Development of a Diagnostic Artificial Intelligence Tool for Lateral Lymph Node Metastasis in Advanced Rectal Cancer.

BACKGROUND: Metastatic lateral lymph node dissection can improve survival in patients with rectal adenocarcinoma, with or without chemoradiotherapy. However, the optimal imaging diagnostic criteria for lateral lymph node metastases remain undetermined. OBJECTIVE: To develop a lateral lymph node metastasis diagnostic artificial intelligence tool using deep learning, for patients with rectal adenocarcinoma who underwent radical surgery and lateral lymph node dissection. DESIGN: Retrospective study. SETTINGS: Multicenter study. PATIENTS: A total of 209 patients with rectal adenocarcinoma, who underwent radical surgery and lateral lymph node dissection at 15 participating hospitals, were enrolled in the study and allocated to training (n = 139), test (n = 17), or validation (n = 53) cohorts. MAIN OUTCOME MEASURES: In the neoadjuvant treatment group, images taken before pretreatment were classified as baseline images and those taken after pretreatment as presurgery images. In the upfront surgery group, presurgery images were classified as both baseline and presurgery images. We constructed 2 types of artificial intelligence, using baseline and presurgery images, by inputting the patches from these images into ResNet-18, and we assessed their diagnostic accuracy. RESULTS: Overall, 124 patients underwent surgery alone, 52 received neoadjuvant chemotherapy, and 33 received chemoradiotherapy. The number of resected lateral lymph nodes in the training, test, and validation cohorts was 2418, 279, and 850, respectively. The metastatic rates were 2.8%, 0.7%, and 3.7%, respectively. In the validation cohort, the precision-recall area under the curve was 0.870 and 0.963 for the baseline and presurgery images, respectively. Although both baseline and presurgery images provided good accuracy for diagnosing lateral lymph node metastases, the accuracy of presurgery images was better than that of baseline images. LIMITATIONS: The number of cases is small. CONCLUSIONS: An artificial intelligence tool is a promising tool for diagnosing lateral lymph node metastasis with high accuracy. DESARROLLO DE UNA HERRAMIENTA DE INTELIGENCIA ARTIFICIAL PARA EL DIAGNSTICO DE METSTASIS EN GANGLIOS LINFTICOS LATERALES EN CNCER DE RECTO AVANZADO: ANTECEDENTES:Disección de nódulos linfáticos laterales metastásicos puede mejorar la supervivencia en pacientes con adenocarcinoma del recto, con o sin quimiorradioterapia. Sin embargo, aún no se han determinado los criterios óptimos de diagnóstico por imágenes de los nódulos linfáticos laterales metastásicos.OBJETIVO:Nuestro objetivo fue desarrollar una herramienta de inteligencia artificial para el diagnóstico de metástasis en nódulos linfáticos laterales mediante el aprendizaje profundo, para pacientes con adenocarcinoma del recto que se sometieron a cirugía radical y disección de nódulos linfáticos laterales.DISEÑO:Estudio retrospectivo.AJUSTES:Estudio multicéntrico.PACIENTES:Un total de 209 pacientes con adenocarcinoma del recto, que se sometieron a cirugía radical y disección de nódulos linfáticos laterales en 15 hospitales participantes, se inscribieron en el estudio y se asignaron a cohortes de entrenamiento (n = 139), prueba (n = 17) o validación (n = 53).PRINCIPALES MEDIDAS DE RESULTADO:En el grupo de tratamiento neoadyuvante, las imágenes tomadas antes del tratamiento se clasificaron como imágenes de referencia y las posteriores al tratamiento, como imágenes previas a la cirugía. En el grupo de cirugía inicial, las imágenes previas a la cirugía se clasificaron como imágenes de referencia y previas a la cirugía. Construimos dos tipos de inteligencia artificial, utilizando imágenes de referencia y previas a la cirugía, ingresando los parches de estas imágenes en ResNet-18. Evaluamos la precisión diagnóstica de los dos tipos de inteligencia artificial.RESULTADOS:En general, 124 pacientes se sometieron a cirugía solamente, 52 recibieron quimioterapia neoadyuvante y 33 recibieron quimiorradioterapia. El número de nódulos linfáticos laterales removidos en los cohortes de entrenamiento, prueba y validación fue de 2,418; 279 y 850, respectivamente. Las tasas metastásicas fueron 2.8%, 0.7%, y 3.7%, respectivamente. En el cohorte de validación, el área de recuperación de precisión bajo la curva fue de 0.870 y 0.963 para las imágenes de referencia y antes de la cirugía, respectivamente. Aunque tanto las imágenes previas a la cirugía como las iniciales proporcionaron una buena precisión para diagnosticar metástasis en los nódulos linfáticos laterales, la precisión de las imágenes previas a la cirugía fue mejor que la de las imágenes iniciales.LIMITACIONES:El número de casos es pequeño.CONCLUSIÓN:La inteligencia artificial es una herramienta prometedora para diagnosticar metástasis en los nódulos linfáticos laterales con alta precisión. (Traducción-Dr. Aurian Garcia Gonzalez ).

