Randomized multicenter phase II trial of prophylactic irradiation of para-aortic lymph nodes in advanced cervical cancer according to tumor hypoxia: Korean Radiation Oncology Group (KROG 07-01) study.

We conducted a prospective phase II study on whether extended-field irradiation (EFI) confers survival benefits depending on hypoxic markers in locally advanced uterine cervical cancer (LAUCC). RNA-seq was performed to identify immune and hypoxic gene signatures. A total of 288 patients were randomized to either EFI or pelvic radiotherapy (PRT). All patients completed chemoradiotherapy. Overall, significantly higher 5-year para-aortic recurrence free survival (PARFS) rate occurred in EFI (97.6%) than in PRT group (87.2%), with marginal tendency to improve disease-free survival (DFS; 78% vs 70%, P = .066). Subgroup analyses were performed based on carbonic anhydrase 9 (CA9)-only positive, CA9/hypoxia-inducible factor (HIF) double positive and CA9 negative. In the CA9-only positive, EFI successfully increased 5-year PARFS (100% vs 76.4%, P = .010), resulting in significantly improved long-term DFS (85.7% vs 54.7%, P = .023) compared to the PRT, while there was no such benefit of EFI in the CA9/HIFs double positive. RNA-seq analysis identified distinct immunehigh subgroup with negative correlation with hypoxia gene signatures (R = -.37, P < .01), which showed a higher 5-year DFS than the immunelow (P = .032). Hypoxia-related genes were upregulated in the CA9/HIFs double positive compared to CA9 negative (P < .05). Only 17.4% of patients in CA9-negative group showed immunelow signatures, while 40.0% of patients in the double-positive group exhibited immunelow signatures. In conclusion, EFI improved PARFS significantly in all patients, but therapeutic efficacy of EFI in terms of improved DFS was solely observed in CA9-only positive LAUCC, and not in CA9/HIFs double-positive subgroup. RNA-seq analysis suggested that hypoxia-induced immunosuppression may be related to treatment resistance in LAUCC.

Incidence and treatment outcomes of ovarian sarcoma compared to epithelial ovarian cancer from the national cancer registry.

PURPOSE: To investigate the incidence and survival outcomes of primary ovarian sarcoma compared to those of epithelial ovarian cancer. METHODS: Data on primary ovarian sarcoma patients (n = 1361) and epithelial ovarian cancer patients (n = 30,366) between 1999 and 2017 were obtained from the Korea Central Cancer Registry, and their respective age-standardized incidence rate (ASR) and relative survival rate were calculated and compared. RESULTS: Based on the ASR, the incidence of epithelial ovarian cancer was 4.75 per 100,000 women, while that of primary ovarian sarcoma was 0.22 per 100,000 women. The ASR ratio was 21.94 without significant change of ASR during the study period. Primary ovarian sarcoma had a better survival curve compared with epithelial ovarian cancer, though the difference was not statistically significant (5 yr overall survival 64.0% vs. 61.5%; p = 0.6030). In addition, among the pure sarcomas, the fibrosarcoma histologic subtype showed the best overall survival, and that of liposarcomas and stromal cell sarcoma were behind that (5 yr overall survival 85.0%, 76.7%, and 72.7%; p < 0.0001). CONCLUSIONS: The incidence of primary ovarian sarcoma is quite low, with an ASR of 0.22/100,000 during the last 20 years. There were no significant differences between survival rates of primary ovarian sarcoma and epithelial ovarian cancer.

Second primary cancer after primary peritoneal, epithelial ovarian, and fallopian tubal cancer: a retrospective study.

