Color Doppler, HDlive, and HDlive Silhouette features of an umbilical cord cyst before 11 weeks of gestation: Diagnostic value

OBJECTIVE

We describe transvaginal color Doppler, HDlive, and HDlive Silhouette features of an umbilical cord cyst (UCC) before 11 weeks of gestation.

METHODS

In this cohort study, 135 transvaginal dating scans were performed at 7 to 10 + 6 weeks of gestation, and 17 UCCs were identified (12.6%). UCC was evaluated using color Doppler, HDlive, and HDlive Silhouette. The clinical characteristics, pregnancy courses, and outcomes were also investigated.

RESULTS

UCC location was on the fetal side in six cases, at the free loop in 10 cases, and on the placental side in one case. There were seven single and 10 multiple cysts. Cyst diameters ranged from 3.3 to 11.3 mm (mean, 5.6; standard deviation, ±2.1). Blood flow inside the cyst was noted in three cases (17.6%). HDlive clearly showed the spatial relationships among UCC, the umbilical cord, midgut herniation, yolk sac, and embryo. Location of UCC could be clearly identified with HDlive. HDlive Silhouette showed central cysts inside UCCs in seven cases (41.2%). HDlive Silhouette also clearly demonstrated the sac of midgut herniation in the umbilical cord in 12 cases (70.6%). All UCCs resolved before 15 weeks (mean, 11.1 weeks; standard deviation, ±1.5). All fetuses with UCCs showed good neonatal outcomes.

CONCLUSION

The incidence of UCC was high compared with that in previous reports. Color Doppler, HDlive, and HDlive Silhouette may provide information on the nature and origin of UCCs before 11 weeks of gestation. UCC before 11 weeks of gestation may be a common, transient, and benign finding.

Fetal brain activity and the free energy principle

OBJECTIVES

To study whether the free energy principle can explain fetal brain activity and the existence of fetal consciousness via a chaotic dimension derived using artificial intelligence.

METHODS

In this observational study, we used a four-dimensional ultrasound technique obtained to collect images of fetal faces from pregnancies at 27-37 weeks of gestation, between February and December 2021. We developed an artificial intelligence classifier that recognizes fetal facial expressions, which are thought to relate to fetal brain activity. We then applied the classifier to video files of facial images to generate each expression category's probabilities. We calculated the chaotic dimensions from the probability lists, and we created and investigated the free energy principle's mathematical model that was assumed to be linked to the chaotic dimension. We used a Mann-Whitney test, linear regression test, and one-way analysis of variance for statistical analysis.

RESULTS

The chaotic dimension revealed that the fetus had dense and sparse states of brain activity, which fluctuated at a statistically significant level. The chaotic dimension and free energy were larger in the sparse state than in the dense state.

CONCLUSIONS

The fluctuating free energy suggests consciousness seemed to exist in the fetus after 27 weeks.

Fetal Cerebellar Growth Curves Based on Biomathematics in Normally Developing Japanese Fetuses and Fetuses with Trisomy 18

We used biomathematics to describe and compare cerebellar growth in normally developing and trisomy 18 Japanese fetuses. This retrospective study included 407 singleton pregnancies with fetuses at 14-39 weeks of gestation and 33 fetuses with trisomy 18 at 17-35 weeks. We used ultrasonography to measure fetal transverse cerebellar diameter (TCD) and anteroposterior cerebellar diameter (APCD). We hypothesized that cerebellar growth is proportional to cerebellar length at any given time point. We determined the formula L(t) ≒Keat+r, where e is Napier's number, t is time, L is cerebellar length, and a, K, and r are constants. We then obtained regression functions for each TCD and APCD in all fetuses. The regression equations for TCD and APCD values in normal fetuses, expressed as exponential functions, were TCD(t)=27.85e0.02788t-28.62 (mm) (adjusted R2=0.997), and APCD(t)=324.29e0.00286t-322.62 (mm) (adjusted R2=0.995). These functions indicated that TCD and APCD grew at constant rates of 2.788%/week and 0.286%/week, respectively, throughout gestation. TCD (0.0153%/week) and APCD (0.000430%/week) grew more slowly in trisomy 18 fetuses. This study demonstrates the potential of biomathematics in clinical research and may aid in biological understanding of fetal cerebellar growth.

Artificial intelligence to understand fluctuation of fetal brain activity by recognizing facial expressions

OBJECTIVE

To examine whether artificial intelligence can achieve discoveries regarding fetal brain activity.

METHODS

In this observational study, the authors collected images of fetal faces using a four-dimensional ultrasound technique obtained from singleton pregnancies of outpatients in routine practice at 27 to 37 weeks of gestation between February 1 and December 31, 2021. The authors developed an artificial intelligence classifier to recognize seven facial expressions of fetuses, then applied it to video files of fetal facial images to generate the probabilities, as confidence scores, of each expression category. Discrete Fourier transform and chaotic analysis were used to investigate the scores. Mann-Whitney test, t test, variance test, and one-way analysis of variance were used for statistical analysis.