Improved Object Detection Artificial Intelligence Using the Revised RetinaNet Model for the Automatic Detection of Ulcerations, Vascular Lesions, and Tumors in Wireless Capsule Endoscopy.

The use of computer-aided detection models to diagnose lesions in images from wireless capsule endoscopy (WCE) is a topical endoscopic diagnostic solution. We revised our artificial intelligence (AI) model, RetinaNet, to better diagnose multiple types of lesions, including erosions and ulcers, vascular lesions, and tumors. RetinaNet was trained using the data of 1234 patients, consisting of images of 6476 erosions and ulcers, 1916 vascular lesions, 7127 tumors, and 14,014,149 normal tissues. The mean area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for each lesion were evaluated using five-fold stratified cross-validation. Each cross-validation set consisted of between 6,647,148 and 7,267,813 images from 217 patients. The mean AUC values were 0.997 for erosions and ulcers, 0.998 for vascular lesions, and 0.998 for tumors. The mean sensitivities were 0.919, 0.878, and 0.876, respectively. The mean specificities were 0.936, 0.969, and 0.937, and the mean accuracies were 0.930, 0.962, and 0.924, respectively. We developed a new version of an AI-based diagnostic model for the multiclass identification of small bowel lesions in WCE images to help endoscopists appropriately diagnose small intestine diseases in daily clinical practice.

Development and multi-institutional validation of an artificial intelligence-based diagnostic system for gastric biopsy.

To overcome the increasing burden on pathologists in diagnosing gastric biopsies, we developed an artificial intelligence-based system for the pathological diagnosis of gastric biopsies (AI-G), which is expected to work well in daily clinical practice in multiple institutes. The multistage semantic segmentation for pathology (MSP) method utilizes the distribution of feature values extracted from patches of whole-slide images (WSI) like pathologists' "low-power view" information of microscopy. The training dataset included WSIs of 4511 gastric biopsy tissues from 984 patients. In tissue-level validation, MSP AI-G showed better accuracy (91.0%) than that of conventional patch-based AI-G (PB AI-G) (89.8%). Importantly, MSP AI-G unanimously achieved higher accuracy rates (0.946 ± 0.023) than PB AI-G (0.861 ± 0.078) in tissue-level analysis, when applied to the cohorts of 10 different institutes (3450 samples of 1772 patients in all institutes, 198-555 samples of 143-206 patients in each institute). MSP AI-G had high diagnostic accuracy and robustness in multi-institutions. When pathologists selectively review specimens in which pathologist's diagnosis and AI prediction are discordant, the requirement of a secondary review process is significantly less compared with reviewing all specimens by another pathologist.

Decomposing normal and abnormal features of medical images for content-based image retrieval of glioma imaging.