BACKGROUND: In this retrospective study, data from patients listed in the Korea Central Cancer Registry during 1993-2014 were analysed, to investigate the incidence and survival of second primary cancers (SPCs) after a diagnosis of primary peritoneal, epithelial ovarian, and fallopian tubal (POFT) cancer. METHODS: The standardised incidence ratio (SIR) and survival outcomes of patients with SPCs among POFT cancer survivors were analysed. RESULTS: Among 20,738 POFT cancer survivors, 798 (3.84%) developed SPCs, at an average interval of 5.50 years. SPC risk in POFT survivors (SIR, 1.29) was higher compared to the general population. The most high-risk type of SPC was leukaemia (3.07) followed by the lung and bronchus (1.80), colon (1.58), rectum and rectosigmoid junction (1.42), thyroid (1.34), and breast (1.26). In women aged < 60 years, cancer of the breast (1.30), ascending colon (2.26), and transverse colon (4.07) as SPCs increased. Up to 10 years after POFT cancer treatment, leukaemia risk increased, especially in those < 60 years, with serous histology, and with distant stage, which required aggressive chemotherapy. The median overall survival time was 12.8 years and 14.3 years in women with POFT cancer and SPCs, respectively. Thyroid and breast cancers were favourable prognostic markers among SPCs. CONCLUSIONS: The overall SPC risk increases in POFT cancer survivors, especially in those < 60 years. The cancer risk of breast and the proximal colon increase based on hereditary predisposition, while leukaemia likely develops from aggressive treatment. The median overall survival is favourable in POFT cancer survivors with SPCs.

Comparison of Lower Extremity Edema in Locally Advanced Cervical Cancer: Pretreatment Laparoscopic Surgical Staging with Tailored Radiotherapy Versus Primary Radiotherapy.

BACKGROUND: This study investigated the clinical manifestations of lower extremity edema (LEE) in locally advanced cervical cancer patients treated with two different strategies. METHODS: In total, 79 cervical cancer survivors with International Federation of Gynecology and Obstetrics stage IB2-IIB were included. Six survivors with stage IB1 and who had been suspicious for lymph node metastasis on pretreatment image also were included. Forty-two patients received radiotherapy after pretreatment laparoscopic surgical staging (Group 1), and 43 patients received primary radiotherapy (Group 2). The patients' medical records and survey results of the Korean version of the Gynecologic Cancer Lymphedema Questionnaire (GCLQ-K) were analyzed. RESULTS: The incidence of LEE was higher in Group 1 than in Group 2 (69.0 vs. 11.6 %; P < 0.001). The duration of LEE was longer in Group 1 (mean 77.3 vs. 9.4 months). At the time of survey, 47.6 % of the patients in Group 1 were clinically diagnosed with lymphedema compared with no patients in Group 2. In GCLQ-K, the mean symptom cluster scores for general swelling (0.74 vs. 0.09; P < 0.001), limb swelling (0.22 vs. 0.00; P = 0.006), and heaviness (0.45 vs. 0.23; P = 0.033) were significantly higher in Group 1. One patient in Group 1 developed lymphedema-related angiosarcoma that was diagnosed at 7.8 years after surgery. CONCLUSIONS: Patients with cervical cancer who underwent radiotherapy after laparoscopic surgical staging more commonly experienced LEE and related symptoms than patients who underwent primary radiotherapy. As LEE decreases patients' quality of life, it should be considered during patient consultation and surveillance.

Low expression of miR-449 in gynecologic clear cell carcinoma.

Clinical detection of ovarian clear cell carcinomas is important because of the poor prognosis. To identify microRNA profiles specific for clear cell carcinomas, microRNA expression profiles were compared between clear cell carcinomas and serous carcinomas of the ovary using microRNA microarray. In parallel, clear cell carcinomas were compared with germ cell tumors of the ovary. Six microRNAs differentially expressed between ovarian clear cell and serous carcinomas distinguished uterine clear cell carcinomas from endometrioid carcinomas. MiR-449 was underexpressed in both ovarian and uterine clear cell carcinomas. When germ cell tumors were compared with clear cell carcinomas of the ovary, miR-302d was the most significantly overexpressed microRNA in germ cell tumors. Thus, here we describe microRNA profiles characteristic for clear cell carcinomas of the ovary and uterus.

MicroRNA expression profiling and Notch1 and Notch2 expression in minimal deviation adenocarcinoma of uterine cervix.

