Effect of a web-based fertility preservation training program for medical professionals in Japan.

BACKGROUND: Our web-based training program called "Educating Medical Professionals about Reproductive Issues in Cancer Healthcare" aims to help healthcare professionals communicate promptly with patients and survivors who are adolescents and young adults, with information pertinent to reproductive health issues such as the risk of infertility and fertility preservation. METHODS: The study participants were professional healthcare providers, including physicians, nurses, pharmacists, social workers, midwives, psychologists, laboratory technicians, genetic counselors, and dieticians. Pre- and post- and 3-month follow-up tests consisting of 41 questions were administered to measure changes in knowledge and confidence. The participants also received a follow-up survey that covered confidence, communication techniques, and practice habits. A total of 820 healthcare providers participated in this program. RESULTS: The mean total score from the pre-test to the post-test grew significantly (p < 0.01), and participants' self-confidence increased. In addition, there was a change in the behavior of healthcare providers, who began asking about patients' marital status and parity. CONCLUSION: Our web-based fertility preservation training program improved knowledge and self-confidence regarding fertility preservation issues among healthcare providers caring for adolescents and young adult cancer patients and survivors.

Usefulness of Simple Diffusion Kurtosis Imaging for Head and Neck Tumors: An Early Clinical Study.

Diffusion kurtosis (DK) imaging (DKI), a type of restricted diffusion-weighted imaging, has been reported to be useful for tumor diagnoses in clinical studies. We developed a software program to simultaneously create DK images with apparent diffusion coefficient (ADC) maps and conducted an initial clinical study. Multi-shot echo-planar diffusion-weighted images were obtained at b-values of 0, 400, and 800 sec/mm2 for simple DKI, and DK images were created simultaneously with the ADC map. The usefulness of the DK image and ADC map was evaluated using a pixel analysis of all pixels and a median analysis of the pixels of each case. Tumor and normal tissues differed significantly in both pixel and median analyses. In the pixel analysis, the area under the curve was 0.64 for the mean kurtosis (MK) value and 0.77 for the ADC value. In the median analysis, the MK value was 0.74, and the ADC value was 0.75. The MK and ADC values correlated moderately in the pixel analysis and strongly in the median analysis. Our simple DKI system created DK images simultaneously with ADC maps, and the obtained MK and ADC values were useful for differentiating head and neck tumors from normal tissue.

Utility of phrenic nerve conduction studies for identification of patients with neuromuscular diseases requiring invasive mechanical ventilation.

INTRODUCTION/AIMS: Predicting when a patient will require invasive mechanical ventilation (IMV) is a major challenge in routine care for some neuromuscular diseases. In this study, we prospectively investigated whether phrenic nerve conduction studies (PNCS) can predict when IMV will be required in patients with amyotrophic lateral sclerosis (ALS), Guillain-Barré syndrome (GBS), chronic inflammatory demyelinating polyneuropathy (CIDP), and myotonic dystrophy (DM). METHODS: PNCS amplitude (avAMP) and latency (avLAT) were compared between patients who required IMV (IMV group) and those who did not (non-IMV group). PNCS were performed in 62 healthy controls and in patients with four different diseases that may require IMV: ALS (n = 56), GBS (n = 72), CIDP (n = 38), and DM (n = 24). RESULTS: The IMV group consisted of 12 patients with ALS, 14 with GBS, 2 with CIDP, and 4 with DM. avAMP was significantly lower in the IMV group with ALS than in the non-IMV group (P < .05), but no significant difference was observed in avLAT. avAMP was significantly lower and avLAT was significantly longer in the IMV group with GBS than in the non-IMV group (both P < .05). Receiver operating characteristic analysis showed that the avAMP cutoff between the IMV and non-IMV groups was 184.3 µV (area under the curve = 0.921; sensitivity, 84.6%; specificity, 88.2%) for ALS and GBS. DISCUSSION: PNCSs may aid in determining whether a patient with ALS or GBS requires IMV.

Japan Society of Clinical Oncology Clinical Practice Guidelines 2017 for fertility preservation in childhood, adolescent, and young adult cancer patients: part 2.