In medical imaging, the characteristics purely derived from a disease should reflect the extent to which abnormal findings deviate from the normal features. Indeed, physicians often need corresponding images without abnormal findings of interest or, conversely, images that contain similar abnormal findings regardless of normal anatomical context. This is called comparative diagnostic reading of medical images, which is essential for a correct diagnosis. To support comparative diagnostic reading, content-based image retrieval (CBIR) that can selectively utilize normal and abnormal features in medical images as two separable semantic components will be useful. In this study, we propose a neural network architecture to decompose the semantic components of medical images into two latent codes: normal anatomy code and abnormal anatomy code. The normal anatomy code represents counterfactual normal anatomies that should have existed if the sample is healthy, whereas the abnormal anatomy code attributes to abnormal changes that reflect deviation from the normal baseline. By calculating the similarity based on either normal or abnormal anatomy codes or the combination of the two codes, our algorithm can retrieve images according to the selected semantic component from a dataset consisting of brain magnetic resonance images of gliomas. Moreover, it can utilize a synthetic query vector combining normal and abnormal anatomy codes from two different query images. To evaluate whether the retrieved images are acquired according to the targeted semantic component, the overlap of the ground-truth labels is calculated as metrics of the semantic consistency. Our algorithm provides a flexible CBIR framework by handling the decomposed features with qualitatively and quantitatively remarkable results.

Automatic detection of different types of small-bowel lesions on capsule endoscopy images using a newly developed deep convolutional neural network.

BACKGROUND : Previous computer-aided detection systems for diagnosing lesions in images from wireless capsule endoscopy (WCE) have been limited to a single type of small-bowel lesion. We developed a new artificial intelligence (AI) system able to diagnose multiple types of lesions, including erosions and ulcers, vascular lesions, and tumors. METHODS : We trained the deep neural network system RetinaNet on a data set of 167 patients, which consisted of images of 398 erosions and ulcers, 538 vascular lesions, 4590 tumors, and 34 437 normal tissues. We calculated the mean area under the receiver operating characteristic curve (AUC) for each lesion type using five-fold stratified cross-validation. RESULTS : The mean age of the patients was 63.6 years; 92 were men. The mean AUCs of the AI system were 0.996 (95 %CI 0.992 - 0.999) for erosions and ulcers, 0.950 (95 %CI 0.923 - 0.978) for vascular lesions, and 0.950 (95 %CI 0.913 - 0.988) for tumors. CONCLUSION : We developed and validated a new computer-aided diagnosis system for multiclass diagnosis of small-bowel lesions in WCE images.

Successful pregnancy and delivery achieved following intracytoplasmic sperm injection using teratozoospermic sperm exhibiting marked dysplasia of the fibrous sheath: a case report.

A husband and his wife, both 34 years old, consulted our clinic because of primary infertility. Sperm analysis revealed that the sperm concentration, motility, and progressive motility were (42.8±22.8)×106/mL, 23.3%±12.2%, and 12.9%±6.1%, respectively. Based on Krugar strict morphology criteria, 100% of the sperm were teratozoospermic, with 7.9% DNA fragmentation index. Observation of the sperm under a transmission electron microscope revealed that most parts of the fibrous sheath (FS) surrounding the tails of the sperm were missing from midway through the principal piece to the end piece, although the sperm's heads, necks and midpieces were morphologically normal. To collect oocytes, the gonadotropin-releasing hormone antagonist protocol was carried out, and 7 oocytes were retrieved. Intracytoplasmic sperm injection (ICSI) was performed for all the teratozoospermic sperm. Of the 7 oocytes, 3 were fertilized, and one 8-cell embryo and 2 expanded blastocysts were vitrified. Although repeated transfers of expanded blastocysts resulted in no implantation, one 8-cell embryo transfer in a hormone replacement therapy cycle led to pregnancy. The pregnancy using an 8-cell vitrified embryo resulted in the delivery of a healthy female baby at 38 weeks of gestation. No congenital malformations were found until 28 days after birth. Our results demonstrated that healthy birth could be achieved following the transfer of an embryo derived from ICSI using teratozoospermic sperm exhibiting the dysplasia of the fibrous sheath (DFS). Furthermore, while the previous reports on DFS have not investigated male infertility, we evaluated sperms from various aspects such as Kruger sperm function test, chromatin dispersion test, electron microscopy findings, time-lapse images of the obtained embryos, and concluded that ICSI could be desirable as a treatment policy for DFS.