RESULTS

Facial expression changes were observed in cycles averaging 66 to 73 s. The power spectrum showed that mouthing and neutral expressions were the most prevalent. There was a difference between categories for the spectrum (p = 0.004). Two different states--dense and sparse--of confidence scores were discovered. The correlation dimension was 1.19 ± 0.22 and 1.33 ± 0.27 for dense and sparse, respectively (p = 0.047).

CONCLUSION

This method objectively and quantitatively demonstrated fetal brain activity and may provide insight into how the fetus spends its time in utero.

HDlive Flow Silhouette with spatiotemporal image correlation for assessment of fetal cardiac structures at 12 to 14 + 6 weeks of gestation

OBJECTIVES

To assess fetal cardiac structures using HDlive Flow Silhouette with spatiotemporal image correlation (STIC) at 12 to 14 + 6 weeks of gestation, and verify the feasibility of obtaining five cardiac views in the late first and early second trimesters of pregnancy. The fetal cardiac shape and the aspect of the descending aorta were also evaluated.

METHODS

Eighty normal fetuses at 12 to 14 + 6 weeks of gestation were studied using trans-abdominal HDlive Flow Silhouette with STIC to assess the feasibility of five fetal cardiac views (frontal, spatial three-vessel, panoramic, posterior, and right lateral views). Target structures in each cardiac view were evaluated. 'Good' was assigned when all structures were noted, 'Fair' when only one structure was missed, and 'Poor' when two and more structures could not be detected. Frequencies of an elongated heart and those of a tortuous descending aorta were counted.

RESULTS

Forty-nine fetuses were effectively included in the analysis. Success rates of 'Good' and 'Fair' were significantly higher with spatial three-vessel (p<0.01) and panoramic views (p<0.05). Frequencies of "Elongated heart", "Elongated left ventricle", and "Spherical heart" were 12.2, 6.2, and 81.6%, respectively. Frequencies of "Tortuous descending aorta" and "Straight descending aorta" were 12.2 and 87.8%, respectively.

CONCLUSIONS

The feasibility of obtaining fetal five cardiac views using HDlive Flow Silhouette with STIC is good, and this technique provides useful information for evaluating fetal cardiac structures in the late first and early second trimesters of pregnancy.

Potential of Artificial Intelligence for Estimating Japanese Fetal Weights

We developed an artificial intelligence (AI) method for estimating fetal weights of Japanese fetuses based on the gestational weeks and the bi-parietal diameter, abdominal circumference, and femur length. The AI comprised of neural network architecture was trained by deep learning with a dataset that consists of ± 2 standard devia-tion (SD), ± 1.5SD, and ± 0SD categories of the approved standard values of ultrasonic measurements of the fetal weights of Japanese fetuses (Japan Society of Ultrasonics in Medicine [JSUM] data). We investigated the residuals and compared 2 other regression formulae for estimating the fetal weights of Japanese fetuses by t-test and Bland-Altman analyses, respectively. The residuals of the AI for the test dataset that was 12.5% of the JSUM data were 6.4 ± 2.6, -3.8 ± 8.6, and -0.32 ± 6.3 (g) at -2SD, +2SD, and all categories, respectively. The residu-als of another AI method created with all of the JSUM data, of which 20% were randomized validation data, were -1.5 ± 9.4, -2.5 ± 7.3, and -1.1 ± 6.7 (g) for -2SD, +2SD, and all categories, respectively. The residuals of this AI were not different from zero, whereas those of the published formulae differed from zero. Though vali-dation is required, the AI demonstrated potential for generating fetal weights accurately, especially for extreme fetal weights.

Recognition of facial expression of fetuses by artificial intelligence (AI)

OBJECTIVES

The development of the artificial intelligence (AI) classifier to recognize fetal facial expressions that are considered as being related to the brain development of fetuses as a retrospective, non-interventional pilot study.

METHODS

Images of fetal faces with sonography obtained from outpatient pregnant women with a singleton fetus were enrolled in routine conventional practice from 19 to 38 weeks of gestation from January 1, 2020, to September 30, 2020, with completely de-identified data. The images were classified into seven categories, such as eye blinking, mouthing, face without any expression, scowling, smiling, tongue expulsion, and yawning. The category in which the number of fetuses was less than 10 was eliminated before preparation. Next, we created a deep learning AI classifier with the data. Statistical values such as accuracy for the test dataset and the AI confidence score profiles for each category per image for all data were obtained.

RESULTS

The number of fetuses/images in the rated categories were 14/147, 23/302, 33/320, 8/55, and 10/72 for eye blinking, mouthing, face without any expression, scowling, and yawning, respectively. The accuracy of the AI fetal facial expression for the entire test data set was 0.985. The accuracy/sensitivity/specificity values were 0.996/0.993/1.000, 0.992/0.986/1.000, 0.985/1.000/0.979, 0.996/0.888/1.000, and 1.000/1.000/1.000 for the eye blinking, mouthing, face without any expression, scowling categories, and yawning, respectively.