BACKGROUND: MicroRNA (miRNA) expression is known to be deregulated in cervical carcinomas. However, no data is available about the miRNA expression pattern for the minimal deviation adenocarcinoma (MDA) of uterine cervix. We sought to detect deregulated miRNAs in MDA in an attempt to find the most dependable miRNA or their combinations to understand their tumorigenesis pathway and to identify diagnostic or prognostic biomarkers. We also investigated the association between those miRNAs and their target genes, especially Notch1 and Notch2. METHODS: We evaluated miRNA expression profiles via miRNA microarray and validated them using.real-time PCR assays with 24 formalin-fixed, paraffin-embedded tissue blocks of MDA and 11 normal proliferative endocervical tissues as control. Expression for Notch1 and 2 was assessed by immunohistochemistry. RESULTS: MiRNA-135a-3p, 192-5p, 194-5p, and 494 were up-regulated, whereas miR-34b-5p, 204-5p, 299-5p, 424-5p, and 136-3p were down-regulated in MDA compared with normal proliferative endocervical tissues (all P<0.05). Considering the second-order Akaike Information Criterion consisting of likelihood ratio and number of parameters, miR-34b-5p showed the best discrimination power among the nine candidate miRNAs. A combined panel of miR-34b-5p and 194-5p was the best fit model to discriminate between MDA and control, revealing 100% sensitivity and specificity. Notch1 and Notch2, respective target genes of miR-34b-5p and miR-204-5p, were more frequently expressed in MDA than in control (63% vs. 18%; 52% vs. 18%, respectively, P<0.05). MiR-34b-5p expression level was higher in Notch1-negative samples compared with Notch1-positive ones (P<0.05). Down-regulated miR-494 was associated with poor patient survival (P=0.036). CONCLUSIONS: MDA showed distinctive expression profiles of miRNAs, Notch1, and Notch2 from normal proliferative endocervical tissues. In particular, miR-34b-5p and 194-5p might be used as diagnostic biomarkers and miR-494 as a prognostic predictor for MDA. The miR-34b-5p/Notch1 pathway as well as Notch2 might be important oncogenic contributors to MDA.

Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid.

Ascites cytology is a cost-effective test for metastatic colorectal cancer (CRC) in the abdominal cavity. However, metastatic carcinoma of the peritoneum is difficult to diagnose based on biopsy findings, and ascitic aspiration cytology has a low sensitivity and specificity and a high inter-observer variability. The aim of the present study was to apply artificial intelligence (AI) to classify benign and malignant cells in ascites cytology patch images of metastatic CRC using a deep convolutional neural network. Datasets were collected from The OPEN AI Dataset Project, a nationwide cytology dataset for AI research. The numbers of patch images used for training, validation, and testing were 56,560, 7068, and 6534, respectively. We evaluated 1041 patch images of benign and metastatic CRC in the ascitic fluid to compare the performance of pathologists and an AI algorithm, and to examine whether the diagnostic accuracy of pathologists improved with the assistance of AI. This AI method showed an accuracy, a sensitivity, and a specificity of 93.74%, 87.76%, and 99.75%, respectively, for the differential diagnosis of malignant and benign ascites. The diagnostic accuracy and sensitivity of the pathologist with the assistance of the proposed AI method increased from 86.8% to 90.5% and from 73.3% to 79.3%, respectively. The proposed deep learning method may assist pathologists with different levels of experience in diagnosing metastatic CRC cells of ascites.

Improved Diagnostic Accuracy of Thyroid Fine-Needle Aspiration Cytology with Artificial Intelligence Technology.

