Retreatment with nedaplatin in patients with recurrent gynecological cancer after the development of hypersensitivity reaction to carboplatin.

AIM: Platinum is a milestone drug against gynecologic malignancies. The purpose of this retrospective study was to investigate the feasibility of replacing carboplatin with nedaplatin in patients who had developed a hypersensitivity reaction to carboplatin. MATERIAL AND METHODS: Fifteen patients with recurrent gynecologic cancer (12 ovarian, 1 fallopian tube, 1 endometrial and 1 cervical cancer) who had experienced a hypersensitivity reaction to carboplatin and a possible clinical indication for continuing treatment with platinum were treated with nedaplatin (80 mg/m(2) )-containing regimen. RESULTS: The total number of nedaplatin cycles given was 137 (range 1-29). Four (27%) patients developed hypersensitivity reactions on the second, second, fourth, and ninth administration, respectively. The severities of all the hypersensitivity reactions were grade 3 or less. The other 11 patients (73%) had no nedaplatin-associated hypersensitivity reactions. The incidence of hypersensitivity reactions in the paclitaxel and nedaplatin group (three of four, 75%) was more frequent than the docetaxel and nedaplatin group (none of seven, P=0.024). The objective response rate in eleven patients with measurable disease was 36% (complete response at 9% and partial response at 27%), and the disease control rate was 73% (stable disease at 36%). CONCLUSION: Nedaplatin-associated hypersensitivity reactions are not rare in patients who developed allergic reactions to carboplatin. Retreatment of carboplatin-allergic patients with nedaplatin cannot be recommended without careful consideration of the potential risks and benefits.

A Case of Nonpuerperal Uterine Inversion Caused by Cervical Cancer.

Uterine inversion is a rare puerperal event in the third stage of labor. Nonpuerperal uterine inversion is even rarer and is mainly caused by uterine fibroids, uterine sarcoma, or endometrial cancer. This is the first report of uterine inversion caused by cervical cancer. A 67-year-old woman presented with a 10 cm pelvic mass. Contrast-enhanced magnetic resonance imaging revealed uterine inversion, which was preoperatively diagnosed to be caused by endometrial cancer and was treated using an extended abdominal hysterectomy. Postoperative histopathological examination revealed that the primary tumor was a squamous cell carcinoma with coexistent high-grade squamous intraepithelial lesions and small-cell neuroendocrine carcinoma. Immunostaining was diffusely positive for p16 and negative for estrogen receptors. The postoperative diagnosis was cervical squamous cell carcinoma. Our observations suggested that cervical carcinoma can cause uterine inversion by invading the corpus.

Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.

Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-based diagnosis in patients with uterine sarcomas. Fifteen sequences of MRI for patients (uterine sarcoma group: n = 63; uterine leiomyoma: n = 200) were used to train the models. Six radiologists (three specialists, three practitioners) interpreted the same images for validation. The most important individual sequences for diagnosis were axial T2-weighted imaging (T2WI), sagittal T2WI, and diffusion-weighted imaging. These sequences also represented the most accurate combination (accuracy: 91.3%), achieving diagnostic ability comparable to that of specialists (accuracy: 88.3%) and superior to that of practitioners (accuracy: 80.1%). Moreover, radiologists' diagnostic accuracy improved when provided with DNN results (specialists: 89.6%; practitioners: 92.3%). Our DNN models are valuable to improve diagnostic accuracy, especially in filling the gap of clinical skills between interpreters. This method can be a universal model for the use of deep learning in the diagnostic imaging of rare tumors.