CONCLUSIONS

The AI classifier has the potential to objectively classify fetal facial expressions. AI can advance fetal brain development research using ultrasound.

Primary solitary fibrous tumour of the prostate: A case report and literature review

INTRODUCTION

Solitary fibrous tumour (SFT) is a rare mesenchymal tumour with intermediate malignant potential. Although this tumour arises in several sites, prostatic SFT is an extremely rare neoplasm and may prove confusing owing to the lack of clinical experience because of tumour rarity. The diagnosis may be further difficult because SFTs can manifest positive immunoreactivity for CD34 and progesterone receptor, which are known markers of prostatic stromal tumours. Herein, we describe a case of prostatic SFT that was difficult to differentiate from a prostatic stromal tumour of uncertain malignant potential because of positive immunoreactivity to CD34 and progesterone receptor.

CASE REPORT

A 40-year-old Japanese man presented with lower abdominal pain. Computed tomography revealed a prostatic mass; furthermore, prostate core needle biopsy revealed proliferating bland spindle cells, without necrosis or prominent mitoses. Tumour cells were positive for CD34 and progesterone receptor on immunohistochemical analysis; thus, a prostatic stromal tumour of uncertain malignant potential was initially suspected. However, as the tumour cells showed positive immunoreactivity for STAT6, the final diagnosis was an SFT of the prostate. The patient underwent tumour resection, and at the 6-month postoperative follow-up, neither local recurrence nor distant metastasis occurred.

CONCLUSION

For an accurate diagnosis of an SFT of the prostate, STAT6 immunohistochemistry should be conducted for all mesenchymal tumours of the prostate. When STAT6 immunohistochemical analysis is unfeasible, pathologists should be aware that the morphological and immunohistochemical characteristics of SFT variable from case to case and diagnose with combined analysis of several immunohistochemical markers.

Predicting a live birth by artificial intelligence incorporating both the blastocyst image and conventional embryo evaluation parameters

BACKGROUND

The achievement of live birth is the goal of assisted reproductive technology in reproductive medicine. When the selected blastocyst is transferred to the uterus, the degree of implantation of the blastocyst is evaluated by microscopic inspection, and the result is only about 30%-40%, and the method of predicting live birth from the blastocyst image is unknown. Live births correlate with several clinical conventional embryo evaluation parameters (CEE), such as maternal age. Therefore, it is necessary to develop artificial intelligence (AI) that combines blastocyst images and CEE to predict live births.

AIM

To develop an AI classifier for blastocyst images and CEE to predict the probability of achieving a live birth.

METHODS

A total of 5691 images of blastocysts on the fifth day after oocyte retrieval obtained from consecutive patients from January 2009 to April 2017 with fully deidentified data were retrospectively enrolled with explanations to patients and a website containing additional information with an opt-out option. We have developed a system in which the original architecture of the deep learning neural network is used to predict the probability of live birth from a blastocyst image and CEE.

RESULTS

The live birth rate was 0.387 (= 1587/4104 cases). The number of independent clinical information for predicting live birth is 10, which significantly avoids multicollinearity. A single AI classifier is composed of ten layers of convolutional neural networks, and each elementwise layer of ten factors is developed and obtained with 42792 as the number of training data points and 0.001 as the L2 regularization value. The accuracy, sensitivity, specificity, negative predictive value, positive predictive value, Youden J index, and area under the curve values for predicting live birth are 0.743, 0.638, 0.789, 0.831, 0.573, 0.427, and 0.740, respectively. The optimal cut-off point of the receiver operator characteristic curve is 0.207.

CONCLUSION

AI classifiers have the potential of predicting live births that humans cannot predict. Artificial intelligence may make progress in assisted reproductive technology.

Success rate of five fetal cardiac views using HDlive Flow with spatiotemporal image correlation at 18-21 and 28-31 weeks of gestation

Objective To assess the success rates of five fetal cardiac views using HDlive Flow (Silhouette) with spatiotemporal image correlation (STIC) in the second and third trimesters of pregnancy, and to verify the feasibility of obtaining five cardiac views by volumes. Methods One hundred and eighteen normal fetuses at 18-21 and 28-31 weeks of gestation were studied using HDlive Flow (Silhouette) with STIC to assess the success rates of five fetal cardiac views (frontal, spatial three-vessel, panoramic, posterior, and right lateral views). Target structures in each cardiac view were evaluated. "Good" was assigned when all structures were noted, "Fair" when only one structure was missed, and "Poor" when two and more structures could not been detected. Results There were no significant differences in success rates of each cardiac view between 18-21 and 28-31 weeks of gestation. The rate of "Good" with a spatial three-vessel view was significantly higher than that with other cardiac views at 18-21 and 28-31 weeks, respectively (P < 0.05). Conclusion Five cardiac views using HDlive Flow (Silhouette) with STIC may become an adjunctive and useful tool in fetal cardiac examination.

Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types

The aim of the present study was to explore the feasibility of using deep learning, such as artificial intelligence (AI), to classify cervical squamous epithelial lesions (SILs) from colposcopy images combined with human papilloma virus (HPV) types. Among 330 patients who underwent colposcopy and biopsy performed by gynecological oncologists, a total of 253 patients with confirmed HPV typing tests were enrolled in the present study. Of these patients, 210 were diagnosed with high-grade SIL (HSIL) and 43 were diagnosed with low-grade SIL (LSIL). An original AI classifier with a convolutional neural network catenating with an HPV tensor was developed and trained. The accuracy of the AI classifier and gynecological oncologists was 0.941 and 0.843, respectively. The AI classifier performed better compared with the oncologists, although not significantly. The sensitivity, specificity, positive predictive value, negative predictive value, Youden's J index and the area under the receiver-operating characteristic curve ± standard error for AI colposcopy combined with HPV types and pathological results were 0.956 (43/45), 0.833 (5/6), 0.977 (43/44), 0.714 (5/7), 0.789 and 0.963±0.026, respectively. Although further study is required, the clinical use of AI for the classification of HSIL/LSIL by both colposcopy and HPV type may be feasible.

New method for determining fibrinogen and FDP threshold criteria by artificial intelligence in cases of massive hemorrhage during delivery

AIM

To investigate the feasibility of a novel method using artificial intelligence (AI), in which the fibrinogen criterion was determined by the quantitative relation between the distributions of fibrin/fibrinogen degradation products (FDPs) and fibrinogen.

METHODS

A dataset of 154 deliveries comprising more than 2000 g of blood lost due to hemorrhage, excluding disseminated intravascular coagulation (DIC), among patients from eight national perinatal centers in Japan from 2011 to 2015 were obtained. The fibrinogen threshold criterion was identified by using the function that best fit the distributions of FDP as determined by AI. FDP production was described by differential equations using a dataset containing fibrinogen levels less than the fibrinogen criterion and solved numerically.

RESULTS

A fibrinogen level of 237 mg/dL as the threshold criterion was obtained. The FDP threshold criteria were 2.0 and 8.5 mg/dL for no coagulopathy and a failed coagulation system, respectively.

CONCLUSION

The fibrinogen threshold criterion for patients with massive hemorrhage excluding DIC at delivery were obtained by selecting the functions that best fit the distributions of FDP data by using AI.

Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images

The aim of the present study was to explore the feasibility of using deep learning as artificial intelligence (AI) to classify cervical squamous epithelial lesions (SIL) from colposcopy images. A total of 330 patients who underwent colposcopy and biopsy by gynecologic oncologists were enrolled in the current study. A total of 97 patients received a pathological diagnosis of low-grade SIL (LSIL) and 213 of high-grade SIL (HSIL). An original AI-classifier with 11 layers of the convolutional neural network was developed and trained. The accuracy, sensitivity, specificity and Youden's J index of the AI-classifier and oncologists for diagnosing HSIL were 0.823 and 0.797, 0.800 and 0.831, 0.882 and 0.773, and 0.682 and 0.604, respectively. The area under the receiver-operating characteristic curve was 0.826±0.052 (mean ± standard error), and the 95% confidence interval 0.721–0.928. The optimal cut-off point was 0.692. Cohen's Kappa coefficient for AI and colposcopy was 0.437 (P<0.0005). The AI-classifier performed better than oncologists, although not significantly. Although further study is required, the clinical use of AI for the classification of HSIL/LSIL from by colposcopy may be feasible.

Feasibility of predicting live birth by combining conventional embryo evaluation with artificial intelligence applied to a blastocyst image in patients classified by age

PURPOSE

To identify the multivariate logistic regression in a combination (combination method) involving artificial intelligence (AI) classifiers in images of blastocysts along with a conventional embryo evaluation (CEE) to predict the probability of accomplishing a live birth in patients classified by maternal age.

METHODS

Retrospectively, a total of 5691 blastocysts were enrolled. Images captured 115 hours or 139 hours if not yet sufficiently large after insemination were classified according to age as follows: <35, 35-37, 38-39, 40-41, and ≥42 years old. The classifiers for each category were created by using convolutional neural networks associated with deep learning. Next, the feasibility of a method combining AI with multivariate logistic model functions by CEE was investigated.

RESULTS

The values of the area under the curve (AUC) and the accuracies to predict live birth achieved by the CEE/AI/combination methods were 0.651/0.634/0.655, 0.697/0.688/0.723, 0.771/0.728/0.791, 0.788/0.743/0.806 and 0.820/0.837/0.888, and 0.631/0.647/0.616, 0.687/0.675/0.671, 0.725/0.697/0.732, 0.714/0.776/0.801, and 0.910/0.866/0.784 for age categories of <35, 35-37, 38-39, 40-41, and ≥42 years old, respectively.

CONCLUSIONS

Though there were mostly no significant differences regarding the AUC and the sensitivity plus specificity in all age categories, the combination method seemed to be the best.

Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image

PURPOSE

To make the artificial intelligence (AI) classifiers of the image of the blastocyst implanted later in order to predict the probability of achieving live birth.

METHODS

A system for using the machine learning approaches, which are logistic regression, naive Bayes, nearest neighbors, random forest, neural network, and support vector machine, of artificial intelligence to predict the probability of live birth from a blastocyst image was developed. Eighty images of blastocysts that led to live births and 80 images of blastocysts that led to aneuploid miscarriages were used to create an AI-based method with 5-fold cross-validation retrospectively for classifying embryos.

RESULTS

The logistic regression method showed the best results. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.65, 0.60, 0.70, 0.67, and 0.64, respectively. Area under the curve was 0.65 ± 0.04 (mean ± SE). Estimated probability of belonging to the live birth category was found significantly related to the probability of live birth (P < 0.005).

CONCLUSIONS

Classifiers using artificial intelligence applied toward a blastocyst image have a potential to show the probability of live birth being the outcome.

Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age

PURPOSE

To identify artificial intelligence (AI) classifiers in images of blastocysts to predict the probability of achieving a live birth in patients classified by age. Results are compared to those obtained by conventional embryo (CE) evaluation.

METHODS

A total of 5691 blastocysts were retrospectively enrolled. Images captured 115 hours after insemination (or 139 hours if not yet large enough) were classified according to maternal age as follows: <35, 35-37, 38-39, 40-41, and ≥42 years. The classifiers for each category and a classifier for all ages were related to convolutional neural networks associated with deep learning. Then, the live birth functions predicted by the AI and the multivariate logistic model functions predicted by CE were tested. The feasibility of the AI was investigated.

RESULTS

The accuracies of AI/CE for predicting live birth were 0.64/0.61, 0.71/0.70, 0.78/0.77, 0.81/0.83, 0.88/0.94, and 0.72/0.74 for the age categories <35, 35-37, 38-39, 40-41, and ≥42 years and all ages, respectively. The sum value of the sensitivity and specificity revealed that AI performed better than CE (P = 0.01).

CONCLUSIONS

AI classifiers categorized by age can predict the probability of live birth from an image of the blastocyst and produced better results than were achieved using CE.

Does local infiltration anesthesia on laparoscopic surgical wounds reduce postoperative pain? Randomized control study

PURPOSE

Recently, endoscopic surgeries are widely performed in the gynecological field. Several studies on the use of local anesthesia for pain control after laparoscopic surgery have been conducted; however, its effects remain controversial. Herein, a randomized control study on gynecological laparoscopic surgeries was conducted to analyze the effectiveness of local anesthesia on postoperative pain.

METHODS

Patients who underwent laparoscopic surgeries due to gynecologic benign diseases or endometrial cancer in the early stage were enrolled, and randomly divided into intervention (injected with levobupivacaine), and control (injected with saline) groups. The primary outcome was the dosage of analgesic consumption within 12 hours postoperatively.

RESULTS

A total of 147 patients were enrolled in the intervention group and 147 in the control group. The outcome of local anesthesia was not significantly different between the two groups during the whole analysis. We analyzed the effects of local anesthesia in the laparoscopic surgery subgroup. The dosage of analgesic consumption within 12 h after a laparoscopic hysterectomy (TLH) or TLH with pelvic lymph node dissection (TLH+PLD) in the intervention group was significantly smaller than that in the control group.

CONCLUSION

Local infiltration anesthesia can effectively reduce postoperative pain in patients who underwent TLH or TLH +PLD.

Formulae Based on Biomathematics to Estimate the Standard Value of Fetal Growth of Japanese

We devised biomathematics-based formulae to estimate the standard values of fetal growth of Japanese after 22 weeks' gestation. The growth rates of bi-parietal diameter (BPD), abdominal circumference (AC), femur length (FL), and estimated fetal body weight (EFBW) at the time of gestation were assumed to be proportional to the product of the value at the time and the rest value of an unknown maximum value, respectively. The EFBW was also assumed to follow a multiple logistic function of BPD, AC and FL to fit the standard values of Japanese fetuses published by the Japan Society of Ultrasonics in Medicine. The Mann-Whitney test was used for statistical analysis. The values as a function of gestational day, t, were as follows: BPD(t)=99.6/(1+exp (2.725-0.01837＊t)) (mm); AC(t)=39.7/(1+exp (2.454-0.01379＊t)) (cm); FL(t)=79.6/(1+exp (2.851-0.01710＊t)) (mm); EFBW(t)=8045.1/(1+exp (6.028-0.06582＊BPD(t)-0.1469＊AC(t)+ 0.07377＊FL(t))) (g). EFBW as a function of BPD, AC and FL was as follows: EFBW=8045.1/(1+exp (4.747+ 0.02584＊BPD+0.1010＊AC-0.1416＊FL)) (g). When the BPD, AC and FL were at -2 standard deviation (SD), -1SD, mean and + 2SD, the EFBW values calculated by the formula were statistically closer to the standard values than conventional formulas with p-values of 4.871×10-7, 4.228×10-7, 9.777×10-7 and 0.028, respectively. The formulae based on biomathematics might be useful to estimate the fetal growth standard values.

