FROM AUTHOR]

In this study, we investigate the use of imaging feature-based outcomes research (‘radiomics’) combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III–IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5 fold cross validation with the area under the receiver operating characteristic curve (AUC)  =  0.65 (p  =  0.004), 0.73 (p  =  0.026), and 0.66 (p  =  0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC  =  0.68 (p  =  0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5 fold cross validation with AUC  =  0.60 (p  =  0.092) and 0.65 (p  =  0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5 fold cross validation in a relatively uniform single-institution cohort. The LF model also retained significance in an independent population. [ABSTRACT FROM AUTHOR]