The Japan Society of Clinical Oncology (JSCO) published the "JSCO Clinical Practice Guidelines 2017 for Fertility Preservation in Childhood, Adolescent, and Young Adult Cancer Patients" in 2017. This was the first guideline in cancer reproductive medicine in Japan. In the field of cancer reproductive medicine, close cooperation between an oncologist and a physician for reproductive medicine is important from before treatment initiation until long after treatment. The guideline takes into consideration disease specificity and provides opinions from the perspective of oncologists and specialists in reproductive medicine that are in line with the current state of the Japanese medical system. It is intended to serve as a reference for medical staff in both fields regarding the availability of fertility preservation therapy before the start of cancer treatment. Appropriate use of this guideline makes it easier to determine whether fertility preservation therapy is feasible and, ultimately, to improve survivorship in childhood, adolescent, and young adult cancer patients. In this article (Part 2), we describe details by organ/system and also for pediatric cancer.

Japan Society of Clinical Oncology Clinical Practice Guidelines 2017 for fertility preservation in childhood, adolescent, and young adult cancer patients: part 1.

In 2017, the Japan Society of Clinical Oncology (JSCO) published the JSCO Clinical Practice Guidelines 2017 for Fertility Preservation in Childhood, Adolescent, and Young Adult Cancer Patients. These were the first Japanese guidelines to address issues of oncofertility. In this field of medicine, sustained close cooperation between oncologists and reproductive specialists is essential from the diagnosis of cancer until many years after completion of cancer treatment. These JSCO guidelines were intended to guide multidisciplinary medical staff in considering the availability of fertility preservation options and to help them decide whether to provide fertility preservation to childhood, adolescent, and young adult cancer patients before treatment starts, with the ultimate goal of improving patient survivorship. The guidelines are presented as Parts 1 and 2. This article (Part 1) summarizes the goals of the guidelines and the methods used to develop them and provides an overview of fertility preservation across all oncology areas. It includes general remarks on the basic concepts surrounding fertility preservation and explanations of the impacts of cancer treatment on gonadal function by sex and treatment modality and of the options for protecting/preserving gonadal function and makes recommendations based on 4 clinical questions. Part 2 of these guidelines provides specific recommendations on fertility preservation in 8 types of cancer (gynecologic, breast, urologic, pediatric, hematologic, bone and soft tissue, brain, and digestive).

Development and Evaluation of a Short-time Imaging Method for the Clinical Study of the Apparent Diffusion Coefficient Subtraction Method.

The apparent diffusion coefficient subtraction method (ASM) was developed as a new restricted diffusionweighted imaging technique for magnetic resonance imaging (MRI). The usefulness of the ASM has been established by in vitro basic research using a bio-phantom, and clinical research on the application of the ASM for the human body is needed. Herein, we developed a short-time sequence for ASM imaging of the heads of healthy volunteers (n=2), and we investigated the similarity between the obtained ASM images and diffusion kurtosis (DK) images to determine the utility of the ASM for clinical uses. This study appears to be the first to report ASM images of the human head. We observed that the short-time sequence for the ASM imaging of the head can be scanned in approx. 3 min at 1.5T MRI. The noise reduction effect of median filter processing was confirmed on the ASM images scanned by this sequence. The obtained ASM images showed a weak correlation with the DK images, indicating that the ASM images are restricted diffusion-weighted images. The new shorttime imaging sequence could thus be used in clinical studies applying the ASM.

Evaluation of Fast Diffusion Kurtosis Imaging Using New Software Designed for Widespread Clinical Use.

Clinical research using restricted diffusion-weighted imaging, especially diffusion kurtosis (DK) imaging, has been progressing, with reports on its effectiveness in the diagnostic imaging of cerebral infarctions, neurodegenerative diseases, and tumors, among others. However, the application of DK imaging in daily clinical practice has not spread because of the long imaging time required and the use of specific software for image creation. Herein, with the aim of promoting clinical research using DK imaging at any medical facility, we evaluated fast DK imaging using a new software program. We developed a new macro program that produces DK images using general-purpose, inexpensive software (Microsoft Excel and ImageJ), and we evaluated fast DK imaging using bio-phantoms and a healthy volunteer in clinical trials. The DK images created by the new software with diffusion-weighted images captured with short-time imaging sequences were similar to the original DK images captured with long-time imaging sequences. The DK images using three b-values, which can reduce the imaging time by 43%, were equivalent to the DK images using five b-values. The DK imaging technique developed herein might allow any medical facility to increase its daily clinical use of DK imaging and easily conduct clinical research.