Random-start ovarian stimulation with aromatase inhibitor for fertility preservation in women with Japanese breast cancer.

PURPOSE: Fertility preservation is an important issue for young cancer patients. Random-start controlled ovarian stimulation and double ovarian stimulation have been proposed for efficient oocyte retrieval within the limited time before cancer therapy. We aimed to clarify the efficacy of these new protocols within the Japanese population. METHODS: We performed a retrospective observational study at a multicenter from February 2012 to August 2017. The study entailed 50 cycles with 34 patients who underwent fertility preservation due to breast cancer. Follicular phase or luteal phase ovarian stimulation with aromatase inhibitor was performed. A second ovarian stimulation was started with or without waiting until the next menstruation. We measured the number of retrieved oocytes and cryopreserved oocytes/embryos, the ratio of mature oocytes, and the fertilization rate. RESULTS: The numbers of retrieved oocytes and frozen oocytes/embryos were not significantly different between follicular phase and luteal phase ovarian stimulation. The number of retrieved oocytes was not reduced at the second ovum pick up compared to the first ovum pick up in the double ovarian stimulation. CONCLUSIONS: Random-start controlled ovarian stimulation and double ovarian stimulation with aromatase inhibitor for breast cancer patients were effective protocols for retrieving a greater number of oocytes within the limited time.

Augmentation of arginase â ¡ expression in the human endometrial epithelium in the secretory phase.

L-arginine is the common substrate for arginase and nitric oxide synthase (NOS). Arginase converts L-arginine to urea and L-ornithine. L-Ornithine is the principal precursor for the production of polyamines and L-proline, which are required for cell proliferation and collagen synthesis. Endothelial NOS is expressed in the human endometrial glandular epithelium, but the expression and physiological roles of arginase in the human endometrium are not clear. The objective of this study was to investigate the expression and distribution patterns of arginases Ⅰ (A-Ⅰ) and Ⅱ (A-Ⅱ) in the human endometrium by using immunohistochemistry, reverse transcription-polymerase chain reaction (RTPCR), and western blotting. A-Ⅰ and A-Ⅱ were detected by immunohistochemistry in human endometrial epithelial cells during the proliferative and secretory phases of the menstrual cycle. RT-PCR showed that A-Ⅰ and A-Ⅱ mRNA were expressed in human endometrial tissue. Western blotting analysis results showed the expression of A-Ⅱ protein. Immunohistochemistry and western blotting results showed that expression levels of A-Ⅱ were significantly higher in the secretory phase than in the proliferative phase. Increased A-Ⅱ levels in the secretory phase may be responsible for endometrial growth by increasing polyamines and proline products.

Testosterone inhibits matrix metalloproteinase-1 production in human endometrial stromal cells in vitro.

