Predictors of Newborn's Weight for Height: A Machine Learning Study Using Nationwide Multicenter Ultrasound Data.

There has been no machine learning study with a rich collection of clinical, sonographic markers to compare the performance measures for a variety of newborns' weight-for-height indicators. This study compared the performance measures for a variety of newborns' weight-for-height indicators based on machine learning, ultrasonographic data and maternal/delivery information. The source of data for this study was a multi-center retrospective study with 2949 mother-newborn pairs. The mean-squared-error-over-variance measures of five machine learning approaches were compared for newborn's weight, newborn's weight/height, newborn's weight/height2 and newborn's weight/hieght3. Random forest variable importance, the influence of a variable over average node impurity, was used to identify major predictors of these newborns' weight-for-height indicators among ultrasonographic data and maternal/delivery information. Regarding ultrasonographic fetal biometry, newborn's weight, newborn's weight/height and newborn's weight/height2 were better indicators with smaller mean-squared-error-over-variance measures than newborn's weight/height3. Based on random forest variable importance, the top six predictors of newborn's weight were the same as those of newborn's weight/height and those of newborn's weight/height2: gestational age at delivery time, the first estimated fetal weight and abdominal circumference in week 36 or later, maternal weight and body mass index at delivery time, and the first biparietal diameter in week 36 or later. These six predictors also ranked within the top seven for large-for-gestational-age and the top eight for small-for-gestational-age. In conclusion, newborn's weight, newborn's weight/height and newborn's weight/height2 are more suitable for ultrasonographic fetal biometry with smaller mean-squared-error-over-variance measures than newborn's weight/height3. Machine learning with ultrasonographic data would be an effective noninvasive approach for predicting newborn's weight, weight/height and weight/height2.

Radiation induced pemphigoid disease.

Among the possible complications of radiation therapy, acute and chronic side effects on the skin can be induced by percutaneous radiotherapy in the target site. Common skin lesions include radiation dermatitis, which can be treated by topical application of dressing and ointment. Pemphigoid disease, which displays similar clinical features as other skin diseases such as recurrent cancer and herpes zoster, rarely occurs in the site of radiotherapy; therefore, care must be taken during diagnosis for a timely treatment. The present report is a case of pemphigoid disease that had developed in a patient with endometrioid/clear cell carcinoma after radiation therapy, and the time between onset and radiotherapy was more than 6 months.

A retroperitoneal dedifferentiated liposarcoma mimicking an ovarian tumor.

A 74-year-old postmenopausal woman visited our gynecology clinic complaining of a palpable abdominal mass. Physical and radiological evaluation indicated that the mass exhibited features of a left ovarian neoplasm showing heterogeneous enhancement. Surgical resection was performed to confirm this suspicion. During surgery, a mass was observed only in the left ovary with no invasive growth, but adhesions to the surrounding peritoneum were seen. Given the patient's age, large mass size, and accompanying uterine myoma and right ovarian cyst, total abdominal hysterectomy with bilateral salpingo-oophorectomy was performed. The final pathologic diagnosis was dedifferentiated liposarcoma. The liposarcoma was suspected to originate from retroperitoneal adipose tissue rather than the ovary. Radiotherapy was planned if a gross lesion indicating recurrence followed 6 months later. This case required a considerable multi-disciplinary approach for diagnosis and treatment because of its ambiguous clinical and radiological findings.

Development and validation of a protein-based signature for the detection of ovarian cancer.

To overcome the significant mortality associated with ovarian cancer, a highly sensitive and specific screening test is urgently needed. CA-125 testing is used to monitor response to chemotherapy, detect recurrence, and detect late stage ovarian cancer. However, CA-125 testing, alone or in combination with ultrasonography, has not been adequate for early detection of ovarian cancer. This article discusses the authors' recent report of a novel multiplex assay that uses a panel of six serum biomarkers: leptin, prolactin, osteopontin, insulin-like growth factor II (IGF-II), macrophage inhibitory factor (MIF), and CA-125. The combination of these six proteins yielded 95.3% sensitivity and 99.4% specificity. The application of this test in the clinical context and the most appropriate population, which could benefit from the test, are discussed.