Background: Artificial intelligence (AI) is increasingly being applied in pathology and cytology, showing promising results. We collected a large dataset of whole slide images (WSIs) of thyroid fine-needle aspiration cytology (FNA), incorporating z-stacking, from institutions across the nation to develop an AI model. Methods: We conducted a multicenter retrospective diagnostic accuracy study using thyroid FNA dataset from the Open AI Dataset Project that consists of digitalized images samples collected from 3 university hospitals and 215 Korean institutions through extensive quality check during the case selection, scanning, labeling, and reviewing process. Multiple z-layer images were captured using three different scanners and image patches were extracted from WSIs and resized after focus fusion and color normalization. We pretested six AI models, determining Inception ResNet v2 as the best model using a subset of dataset, and subsequently tested the final model with total datasets. Additionally, we compared the performance of AI and cytopathologists using randomly selected 1031 image patches and reevaluated the cytopathologists' performance after reference to AI results. Results: A total of 10,332 image patches from 306 thyroid FNAs, comprising 78 malignant (papillary thyroid carcinoma) and 228 benign from 86 institutions were used for the AI training. Inception ResNet v2 achieved highest accuracy of 99.7%, 97.7%, and 94.9% for training, validation, and test dataset, respectively (sensitivity 99.9%, 99.6%, and 100% and specificity 99.6%, 96.4%, and 90.4% for training, validation, and test dataset, respectively). In the comparison between AI and human, AI model showed higher accuracy and specificity than the average expert cytopathologists beyond the two-standard deviation (accuracy 99.71% [95% confidence interval (CI), 99.38-100.00%] vs. 88.91% [95% CI, 86.99-90.83%], sensitivity 99.81% [95% CI, 99.54-100.00%] vs. 87.26% [95% CI, 85.22-89.30%], and specificity 99.61% [95% CI, 99.23-99.99%] vs. 90.58% [95% CI, 88.80-92.36%]). Moreover, after referring to the AI results, the performance of all the experts (accuracy 96%, 95%, and 96%, respectively) and the diagnostic agreement (from 0.64 to 0.84) increased. Conclusions: These results suggest that the application of AI technology to thyroid FNA cytology may improve the diagnostic accuracy as well as intra- and inter-observer variability among pathologists. Further confirmatory research is needed.

Transabdominal cardiophrenic lymph node dissection (CPLND) via incised diaphragm replace conventional video-assisted thoracic surgery for cytoreductive surgery in advanced ovarian cancer.

OBJECTIVE: The objective of this study is to describe the feasibility of the new approach, transabdominal CPLND, via incised diaphragm in patients with ovarian cancer by gynecologic oncologists instead of the conventional video-assisted thoracic surgery. METHODS: From November 2008 to December 2011, 11 women (10 primary and 1 recurrent ovarian cancers) underwent CPLND for the extensive cytoreductive surgeries via incised muscle of the right diaphragm by gynecologic oncologists. All ≥5 mm tumors in CPLN, which were the criterion for suspicious malignancy on preoperative axial computed tomogram, were completely resected by gynecologic oncologists. RESULTS: The median tumor size of the CPLN was 10mm (range, 7-17 mm) and metastasis was identified in 45% (5/11) of ≥5 mm CPLN on preoperative computed tomogram. The median number of harvested CPLND was 3 (range 1-12) and metastatic node was 1 (range, 0-10). There was no significant morbidity related to CPLND and mortality associated with surgery. Ten patients achieved the no gross residual disease and one patient accomplished gross residual-1, indicating residual disease measuring ≤1 cm in maximal diameter. CONCLUSION: Transabdominal CPLND via incised diaphragm is feasible as a part of the cytoreductive surgery without significant morbidities by gynecologic oncologist. This procedure could substitute the conventional video-assisted thoracic surgery.

Clear cell carcinomas of the ovary: a multi-institutional study of 129 cases in Korea with prognostic significance of Emi1 and Galectin-3.

Accurate diagnosis of ovarian clear cell carcinoma (CCC) is important because of its poor prognosis with chemoresistance and a high recurrent rate. The clinicopathologic characteristics and prognostic significance of the cell cycle regulator [early mitotic inhibitor-1 (Emi1)] and galactoside-binding protein (Galectin-3) were evaluated. Among 155 CCCs from 18 hospitals in Korea between 1995 and 2006, 129 pure CCCs were selected with consensus using immunohistochemical stains for hepatocyte nuclear factor-1ß, Wilms' tumor protein, and estrogen receptor. The expressions of Emi1, Galectin-3, p53, and Ki-67 labeling index were analyzed with clinicopathologic parameters and the patient's survival. The mean age of the patients was 49.6 yr; the tumors were bilateral in 10.9%, and the average size was 12 cm. Adenofibromatous component was found in 7%, and endometriosis in 48.1% of the cases. Psammoma body was seen in 16.3%. Disease-free survival and overall survival rates were 78.3% and 79.1%, respectively. The International Federation of Obstetrics and Gynecology (FIGO) stage was the most important prognostic indicator. Emi1 expression (>5%) was seen in 23.3% of CCCs, and associated with high FIGO grades and poor overall survival (P<0.05). High Galectin-3 (≥80%) expression was seen in 59.7% of CCCs, and associated with FIGO stages III and IV, and high Ki-67 labeling index. High Ki-67 labeling index (≥50%) and p53 expression (≥50%) were seen in 27.1% and 18.6% of CCCs, respectively, but there was no clinicopathologic and prognostic significance. On the basis of the fact that the expression of Emi1 in CCC was correlated with a high histologic grade and worse overall survival, target therapy using inhibitors of Emi1 may be tried in the management of CCC patients with Emi1 expression.