Evaluation of the clinical performance of noninvasive prenatal testing at a Japanese laboratory.

AIM: We aimed to evaluate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of noninvasive prenatal testing (NIPT) in high-risk pregnant women. METHODS: Pregnant women who underwent GeneTech NIPT, the most commonly used NIPT in Japan, between January 2015 and March 2019, at Japan NIPT Consortium medical sites were recruited for this study. The exclusion criteria were as follows: pregnant women with missing survey items, multiple pregnancy/vanishing twins, chromosomal abnormalities in the fetus other than the NIPT target disease, and nonreportable NIPT results. Sensitivity and specificity were calculated from the obtained data, and maternal age-specific PPV and NPV were estimated. RESULTS: Of the 45 504 cases, 44 263 cases fulfilling the study criteria were included. The mean maternal age and gestational weeks at the time of procedure were 38.5 years and 13.1 weeks, respectively. Sensitivities were 99.78% (95% confidence interval [95% CI]: 98.78-99.96), 99.12% (95% CI: 96.83-99.76), and 100% (95% CI: 88.30-100) for trisomies 21, 18, and 13, respectively. Specificities were more than 99.9% for trisomies 21, 18, and 13, respectively. Maternal age-specific PPVs were more than 93%, 77%, and 43% at the age of 35 years for trisomies 21, 18, and 13, respectively. CONCLUSION: The GeneTech NIPT data showed high sensitivity and specificity in the detection of fetal trisomies 21, 18, and 13 in high-risk pregnant women, and maternal age-specific PPVs were obtained. These results could provide more accurate and improved information regarding NIPT for genetic counseling in Japan.

Investigation into the Effect of Breast Volume on Irradiation Dose Distribution in Asian Women with Breast Cancer.

Reports on irradiation dose distribution in breast cancer radiotherapy with sufficient sample size are limited in Asian patients. Elucidating dose distribution in Asian patients is particularly important as their breast volume differs compared to patients in Europe and North America. Here, we examined dose distribution in the irradiation field relative to breast volume for three irradiation methods historically used in our facility. We investigated the influence of breast volume on each irradiation method for Asian women. A total of 573 women with early-stage breast cancer were treated with breast-conserving surgery and adjuvant radiotherapy. Three methods were compared: wedge (W), field-in-field (FIF), and wedge-field-in-field (W-FIF). In patients with small breast volume, FIF decreased low- and high-dose areas within the planning target volume, and increased optimal dose area more than W. In patients with medium and large breast volumes, FIF decreased high-dose area more than W. The absolute values of correlation coefficients of breast volume to low-, optimal-, and high-dose areas and mean dose were significantly lower in FIF than in W. The correlation coefficients of V107% were 0.00 and 0.28 for FIF and W, respectively. FIF is an excellent irradiation method that is less affected by breast volume than W in Asian breast cancer patients.

Evaluation of the Imaging Process for a Novel Subtraction Method Using Apparent Diffusion Coefficient Values.

Diffusion-weighted imaging may be used to obtain the apparent diffusion coefficient (ADC), which aids the diagnosis of cerebral infarction and tumors. An ADC reflects elements of free diffusion. Diffusion kurtosis imaging (DKI) has attracted attention as a restricted diffusion imaging technique. The ADC subtraction method (ASM) was developed to visualize restricted diffusion with high resolution by using two ADC maps taken with different diffusion times. We conducted the present study to provide a bridge between the reported basic ASM research and clinical research. We developed new imaging software for clinical use and evaluated its performance herein. This software performs the imaging process automatically and continuously at the pixel level, using ImageJ software. The new software uses a macro or a plugin which is compatible with various operating systems via a Java Virtual Machine. We tested the new imaging software's performance by using a Jurkat cell bio-phantom, and the statistical evaluation of the performance clarified that the ASM values of 99.98% of the pixels in the bio-phantom and physiological saline were calculated accurately (p<0.001). The new software may serve as a useful tool for future clinical applications and restricted diffusion imaging research.

