Exploring Tumor Biology with Fluorodeoxyglucose-Positron Emission Tomography Imaging in Breast Carcinoma

Development and External Validation of 18F-FDG PET-Based Radiomic Model for Predicting Pathologic Complete Response after Neoadjuvant Chemotherapy in Breast Cancer

The aim of our retrospective study is to develop and externally validate an 18F-FDG PET-derived radiomics model for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 87 breast cancer patients underwent curative surgery after NAC at Soonchunhyang University Seoul Hospital and were randomly assigned to a training cohort and an internal validation cohort. Radiomic features were extracted from pretreatment PET images. A radiomic-score model was generated using the LASSO method. A combination model incorporating significant clinical variables was constructed. These models were externally validated in a separate cohort of 28 patients from Soonchunhyang University Buscheon Hospital. The model performances were assessed using area under the receiver operating characteristic (AUC). Seven radiomic features were selected to calculate the radiomic-score. Among clinical variables, human epidermal growth factor receptor 2 status was an independent predictor of pCR. The radiomic-score model achieved good discriminability, with AUCs of 0.963, 0.731, and 0.729 for the training, internal validation, and external validation cohorts, respectively. The combination model showed improved predictive performance compared to the radiomic-score model alone, with AUCs of 0.993, 0.772, and 0.906 in three cohorts, respectively. The 18F-FDG PET-derived radiomic-based model is useful for predicting pCR after NAC in breast cancer.

Pretreatment metabolic parameters measured by 18F-FDG-PET to predict the outcome of first-line chemotherapy in extensive-stage small-cell lung cancer

OBJECTIVE

Patients' pretreatment metabolic burden, as measured by radiotracer fluorine-18 fluorodeoxyglucose (F-FDG) PET/computed tomography (CT), has been shown to predict treatment outcome in various malignancies. However, its predictive role in extensive-stage small cell lung cancer (SCLC) has not been definitively determined. This retrospective study investigated the viability of using common pretreatment metabolic parameters, obtained through F-FDG-PET/CT, to predict outcomes of first-line chemotherapy in extensive-stage SCLC.

PARTICIPANTS AND METHODS

The study population comprised 154 consecutive patients with extensive-stage SCLC who underwent a pretreatment F-FDG-PET/CT scan and received standard first-line chemotherapy between January 2011 and December 2015.

RESULTS

Ten (6.5%) and 66 (42.9%) patients achieved a complete or a partial response, respectively (considered an objective response); 35 (22.7%) and 43 (27.9%) experienced stable or progressive disease. The metabolic tumor volume (MTV) was a significant factor for predicting an objective response. For predicting disease control (objective response or stable disease), MTV and total lesion glycolysis (TLG) were nonindependent factors.

CONCLUSION

Greater MTV and TLG could indicate a poorer response to first-line chemotherapy for patients with extensive-stage SCLC, but the predictive efficiency was not high enough for routine reliance. For patients who are not suitable to receive first-line chemotherapy, MTV and TLG may help guide clinical decisions.

Emerging clinical applications of PET based molecular imaging in oncology: the promising future potential for evolving personalized cancer care

This review focuses on the potential of advanced applications of functional molecular imaging in assessing tumor biology and cellular characteristics with emphasis on positron emission tomography (PET) applications with both 18-fluorodeoxyglucose (FDG) and non-FDG tracers. The inherent heterogeneity of cancer cells with their varied cellular biology and metabolic and receptor phenotypic expression in each individual patient and also intra-and inter-lesionally in the same individual mandates for transitioning from a generalized “same-size-fits-all” approach to personalized medicine in oncology. The past two decades have witnessed improvement of oncological imaging through CT, MR imaging, PET, subsequent movement through hybrid or fusion imaging with PET/CT and single-photon emission computerized tomography (SPECT-CT), and now toward the evolving PET/MR imaging. These recent developments have proven invaluable in enhancing oncology care and have the potential to help image the tumor biology at the cellular level, followed by providing a tailored treatment. Molecular imaging, integrated diagnostics or Radiomics, biology-driven interventional radiology and theranostics, all hold immense potential to serve as a guide to give “start and stop” treatment for a patient on an individual basis. This will likely have substantial impact on both treatment costs and outcomes. In this review, we bring forth the current trends in molecular imaging with established techniques (PET/CT), with particular emphasis on newer molecules (such as amino acid metabolism and hypoxia imaging, somatostatin receptor based imaging, and hormone receptor imaging) and further potential for FDG. An introductory discussion on the novel hybrid imaging techniques such as PET/MR is also made to understand the futuristic trends.