Abstract P6-07-03: Long non-coding RNA H19 promotes cancer stemness and worsen breast cancer survival

Background: Cancer stem cells (CSC) are good sources of tumor initiation, heterogeneity, progression, and metastasis because of their unique characteristics. Several potential markers for CSCs have been suggested for breast cancer, including CD44+/CD24−/low, aldehyde dehydrogenase 1 (ALDH1), and epithelial cell adhesion molecule/epithelial-specific antigen. We previously reported that ALDH1 gene expression is related to aggressive phenotypes and poor prognosis in breast cancers. In this study, we conducted differential analysis of mRNA expression in ALDH1-positive breast cancer to identify genes associated with CSC. Next, we performed basic and clinical studies of one gene.

Methods: Messenger RNA was isolated from ALDH1-positive cells and ALDH1-negative cells in 5 ALDH1-positive breast cancers. Microarray analysis revealed that several genes were significantly associated with the ALDH1 gene. Among them, we examined a long non-coding RNA of H19 in this study. We evaluated the effect of H19 on CSCs using RNA interference and a sphere formation assay using two cell lines, HCC1937 and iCSCL10A cells. We also investigated H19 expression in 192 surgical specimens by in situ hybridization and analyzed the relationship between H19 expression and clinic pathological findings in breast cancer patients.

Results: Through in vitro experiments, we confirmed that suppression of H19 reduced sphere formation in both HCC1937 and iCSCL10A cells. Among surgical specimens, 48 samples (25%) expressed H19. We verified thatH19 positivity was significantly higher in ALDH1-positive cases than in ALDH1-negative cases (68% vs 9.7%, p &lt; 0.001). H19 was significantly highly expressed in triple-negative breast cancer (TNBC) (46%) compared with other subtypes: luminal (33%), luminal-HER2 (6%), and HER2-enriched subtype (15%). H19-positive patients showed significantly worse prognosis (5-year disease-free survival 75.8% vs 91.5%, p = 0.001 and 5-year overall survival 88.7% vs 97.7%, p = 0.002). The effect of H19 expression on prognosis was the most significant in TNBC compared to in other subtypes (5-year disease-free survival 63.6% vs 88.9%, p = 0.038).

Conclusions: H19 is clearly associated with CSCs and correlated with poor prognosis in breast cancer patients, particularly TNBC. Our future studies will investigate the role of H19 in maintaining the nature of CSCs and protein-coding genes associated with H19.

Citation Format: Shima H, Kida K, Yamada A, Sugae S, Narui K, Miyagi Y, Ryo A, Ichikawa Y, Ishikawa T, Endo I. Long non-coding RNA H19 promotes cancer stemness and worsen breast cancer survival [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P6-07-03.

Validation of the distress and impact thermometer and the changes of mood during the first 6 months of treatment in gynecological cancer patients: a Kansai Clinical Oncology Group (KCOG)-G1103 prospective study

PURPOSE

To verify distress and impact thermometer (DIT) for screening emotional distress in gynecological cancer patients by Hospital Anxiety and Depression Scale total (HADS-T) as gold standard and to assess emotional changes by DIT and HADS-T.

METHODS

A prospective study was conducted in newly diagnosed gynecological cancer patients during the peri-treatment period after the cancer diagnosis followed by 6-month. We defined a HADS-T score of ≥11 as being indicative of emotional distress.

RESULTS

117 patients were enrolled between May 1, 2011 and March 31, 2012, and 95 were eligible. The median age was 54 years (range 31-77). (1) From the baseline to 3-month, distress (DIT-D) ≥4 with Impact (DIT-I) ≥2 exhibited sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV) of 0.776 [95 % confidential interval (CI) 0.688, 0.850], 0.889 (95 % CI 0.824, 0.954), 0.868 (95 % CI 0.792, 0.949), and 0.808 (95 % CI 0.731, 0.886), respectively. (2) At 6-month, DIT-D ≥2 with DIT-I ≥1 exhibited sensitivity, specificity, PPV and NPV of 0.893 (95 % CI 0.778, 1), 0.825 (95 % CI 0.707, 0.942), 0.781 (95 % CI 0.638, 0.928), and 0.917 (95 % CI 0.826, 1). (3) At 6-month, the HADS-T, DIT-D, and DIT-I scores in individual patients were significantly reduced by a mean of 4.57 (p < 0.0001), 2.34 (p < 0.0001), and 1.10 (p = 0.0031), respectively, compared with those scores of baseline (Student's paired t test), but still remained high.

CONCLUSIONS

(1) On acute phase within 3-month setting, DIT; DIT-D ≥4 with DIT-I ≥2, is a reliable cut-off to screen emotional distress among gynecological cancer patients. (2) The patients' moods had improved, but not completely recovered at 6-month after the diagnosis.