Morphometric Analysis of the Eye by Magnetic Resonance Imaging in MGST2-Gene-Deficient Mice.

Strabismus, a neuro-ophthalmological condition characterized by misalignment of the eyes, is a common ophthalmic disorder affecting both children and adults. In our previous study, we identified the microsomal glutathione S-transferase 2 (MGST2) gene as one of the potential candidates for comitant strabismus susceptibility in a Japanese population. The MGST2 gene belongs to the membrane-associated protein involved in the generation of pro-inflammatory mediators, and it is also found in the protection against oxidative stress by decreasing the reactivity of oxidized lipids. To look for the roles of the MGST2 gene in the development, eye alignment, and overall morphology of the eye as the possible background of strabismus, MGST2 gene knockout (KO) mice were generated by CRISPR/Cas9-mediated gene editing with guide RNAs targeting the MGST2 exon 2. The ocular morphology of the KO mice was analyzed through high-resolution images obtained by a magnetic resonance imaging (MRI) machine for small animals. The morphometric analyses showed that the height, width, and volume of the eyeballs in MGST2 KO homozygous mice were significantly greater than those of wild-type mice, indicating that the eyes of MGST2 KO homozygous mice were significantly enlarged. There were no significant differences in the axis length and axis angle. These morphological changes may potentially contribute to the development of a subgroup of strabismus.

Long-lasting redundant gnrh1/3 expression in GnRH neurons enabled apparent switching of paralog usage during evolution.

Expressed subtype of paralogous genes in functionally homologous cells sometimes show differences across species, the reasons for which have not been explained. The present study examined hypophysiotropic gonadotropin-releasing hormone (GnRH) neurons in vertebrates to investigate this mechanism. These neurons express either gnrh1 or gnrh3 paralogs, depending on the species, and apparent switching of the expressed paralogs in them occurred at least four times in vertebrate evolution. First, we found redundant expression of gnrh1 and gnrh3 in a single neuron in piranha and hypothesized that it may represent an ancestral GnRH system. Moreover, the gnrh1/gnrh3 enhancer of piranha induced reporter RFP/GFP co-expression in a single hypophysiotropic GnRH neuron in both zebrafish and medaka, whose GnRH neurons only express either gnrh3 or gnrh1. Thus, we propose that redundant expression of gnrh1/3 of relatively recent common ancestors may be the key to apparent switching of the paralog usage among present-day species.

Enhancing Diagnostic Precision: Evaluation of Preprocessing Filters in Simple Diffusion Kurtosis Imaging for Head and Neck Tumors.

Background: Our initial clinical study using simple diffusion kurtosis imaging (SDI), which simultaneously produces a diffusion kurtosis image (DKI) and an apparent diffusion coefficient map, confirmed the usefulness of SDI for tumor diagnosis. However, the obtained DKI had noticeable variability in the mean kurtosis (MK) values, which is inherent to SDI. We aimed to improve this variability in SDI by preprocessing with three different filters (Gaussian [G], median [M], and nonlocal mean) of the diffusion-weighted images used for SDI. Methods: The usefulness of filter parameters for diagnosis was examined in basic and clinical studies involving 13 patients with head and neck tumors. Results: The filter parameters, which did not change the median MK value, but reduced the variability and significantly homogenized the MK values in tumor and normal tissues in both basic and clinical studies, were identified. In the receiver operating characteristic curve analysis for distinguishing tumors from normal tissues using MK values, the area under curve values significantly improved from 0.627 without filters to 0.641 with G (σ = 0.5) and 0.638 with M (radius = 0.5). Conclusions: Thus, image pretreatment with G and M for SDI was shown to be useful for improving tumor diagnosis in clinical practice.

Bayesian statistical modeling to predict observer-specific optimal windowing parameters in magnetic resonance imaging.