Data-Efficient Computational Pathology Platform for Faster and Cheaper Breast Cancer Subtype Identifications: Development of a Deep Learning Model.

BACKGROUND: Breast cancer subtyping is a crucial step in determining therapeutic options, but the molecular examination based on immunohistochemical staining is expensive and time-consuming. Deep learning opens up the possibility to predict the subtypes based on the morphological information from hematoxylin and eosin staining, a much cheaper and faster alternative. However, training the predictive model conventionally requires a large number of histology images, which is challenging to collect by a single institute. OBJECTIVE: We aimed to develop a data-efficient computational pathology platform, 3DHistoNet, which is capable of learning from z-stacked histology images to accurately predict breast cancer subtypes with a small sample size. METHODS: We retrospectively examined 401 cases of patients with primary breast carcinoma diagnosed between 2018 and 2020 at the Department of Pathology, National Cancer Center, South Korea. Pathology slides of the patients with breast carcinoma were prepared according to the standard protocols. Age, gender, histologic grade, hormone receptor (estrogen receptor [ER], progesterone receptor [PR], and androgen receptor [AR]) status, erb-B2 receptor tyrosine kinase 2 (HER2) status, and Ki-67 index were evaluated by reviewing medical charts and pathological records. RESULTS: The area under the receiver operating characteristic curve and decision curve were analyzed to evaluate the performance of our 3DHistoNet platform for predicting the ER, PR, AR, HER2, and Ki67 subtype biomarkers with 5-fold cross-validation. We demonstrated that 3DHistoNet can predict all clinically important biomarkers (ER, PR, AR, HER2, and Ki67) with performance exceeding the conventional multiple instance learning models by a considerable margin (area under the receiver operating characteristic curve: 0.75-0.91 vs 0.67-0.8). We further showed that our z-stack histology scanning method can make up for insufficient training data sets without any additional cost incurred. Finally, 3DHistoNet offered an additional capability to generate attention maps that reveal correlations between Ki67 and histomorphological features, which renders the hematoxylin and eosin image in higher fidelity to the pathologist. CONCLUSIONS: Our stand-alone, data-efficient pathology platform that can both generate z-stacked images and predict key biomarkers is an appealing tool for breast cancer diagnosis. Its development would encourage morphology-based diagnosis, which is faster, cheaper, and less error-prone compared to the protein quantification method based on immunohistochemical staining.

Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid.

A Pleural effusion cytology is vital for treating metastatic breast cancer; however, concerns have arisen regarding the low accuracy and inter-observer variability in cytologic diagnosis. Although artificial intelligence-based image analysis has shown promise in cytopathology research, its application in diagnosing breast cancer in pleural fluid remains unexplored. To overcome these limitations, we evaluate the diagnostic accuracy of an artificial intelligence-based model using a large collection of cytopathological slides, to detect the malignant pleural effusion cytology associated with breast cancer. This study includes a total of 569 cytological slides of malignant pleural effusion of metastatic breast cancer from various institutions. We extracted 34,221 augmented image patches from whole-slide images and trained and validated a deep convolutional neural network model (DCNN) (Inception-ResNet-V2) with the images. Using this model, we classified 845 randomly selected patches, which were reviewed by three pathologists to compare their accuracy. The DCNN model outperforms the pathologists by demonstrating higher accuracy, sensitivity, and specificity compared to the pathologists (81.1% vs. 68.7%, 95.0% vs. 72.5%, and 98.6% vs. 88.9%, respectively). The pathologists reviewed the discordant cases of DCNN. After re-examination, the average accuracy, sensitivity, and specificity of the pathologists improved to 87.9, 80.2, and 95.7%, respectively. This study shows that DCNN can accurately diagnose malignant pleural effusion cytology in breast cancer and has the potential to support pathologists.