Feasibility and efficacy of intraperitoneal docetaxel administration as salvage chemotherapy for malignant gynaecological ascites

Ovarian and endometrial cancers diagnosed at advanced stages are often associated with malignant ascites. This study aimed to determine the safety, feasibility and efficacy of intraperitoneal (IP) docetaxel (TXT) for the treatment of ascites. A phase I study, including nine patients, was undertaken to determine the maximum tolerable dose. Efficacy was retrospectively assessed in 18 patients treated with 40-70 mg/m(2) IP TXT between 2005 and 2012. In a phase I study, the dose was safely escalated to a maximum of 70 mg/m(2), at which level no patients had grade -3 haematological adverse events. In a retrospective study of 18 patients, seven had an Eastern Cooperative Oncology Group performance status of 3; 16 had prior paclitaxel administration and two, with doses of 40 and 70 mg/m(2), experienced a serological response and a decrease in paracentesis. Thus, palliative treatment of recurrent OC should be further studied with 40 mg/m(2) among more patients, and 70 mg/m(2) could be evaluated for first-line IP chemotherapy.

Global histone modification of H3K27 correlates with the outcomes in patients with metachronous liver metastasis of colorectal cancer

BACKGROUND

We evaluated the methylation patterns of histone H3 lysine 27 (H3K27), H3 lysine 36 (H3K36) and the expression of H3K27 methylase EZH2 in patients with colorectal carcinomas with metachronous liver metastasis to search for biomarkers identifying these patients.

METHODS

Double 2-mm core tissue microarrays were made from 54 paraffin-embedded samples of primary colorectal adenocarcinomas and corresponding liver metastases and examined using an immunohistochemical analysis of dimethylation and trimethylation in H3K27, H3K36 and EZH2. Positive tumor cell staining for each histone modification (H-score) was used to classify patients into low- and high-staining groups, which were then examined to identify any correlations between the clinicopathological parameters and the clinical outcomes.

RESULTS

The H-scores of H3K27me2 were lower in the liver metastases than in the corresponding primary tumors, while the H-scores of H3K36me2 were higher in the liver metastases than in the corresponding primary tumors (P < 0.001). H3K27me2 in the primary tumors correlated with tumor size (P = 0.016), H3K36me2 in the primary tumors correlated with histological type (P = 0.038), and H3K36me3 in the primary tumors correlated with lymph node metastasis (P = 0.017). In addition, lower levels of H3K27me2 in the primary tumors correlated with poorer survival rates (P = 0.039). The multivariate survival analysis showed that the H3K27me2 status is an independent prognostic factor for colorectal cancer patients (P = 0.047).

CONCLUSIONS

Our findings suggest that the methylation level of H3K27me2 detected with immunohistochemistry may be an independent prognostic factor for metachronous liver metastasis of colorectal carcinomas.

Use of human equilibrative nucleoside transporter 1 to predict survival after adjuvant gemcitabine chemotherapy in resected pancreatic adenocarcinoma

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Background: Gemcitabine is a promising adjuvant treatment for patients with resected pancreatic adenocarcinoma. Human equilibrative nucleoside transporter 1 (hENT1) is the major transporter responsible for 2′, 2′-difluoro-2deoxycytidine (gemcitabine) uptake into cells. The aim of this study was to determine the outcomes according to the expression of hENT1 in tumor cells in patients treated with adjuvant gemcitabine chemotherapy after curative resection.

Methods: We studied 27 pancreatic adenocarcinoma patients treated with gemcitabine adjuvant chemotherapy after curative resection and 8 gemcitabine naïve patients between 2006 and 2008. The hENT expressions were assessed using immunohistochemistry. The staining intensity of hENT1 protein was assigned a score from 0 to 3 based on staining with 0: no staining, 1: weakly positive, 2: moderately positive, 3: strongly positive. The percentage of positive tumor cells was scored as follows, 0: no positive tumor cell; 1: < 50% positive cells, 2: 51-80% positive cells, 3: > 81% positive cells. The hENT1 score was obtained by calculating the sum of these two scores. Each patients received adjuvant chemotherapy by either protocols as follows; GEM 1,000 mg/m2biweekly × 12 (6 months) or GEM 1,000 mg/m2Days 1,8,15; every 4 weeks for 6 months.

Results: 11 patients were assigned to low hENT1 expression group (hENT1 score <4) and 16 patients to high hENT1 group (hENT1 score 4,5,6). The median DFS was 7.3 months (95% CI, 3.6-11.1) in the low hENT1 group, and 9.3 months (95% CI, 4.2-14.5) in the high hENT1 group. The median OS was 11.8 months (95% CI, 6.9- 16.6) in the low hENT1 group, and 22.2 months (95% CI, 11.5-32.9). The high hENT1 group had significantly longer DFS (Log-rank, p=0.04) and OS (p=0.02). In the gemcitabine naïve patients after curative resection, neither DFS nor OS correlated with hENT1 expression.