Magnetic resonance (MR) images require a process known as windowing for optimizing the display conditions. However, the conventional windowing process often fails to achieve the preferred display conditions for observers due to various factors. This study proposes a novel framework for predicting the preferred windowing parameters for each observer using Bayesian statistical modeling. MR images obtained from 1000 patients were divided into training and test sets at a 7:3 ratio. The image intensity and windowing parameters were standardized using previously reported methods. Bayesian statistical modeling was utilized to predict the windowing parameters preferred by three MR imaging (MRI) operators. The performance of the proposed framework was evaluated by assessing the mean relative error (MRE), mean absolute error (MAE), and Pearson's correlation coefficient (ρ) of the test set. In addition, the naive method, which presumes that the average value of the windowing parameters for each acquisition sequence and body region in the training set is optimal, was also used for comparison. Three MRI operators and three radiologists conducted visual assessments. The mean MRE, MAE, and ρ values for the window level and width (WL/WW) in the proposed framework were 12.6 and 13.9, 42.9 and 85.4, and 0.98 and 0.98, respectively. These results outperformed those obtained using the naive method. The visual assessments revealed no significant differences between the original and predicted display conditions, indicating that the proposed framework accurately predicts individualized windowing parameters with the additional advantages of robustness and ease of use. Thus, the proposed framework can effectively predict the windowing parameters preferred by each observer.

[Examination of Reacquisition Mode in Readout-segmented EPI during the Body Motion].

Readout-segmented echo planar imaging has a reacquisition mode in case of body motion. The phantom was moved and imaged to characterize the reacquisition mode. The counts, angle, and terms of continuous body motion and the terms and counts of intermittent body motion were changed. Then, we investigated the effect on the image quality and the improvement effect of the reacquisition mode. We compared the signal intensity of each pixel in images without body motion and images with and without the reacquisition mode using Spearman's correlation coefficient. The correlation coefficient decreased with increasing counts of body motion. There was no difference by angle. The correlation coefficient was high for body motion immediately after the start of imaging and decreased thereafter. The correlation coefficient was high when the counts of body motion were decreased and even when the terms of body motion increased. In all cases, the correlation coefficient was improved by the reacquisition mode.

Estimating the differences between inter-operator contrast enhancement in cerebral CT angiography.

BACKGROUND: Computed tomography (CT) angiography (CTA) is a non-invasive imaging method used to detect arteries and examine various brain diseases. When CTA is performed for follow-up or postoperative evaluation, reproducibility of vessel delineation is required. A reproducible and stable contrast enhancement can be achieved by manipulating the factors affecting it. Previous studies have investigated several factors that alter the contrast enhancement of arteries. However, no reports establishing the effect of different operators on contrast enhancement exist. PURPOSE: To assess the differences between inter-operator arterial contrast enhancement in cerebral CTA using Bayesian statistical modeling. METHODS: Image data were obtained using a multistage sampling method from the cerebral CTA scans of patients who underwent the process between January 2015 and December 2018. Several Bayesian statistical models were developed, and the objective variable was the mean CT number of the bilateral internal carotid arteries after contrast enhancement. The explanatory variables were sex, age, fractional dose (FD), and the operator's information. The posterior distributions of the parameters were computed via Bayesian inference using the Markov chain Monte Carlo (MCMC) method, with the Hamiltonian Monte Carlo method employed as the algorithm. The posterior predictive distributions were computed using the posterior distributions of the parameters. Finally, the differences between inter-operator arterial contrast enhancement on the CT number in cerebral CTA were estimated. RESULTS: The posterior distributions showed that all parameters representing the difference between operators included zero at the 95% credible intervals (CIs). The maximum mean difference between inter-operator CT number in the posterior predictive distribution was only 12.59 Hounsfield units (HUs). CONCLUSIONS: The Bayesian statistical modeling results suggest that contrast enhancement of cerebral CTA examination between operator-to-operator differences in postcontrast CT number was small compared to those within-operator differences resulting from factors not considered in the model.

Quantitative evaluation of the reduction of distortion and metallic artifacts in magnetic resonance images using the multiacquisition variable­resonance image combination selective sequence.

