Search

The total body irradiation of lymphomas and co-irradiation in the treatment of adjacent solid tumors can lead to a reduced ovarian function, premature ovarian insufficiency, and menopause. A small number of studies has assessed the radiation-induced damage of primordial follicles in animal models and humans. Studies are emerging that evaluate radiation-induced damage to the surrounding ovarian tissue including stromal and immune cells. We reviewed basic laboratory work to assess the current state of knowledge and to establish an experimental setting for further studies in animals and humans. The experimental approaches were mostly performed using mouse models. Most studies relied on single doses as high as 1 Gy, which is considered to cause severe damage to the ovary. Changes in the ovarian reserve were related to the primordial follicle count, providing reproducible evidence that radiation with 1 Gy leads to a significant depletion. Radiation with 0.1 Gy mostly did not show an effect on the primordial follicles. Fewer data exist on the effects of radiation on the ovarian microenvironment including theca-interstitial, immune, endothelial, and smooth muscle cells. We concluded that a mouse model would provide the most reliable model to study the effects of low-dose radiation. Furthermore, both immunohistochemistry and fluorescence-activated cell sorting (FACS) analyses were valuable to analyze not only the germ cells but also the ovarian microenvironment.

Objective: To investigate the diagnostic performance of high-resolution single-shot fast spin-echo (SSFSE) imaging with deep learning (DL) reconstruction algorithm on follicle counting and compare it with original SSFSE images and conventional fast spin-echo (FSE) images., Methods: This study included 20 participants (40 ovaries) with clinically confirmed polycystic ovary syndrome (PCOS) who underwent high-resolution ovary MRI, including three-plane T2-weighted FSE sequences and slice-matched T2-weighted SSFSE sequences. A DL reconstruction algorithm was applied to the SSFSE sequences to generate SSFSE-DL images, and the original SSFSE images were also saved. Subjective evaluations such as the blurring artifacts, subjective noise, and clarity of the follicles on the SSFSE-DL, SSFSE, and conventional FSE images were independently conducted by two observers. Intra-class correlation coefficients and Bland-Altman plots were used to present the repeatability and reproducibility of the follicle number per ovary (FNPO) based on the three types of images., Results: SSFSE-DL images showed less blurring artifact, subjective noise, and better clarity of the follicles than SSFSE and FSE ( p < 0.05). For the repeatability of the FNPO, SSFSE-DL showed the highest intra-observer (ICC = 0.930; 95% CI: 0.878-0.962) and inter-observer (ICC = 0.914; 95% CI: 0.843-0.953) agreements. The inter-observer 95% limits of agreement (LOA) for SSFSE-DL, SSFSE, and FSE ranged from -3.7 to 4.5, -4.4 to 7.0, and -7.1 to 7.6, respectively. The intra-observer 95% LOA for SSFSE-DL, SSFSE, and FSE ranged from -3.5 to 4.0, -5.1 to 6.1, and -5.7 to 4.2, respectively. The absolute values of intra-observer and inter-observer differences for SSFSE-DL were significantly lower than those for SSFSE and FSE ( p < 0.05)., Conclusions: Compared with the original SSFSE images and the conventional FSE images, high-resolution SSFSE images with DL reconstruction algorithm can better display follicles, thus improving FNPO assessment.