Eugenia Mylona

Synthetic data generation has emerged as a promising solution to overcome the challenges which are posed by data scarcity and privacy concerns, as well as, to address the need for training artificial intelligence (AI) algorithms on unbiased data with sufficient sample size and statistical power. Our review explores the application and efficacy of synthetic data methods in healthcare considering the diversity of medical data. To this end, we systematically searched the PubMed and Scopus databases with a great focus on tabular, imaging, radiomics, time-series, and omics data. Studies involving multi-modal synthetic data generation were also explored. The type of method used for the synthetic data generation process was identified in each study and was categorized into statistical, probabilistic, machine learning, and deep learning. Emphasis was given to the programming languages used for the implementation of each method. Our evaluation revealed that the majority of the studies utilize synthetic data generators to: (i) reduce the cost and time required for clinical trials for rare diseases and conditions, (ii) enhance the predictive power of AI models in personalized medicine, (iii) ensure the delivery of fair treatment recommendations across diverse patient populations, and (iv) enable researchers to access high-quality, representative multimodal datasets without exposing sensitive patient information, among others. We underline the wide use of deep learning based synthetic data generators in 72.6 % of the included studies, with 75.3 % of the generators being implemented in Python. A thorough documentation of open-source repositories is finally provided to accelerate research in the field.

AIMS: To investigate the clinicopathological and prognostic significance of membrane type 1 matrix metalloproteinase (MT1-MMP) and MMP-9 proteins expression in invasive breast carcinoma and their relationship to tumour proliferation and expression of c-erbB2 and peroxisome proliferator-activated receptor (PPAR) gamma. METHODS: Immunohistochemistry was carried out on 175 paraffin-embedded breast tissue specimens to detect MT1-MMP, MMP-9, oestrogen receptor (ER), progesterone receptor, c-erbB-2, Ki67, topoisomerase IIalpha (topo IIalpha) and PPARgamma protein expression. RESULTS: Both MT1-MMP and MMP-9 were expressed in the cytoplasm of the malignant cells and the peritumoral stroma. Cytoplasmic MT1-MMP was more often observed in ER+ tumours (P = 0.022), of a lower nuclear grade (P = 0.020) and with reduced expression of Ki67 and topo IIalpha (P = 0.027 and P = 0.006, respectively). Moreover, cytoplasmic MT1-MMP was positively associated with MMP-9 (P = 0.010) and PPARgamma (P < 0.0001). Cytoplasmic MMP-9 was inversely associated with Ki67 (P = 0.034) and topo IIalpha (P = 0.004), whereas its relationship with MT1-MMP (P = 0.034) and PPARgamma (P = 0.024) was found to be positive. Stromal MMP-9 was more often observed in c-erbB2+ tumours (P = 0.043) and had an unfavourable impact on overall and relapse-free survival in both univariate (P = 0.0157 and P = 0.0274, respectively) and multivariate analyses (P = 0.007 and P = 0.024, respectively). CONCLUSIONS: Cytoplasmic MT1-MMP and MMP-9 seem to be related to well-differentiated tumours, with a low proliferation potential, while stromal MMP-9 is associated with an aggressive tumour phenotype and is recognized as an independent poor prognostic indicator.

Purpose Anatomical variations occur during head and neck (H&amp;N) radiotherapy treatment. kV cone‐beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (DLMs) have recently been proposed to generate pseudo‐CT (pCT) from CBCT to perform dose calculation. This study aims to evaluate the accuracy of a DLM and to compare this method with three existing methods of dose calculation from CBCT in H&amp;N cancer radiotherapy. Methods Forty‐four patients received VMAT for H&amp;N cancer (70‐63‐56 Gy). For each patient, reference CT (Bigbore, Philips) and CBCT images (XVI, Elekta) were acquired. The DLM was based on a generative adversarial network. The three compared methods were: (a) a method using a density to Hounsfield Unit (HU) relation from phantom CBCT image (HU‐D curve method), (b) a water–air‐bone density assignment method (DAM), and iii) a method using deformable image registration (DIR). The imaging endpoints were the mean absolute error (MAE) and mean error (ME) of HU from pCT and reference CT (CT ref ). The dosimetric endpoints were dose discrepancies and 3D gamma analyses (local, 2%/2 mm, 30% dose threshold). Dose discrepancies were defined as the mean absolute differences between DVHs calculated from the CT ref and pCT of each method. Results In the entire body, the MAEs and MEs of the DLM, HU‐D curve method, DAM, and DIR method were 82.4 and 17.1 HU, 266.6 and 208.9 HU, 113.2 and 14.2 HU, and 95.5 and −36.6 HU, respectively. The MAE obtained using the DLM differed significantly from those of other methods (Wilcoxon, P ≤ 0.05). The DLM dose discrepancies were 7 ± 8 cGy (maximum = 44 cGy) for the ipsilateral parotid gland D mean and 5 ± 6 cGy (max = 26 cGy) for the contralateral parotid gland mean dose (D mean ). For the parotid gland D mean , no significant dose difference was observed between the DLM and other methods. The mean 3D gamma pass rate ± standard deviation was 98.1 ± 1.2%, 91.0 ± 5.3%, 97.9 ± 1.6%, and 98.8 ± 0.7% for the DLM, HU‐D method, DAM, and DIR method, respectively. The gamma pass rates and mean gamma results of the HU‐D curve method, DAM, and DIR method differed significantly from those of the DLM. Conclusions For H&amp;N radiotherapy, DIR method and DLM appears as the most appealing CBCT‐based dose calculation methods among the four methods in terms of dose accuracy as well as calculation time. Using the DIR method or DLM with CBCT images enables dose monitoring in the parotid glands during the treatment course and may be used to trigger replanning.