Magnetic resonance imaging (MRI) is superior to computed tomography (CT) in determining changes in tissue structure, such as those observed following inflammation and infection. However, when metal implants or other metal objects are present, MRI exhibits more distortion and artifacts compared with CT, which hinders the accurate measurement of the implants. A limited number of reports have examined whether the novel MRI sequence, multiacquisition variable-resonance image combination selective (MAVRIC SL), can accurately measure metal implants without distortion. Therefore, the present study aimed to demonstrate whether MAVRIC SL could accurately measure metal implants without distortion and whether the area around the metal implants could be well delineated without artifacts. An agar phantom containing a titanium alloy lumbar implant was used for the present study and was imaged using a 3.0 T MRI machine. A total of three imaging sequences, namely MAVRIC SL, CUBE and magnetic image compilation (MAGiC), were applied and the results were compared. Distortion was evaluated by measuring the screw diameter and distance between the screws multiple times in the phase and frequency directions by two different investigators. The artifact region around the implant was examined using a quantitative method following standardization of the phantom signal values. It was revealed that MAVRIC SL was a superior sequence compared with CUBE and MAGiC, as there was significantly less distortion, a lack of bias between the two different investigators and significantly reduced artifact regions. These results suggested the possibility of utilizing MAVRIC SL for follow-up to observe metal implant insertions.

Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients.

Deep inspiration breath-hold (DIBH) is an excellent technique to reduce the incidental radiation received by the heart during radiotherapy in patients with breast cancer. However, DIBH is costly and time-consuming for patients and radiotherapy staff. In Asian countries, the use of DIBH is restricted due to the limited number of patients with a high mean heart dose (MHD) and the shortage of radiotherapy personnel and equipment compared to that in the USA. This study aimed to develop, evaluate, and compare the performance of ten machine learning algorithms for predicting MHD using a patient's body mass index and single-slice CT parameters to identify patients who may not require DIBH. Machine learning models were built and tested using a dataset containing 207 patients with left-sided breast cancer who were treated with field-in-field radiotherapy with free breathing. The average MHD was 251 cGy. Stratified repeated four-fold cross-validation was used to build models using 165 training data. The models were compared internally using their average performance metrics: F2 score, AUC, recall, accuracy, Cohen's kappa, and Matthews correlation coefficient. The final performance evaluation for each model was further externally analyzed using 42 unseen test data. The performance of each model was evaluated as a binary classifier by setting the cut-off value of MHD ≥ 300 cGy. The deep neural network (DNN) achieved the highest F2 score (78.9%). Most models successfully classified all patients with high MHD as true positive. This study indicates that the ten models, especially the DNN, might have the potential to identify patients who may not require DIBH.

Evaluation of the accuracy of heart dose prediction by machine learning for selecting patients not requiring deep inspiration breath­hold radiotherapy after breast cancer surgery.

Increased heart dose during postoperative radiotherapy (RT) for left-sided breast cancer (BC) can cause cardiac injury, which can decrease patient survival. The deep inspiration breath-hold technique (DIBH) is becoming increasingly common for reducing the mean heart dose (MHD) in patients with left-sided BC. However, treatment planning and DIBH for RT are laborious, time-consuming and costly for patients and RT staff. In addition, the proportion of patients with left BC with low MHD is considerably higher among Asian women, mainly due to their smaller breast volume compared with that in Western countries. The present study aimed to determine the optimal machine learning (ML) model for predicting the MHD after RT to pre-select patients with low MHD who will not require DIBH prior to RT planning. In total, 562 patients with BC who received postoperative RT were randomly divided into the trainval (n=449) and external (n=113) test datasets for ML using Python (version 3.8). Imbalanced data were corrected using synthetic minority oversampling with Gaussian noise. Specifically, right-left, tumor site, chest wall thickness, irradiation method, body mass index and separation were the six explanatory variables used for ML, with four supervised ML algorithms used. Using the optimal value of hyperparameter tuning with root mean squared error (RMSE) as an indicator for the internal test data, the model yielding the best F2 score evaluation was selected for final validation using the external test data. The predictive ability of MHD for true MHD after RT was the highest among all algorithms for the deep neural network, with a RMSE of 77.4, F2 score of 0.80 and area under the curve-receiver operating characteristic of 0.88, for a cut-off value of 300 cGy. The present study suggested that ML can be used to pre-select female Asian patients with low MHD who do not require DIBH for the postoperative RT of BC.