Conclusions: In the pancreatic cancer patients treated with adjuvant gemcitabine chemotherapy after curative resection, both DFS and OS correlated with hENT1 expression. The expression of hENT1 may provides prognostic information and predictive for benefit from gemcitabine in these patients.

No significant financial relationships to disclose.

Abstract P1-11-13: Triple Negative or HER2 Positive Subtypes of Breast Cancer Groups Are Chemo-Sensitive, but Higher Rate of Brain Metastasis Contributes Poorer Prognosis

Background: The purpose of this study was to determine the primary chemosensitivity and prognosis among women with four common breast subtypes, Luminal A, Luminal B, HER2 and Triple negative (TN). In this study, we evaluated the response to primary chemotherapy of each subtype, reported the outcome of each subgroup after primary chemotherapy.

Method: We analyzed the outcome and characteristics of patients treated with primary chemotherapy using anthracycline and/or taxanes. Before initiation of chemotherapy, invasive carcinoma was confirmed on initial biopsy specimen obtained and hormone receptor status and HER2/neu status was also determined on this specimen. ER and PgR positivity was recognized at a cut-off of &gt; 10% positive nuclei by immunohistochemistory (IHC). HER2/neu-positive status was defined as either 3+ by IHC or presence of gene amplification by fluorescence in situ hybridization testing. Breast cancer subtypes were defied as follow, TN (ER-, PgR-, HER2-), Luminal A (ER+ and/or PgR+, HER2-), Luminal B (ER+ and/or PgR+, HER2+), HER2 (ER-, PgR-, HER2+).

Result: Between 2000 and 2007, 639 breast cancer patients were treated with primary chemotherapy at Cancer Institute Hospital. Clinical and immunohistochemical data was available on 503 patients. Median observation period was 49.9 months (2.8-122.4). In these cases, 105 cases (20.9%) were defined as TN, 276 cases (54.9%) were defined as Luminal A, 49 cases (9.7%) were defined as Luminal B, 73 cases (14.5%) were defined as HER2, respectively. 138 patients (27.4%) received anthracycline-based regimen, 139 patients (27.6%) received taxane, 227 patients (45.1%) received taxane-anthracycline combination regimen. The pathologic complete response (pCR) rate of each group was 15.2%, 2.0%, 8.2%, 16.4%, in TN, Luminal A, Luminal B, HER2, respectively (P&lt;0.001). The 5-yr disease free survival estimated 69.1%, 74.4%, 62.8%, 70.6% (p=0.140), and the 5-yr overall survival estimated 69.1%, 75.6%, 88.6%, 69.4% in TN, Luminal A, Luminal B, HER2, respectively (p=0.007). Mean survival time from the first recurrence was 21.1 months (95%CI 11.5-30.7), 40.6 months (95%CI 31.6-49.6), 81.8 months (95%CI 59.1-104.5), 30.0 months (95%CI 21.1-38.9), respectively (P&lt;0.001). According to the first recurrence, most frequent visceral metastatic site of TN and HER2 patients was brain (P&lt;0.001), and median time to brain metastasis was

13.2 months (95%CI 8.5-17.9). Surprisingly, three (21.4%) of the patients who had brain metastasis resulted in pCR by primary chemotherapy. Of note, Luminal A patients were more likely to have bone metastasis than other groups at first (p=0.003), and median time to bone metastasis was

16.3 months (95%CI 14.1-18.6).

Conclusions: With primary chemotherapy, pCR rate of TN and HER2 were higher than Luminal groups, but they developed brain metastasis early irrespective of pCR, this might contribute to their worse prognosis. In contrast, Luminal A developed bone metastasis at first, this might result in good prognosis instead of their low pCR rate.

Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P1-11-13.

Secretion of intelectin-1 from malignant pleural mesothelioma into pleural effusion

Background:

Malignant pleural mesothelioma (MPM) is a rare but fatal tumour. Although most MPM patients show pleural effusion at even the early stage, it is hard to diagnose as MPM at the early stage because a sensitive and reliable diagnostic marker for MPM has not been found in plasma or pleural effusion.

Methods:

In this study, we investigated whether intelectin-1 was specifically contained in MPM cells and the pleural effusion of MPM patient by immunohistochemistry, western blotting, and enzyme-linked immunosorbent assay.

Results:

Malignant pleural mesothelioma cell lines, but not lung adenocarcinoma cell lines, secreted intelectin-1. In immunohistochemistry, epithelioid-type MPMs, but neither pleura-invading lung adenocarcinomas nor reactive mesothelial cells near the lung adenocarcinomas, were stained with anti-intelectin antibodies. Pleural effusion of MPM patients contained a higher concentration of intelectin-1 than that of lung cancer patients.

Conclusion:

These results suggest that detection of intelectin-1 may be useful for a differential diagnosis of epithelioid-type MPM in immunohistochemistry and that a high concentration of intelectin-1 in pleural effusion can be used as a new marker for clinical diagnosis of MPM.