Introduction The quantitative imaging features (radiomics) that can be obtained from the
different modalities of current-generation hybrid imaging can give complementary …

Purpose We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts
(PDX) to first, optimize and identify robust radiomic features to predict response to therapy in …

Purpose Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) is
commonly accepted as the gold standard to assess outcome after NAC in breast cancer …

To explore radiomic features of pharmacokinetic dynamic contrast-enhanced (Pk-DCE) MRI
on the extensive Tofts model to diagnose breast cancer and predict molecular phenotype …

Breast cancer is the most common malignant tumor and the leading cause of cancer‐related
deaths in women worldwide. Effective means of predicting the prognosis of breast cancer …

Previously, we developed an" activity painting" tool for PET image simulation; however, it
could simulate heterogeneous patterns only in the air. We aimed to improve this phantom …

Abstract Machine learning currently represents the cornerstone of modern artificial
intelligence. The algorithms involved have rapidly permeated into medical sciences and …

PET-CT is an important imaging modality which is well established in the recent years. The
role of the molecular imaging in the evaluation of the various pathologies has been …

This chapter provides an overview of the topic of artificial intelligence and machine learning
approaches in the assessment of treatment response and theranostics with a focus on …