Excellent Outcomes in Children, Adolescents, and Young Adults with Ovarian Germ Cell Tumors Treated by Either Reduced- or Standard-Dose Bleomycin.

To investigate the outcomes of children, adolescents, and young adults (AYAs) with malignant ovarian germ cell tumors (MOGCTs), we analyzed the data of 61 patients aged ≤39 years diagnosed with MOGCT between 2006 and 2022. Among 59 patients who received chemotherapy after initial diagnosis, 57 received BEP (standard dose of bleomycin with 30 units per week, n = 13) or bEP (reduced dose of bleomycin with 15 units/m2 on day 1, n = 44). The 5-year overall survival (OS) and event-free survival (EFS) rates were 98.3% and 84.9%, respectively. Reduced bleomycin dose did not adversely affect survival. Normalization of tumor markers within 3 months after surgery was significantly associated with better EFS (p < 0.01). Of the 59 surviving patients, 8 experienced surgery-related menopause, while 49 demonstrated menstrual recovery. After completion of chemotherapy, there was no significant difference in pulmonary function regarding bleomycin dose, and no overt nephrotoxicity. Approximately 60% and 25% of survivors experienced peripheral neuropathy at the end of chemotherapy and after 1 year, respectively (p < 0.01). Children and AYAs with MOGCT have favorable survival rates with minimal long-term toxicity, which are not influenced by a reduced bleomycin dose. Rapid normalization of tumor markers is associated with improved outcomes.

Deep learning-based diagnosis of lung cancer using a nationwide respiratory cytology image set: improving accuracy and inter-observer variability.

Deep learning (DL)-based image analysis has recently seen widespread application in digital pathology. Recent studies utilizing DL in cytopathology have shown promising results, however, the development of DL models for respiratory specimens is limited. In this study, we designed a DL model to improve lung cancer diagnosis accuracy using cytological images from the respiratory tract. This retrospective, multicenter study used digital cytology images of respiratory specimens from a quality-controlled national dataset collected from over 200 institutions. The image processing involves generating extended z-stack images to reduce the phase difference of cell clusters, color normalizing, and cropping image patches to 256 × 256 pixels. The accuracy of diagnosing lung cancer in humans from image patches before and after receiving AI assistance was compared. 30,590 image patches (1,273 whole slide images [WSIs]) were divided into 27,362 (1,146 WSIs) for training, 2,928 (126 WSIs) for validation, and 1,272 (1,272 WSIs) for testing. The Densenet121 model, which showed the best performance among six convolutional neural network models, was used for analysis. The results of sensitivity, specificity, and accuracy were 95.9%, 98.2%, and 96.9% respectively, outperforming the average of three experienced pathologists. The accuracy of pathologists after receiving AI assistance improved from 82.9% to 95.9%, and the inter-rater agreement of Fleiss' Kappa value was improved from 0.553 to 0.908. In conclusion, this study demonstrated that a DL model was effective in diagnosing lung cancer in respiratory cytology. By increasing diagnostic accuracy and reducing inter-observer variability, AI has the potential to enhance the diagnostic capabilities of pathologists.

Serum HE4 level is an independent prognostic factor in epithelial ovarian cancer.