Androgen receptor (AR) is reported to be expressed in human uterine endometrium, but not much information is available on the role of androgens in human endometrium. The purpose of this study is to investigate the role of androgens in the regulation of matrix metalloproteinase (MMP)-1, which is one of the important MMPs for menstruation and embryo implantation in human endometrium. Human endometrial stromal cells (HESCs) were obtained from human endometrium by enzymatic dissociation method. Purified HESCs were incubated with 17beta-estradiol (E2), testosterone, or E2 + testosterone. Progestins (natural progesterone or medroxyprogesterone acetate) or vehicle (dimethyl sulfoxide) were also added to the media instead of testosterone. Furthermore, hydroxyflutamide (FLU),a specific AR antagonist, was also supplemented to cultured media. The amounts of MMP-1 in cultured media and in HESC lysates were examined by ELISA measurements and western blotting analysis respectively. The expression of ARmRNA in HESCs RNA was analyzed by RT-PCR. Testosterone significantly inhibited MMP-1 in both cultured media and cell lysates in a dose-dependent manner. Progestins also inhibited MMP-1. Furthermore, FLU completely recovered the decrease of MMP-1 induced by testosterone. ARmRNA was detected in all HESCs RNA. The present study demonstrated that the secretion and production of MMP-1 in HESCs in vitro were inhibited by testosterone through androgen receptors in a manner similar to that seen for progesterone. These findings indicate that androgen may play an important role in morphological and functional changes of human endometrium.

Haem oxygenase augments porcine granulosa cell apoptosis in vitro.

Haem oxygenases produce carbon monoxide, which, like nitric oxide, is a gaseous messenger molecule that is one of several important survival factors in ovarian follicles. However, little is known about the expression and possible functions of these enzymes in granulosa cells. The purpose of this study was to investigate the expression and possible role of haem oxygenases in porcine granulosa cells (PGCs). We obtained frozen sections of porcine ovaries and PGCs from ovarian follicles of various sizes by needle aspiration, and examined the expression of haem oxygenase-1 (HO-1; inducible type) and HO-2 (constitutive type) in PGCs by immunohistochemistry, RT-PCR, western blotting and flow cytometry. Both types of haem oxygenase were identified in PGCs throughout follicular development, but HO-1 was expressed primarily in granulosa cells in atretic follicles. We also investigated the effect of haem oxygenases on apoptosis of granulosa cells (flow cytometry to detect subdiploid DNA fluorescence) and on expression of Fas ligand (quantitative analysis of western blotting and flow cytometry). In tightly bound PGCs, the mean proportion of apoptotic cells treated with 1 microM haemin (a haem oxygenase substrate) was approximately 1.7-fold greater than that in untreated controls, and zinc protoporphyrin IX (ZnPP IX; a haem oxygenase inhibitor) completely inhibited the increase in apoptosis induced by haemin in 24-h culture. Conversely, in weakly associated PGCs, the proportion of apoptotic cells was not altered by haemin. The quantity of Fas ligand protein was increased in a dose-dependent manner in tightly bound PGCs treated with haemin compared with controls, and the haemin-induced increase in Fas ligand protein was inhibited by ZnPP IX. Thus we identified inducible HO-1 and constitutive HO-2 in PGCs throughout follicular development, and we conclude that products of reactions catalysed by haem oxygenases are likely to be important autocrine/paracrine factors that regulate apoptosis in PGCs.

Usefulness of CA19-9 versus CA125 for the diagnosis of endometriosis.

OBJECTIVE: To investigate the clinical value of the serum CA19-9 level in comparison with the serum CA125 level for diagnosing and determining the severity of endometriosis. DESIGN: Retrospective study. SETTING: Department of Comprehensive Reproductive Medicine in a university hospital. PATIENT(S): One hundred one women with endometriosis and 22 without endometriosis participated in this study. INTERVENTION(S): Blood samples were collected before the operation (laparoscopy, oophrectomy, cystectomy, and/or hysterectomy), and tissue samples of ovarian chocolate cysts were collected during the operation. MAIN OUTCOME MEASURE(S): The serum CA19-9 and CA125 levels and the localization of these antigens in ovarian chocolate cysts. RESULT(S): The mean serum CA19-9 levels in patients at all stages of endometriosis were significantly higher than those in patients without endometriosis, and serum CA19-9 levels significantly correlated with the Revised American Fertility Society classification scores. Intense staining of CA19-9 was observed in 15 of the 20 samples of ovarian chocolate cysts. CONCLUSION(S): CA19-9 is a useful marker for determining the severity of endometriosis.