BACKGROUND: This study was designed to determine whether serum HE4 is an independent prognostic factor in ovarian cancer patients. METHODS: We measured HE4 in pretreatment serum samples from 80 women with epithelial ovarian cancer, using an enzyme-linked immunosorbent assay. The results were correlated with clinical data. RESULTS: The median serum HE4 level in ovarian cancer patients was 98.7 (range, 80.3-222.8) pg/ml. Elevated serum HE4 levels before therapy significantly correlated with a poorer progression free survival (log-rank test, P = 0.017). Multivariate analysis revealed serum HE4 to be an independent prognostic factor for progression-free survival (P = 0.036). In multivariate regression analysis, high serum HE4 levels significantly correlated with high tumor grade and serous histology (P = 0.004 and 0.017). In addition, high serum HE4 levels were significantly associated with residual tumor size and operative time (P = 0.003 and 0.033). CONCLUSIONS: Pretreatment serum HE4 seems to be an additional factor for predicting the outcome of patients with epithelial ovarian cancer. Due to its independence from established prognostic factors, serum HE4 may provide additional prognostic information.

Correlation between body mass index and prevalence of hereditary nonpolyposis colorectal cancer in Korean patients with endometrial cancer.

OBJECTIVE: The purpose of the study was to investigate the relationship between body mass index (BMI) and hereditary nonpolyposis colorectal cancer (HNPCC) in Korean women with endometrial cancer. METHODS: Among 227 patients with endometrial cancer in the study population, 20 patients (8.8%) had HNPCC. The patients were divided into 2 groups based on the BMI: nonobese (BMI ≤25 kg/m) and obese (BMI >25 kg/m); then the nonobese group was subdivided into 2 groups: normal weight (BMI <23 kg/m) and overweight (BMI, 23-25 kg/m). The distributions of BMI categories were compared between patients with sporadic endometrial cancer and with HNPCC-related endometrial cancer. RESULTS: Among 207 patients with sporadic endometrial cancer, 119 (57.5%) were nonobese and 88 patients (42.5%) were obese. Of 20 patients with endometrial cancer related to HNPCC, 10 (50.0%) were nonobese and 10 (50.0%) were obese. In a subgroup analysis of only nonobese patients, 68 patients with sporadic endometrial cancer had normal weight and 51 were overweight. On the other hand, all 10 patients with HNPCC related to endometrial cancer had normal weight. There was no significant difference between the group with HNPCC-related endometrial cancer and the group with sporadic endometrial cancer according to BMI (P = 0.221). However, BMI proportions in HNPCC related to endometrial cancer were significantly different from those in sporadic endometrial cancer (P = 0.016). Among a subgroup of nonobese patients, the proportion of normal weight was significant higher in patients with HNPCC-related endometrial cancer compared to those in sporadic endometrial cancer (P = 0.006). CONCLUSION: Body mass index was not different between sporadic endometrial cancer and HNPCC-related endometrial cancer in Koreans. However, BMI proportions in the patients with HNPCC related to endometrial cancer was significantly different from those in sporadic endometrial cancer. Specifically, among nonobese patients, the proportion of normal weight was significantly high in Korean women with HNPCC.

Matrix-assisted laser desorption/ionization mass spectrometry reveals decreased calcylcin expression in small cell lung cancer.

To date, most of the proteomic analyses on lung cancer tissue samples have been performed using surgical specimens, which are obtained after a diagnosis is made. To determine if a proteomic signature obtained from bronchoscopic biopsy samples could be found to assist with diagnosis, 50 lung cancer bronchoscopic biopsy samples and 13 adjacent normal lung tissue samples were analyzed using histology-directed, matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS). Lung tissue samples were cryosectioned, and sinapinic acid was robotically deposited on areas of each tissue section enriched in epithelial cells, either tumor or normal. Mass spectra were acquired using a MALDI-time of flight instrument. Small cell lung cancers (SCLCs) demonstrated clearly different protein profiles from normal lung tissue and from non-small cell lung cancers (NSCLCs). Calcyclin (m/z= 10,094.7) was identified to be underexpressed in small cell lung cancers, as compared with non-small cell lung cancers and normal lung tissue. An immunohistochemistry study using 152 NSCLCs and 21 SCLCs confirmed significantly reduced calcyclin stain in SCLCs. Thus, protein profiles obtained from bronchoscopic biopsy samples via MALDI MS distinguish cancerous epithelium from normal lung tissue and between NSCLCs and SCLCs.

Survival After Hyperthermic Intraperitoneal Chemotherapy and Primary or Interval Cytoreductive Surgery in Ovarian Cancer: A Randomized Clinical Trial.

Importance: Ovarian cancer has the highest mortality rate among gynecologic malignant tumors. Data are lacking on the survival benefit of hyperthermic intraperitoneal chemotherapy (HIPEC) in women with ovarian cancer who underwent primary or interval cytoreductive surgery. Objective: To assess the clinical benefit of HIPEC after primary or interval maximal cytoreductive surgery in women with stage III or IV primary advanced ovarian cancer. Design, Setting, and Participants: In this single-blind randomized clinical trial performed at 2 institutions in South Korea from March 2, 2010, to January 22, 2016, a total of 184 patients with stage III or IV ovarian cancer with residual tumor size less than 1 cm were randomized (1:1) to a HIPEC (41.5 °C, 75 mg/m2 of cisplatin, 90 minutes) or control group. The primary end point was progression-free survival. Overall survival and adverse events were key secondary end points. The date of the last follow-up was January 10, 2020, and the data were locked on February 17, 2020. Exposures: Hyperthermic intraperitoneal chemotherapy after cytoreductive surgery. Main Outcomes and Measures: Progression-free and overall survival. Results: Of the 184 Korean women who underwent randomization, 92 were randomized to the HIPEC group (median age, 52.0 years; IQR, 46.0-59.5 years) and 92 to the control group (median age, 53.5 years; IQR, 47.5-61.0 years). After a median follow-up of 69.4 months (IQR, 54.4-86.3 months), median progression-free survival was 18.8 months (IQR, 13.0-43.2 months) in the control group and 19.8 months (IQR, 13.7-55.4 months) in the HIPEC group (P = .43), and median overall survival was 61.3 months (IQR, 34.3 months to not reported) in the control group and 69.5 months (IQR, 45.6 months to not reported) in the HIPEC group (P = .52). In the subgroup of interval cytoreductive surgery after neoadjuvant chemotherapy, the median progression-free survival was 15.4 months (IQR, 10.6-21.1 months) in the control group and 17.4 months (IQR, 13.8-31.5 months) in the HIPEC group (hazard ratio for disease progression or death, 0.60; 95% CI, 0.37-0.99; P = .04), and the median overall survival was 48.2 months (IQR, 33.8-61.3 months) in the control group and 61.8 months (IQR, 46.7 months to not reported) in the HIPEC group (hazard ratio, 0.53; 95% CI, 0.29-0.96; P = .04). In the subgroup of primary cytoreductive surgery, median progression-free survival was 29.7 (IQR, 17.2-90.1 months) in the control group and 23.9 months (IQR, 12.3-71.5 months) in the HIPEC group, and the median overall survival was not reached in the control group and 71.3 months (IQR, 45.6 months to not reported) in the HIPEC group. Conclusions and Relevance: The addition of HIPEC to cytoreductive surgery did not improve progression-free and overall survival in patients with advanced epithelial ovarian cancer. Although the results are from a subgroup analysis, the addition of HIPEC to interval cytoreductive surgery provided an improvement of progression-free and overall survival. Trial Registration: ClinicalTrials.gov Identifier: NCT01091636.

Is neo-adjuvant chemotherapy a "waiver" of extensive upper abdominal surgery in advanced epithelial ovarian cancer?

BACKGROUND: The goal of this study was to explore the necessity of extensive surgical procedures in patients who received neoadjuvant chemotherapy (NAC). METHODS: We analyzed the surgical outcomes and frequency of extensive procedures required for maximal cytoreductive surgery after NAC and primary debulking surgery (PDS) in 256 women with advanced epithelial ovarian cancer. RESULTS: NAC was performed in 116 of 256 women (45.3%). In NAC group, complete cytoreduction rate and optimal cytoreduction rate were 60.3 and 92.2%, respectively. Although the NAC group comprised patients with higher risk of suboptimal cytoreduction, complete cytoreduction rate was similar to that of PDS group (57.9%, P = .69). Moreover, blood loss and surgical complexity significantly reduced in NAC group (P = .011 and .017). Extensive upper abdominal surgery (EUAS) was performed in 70 of 116 patients (60.3%) in the NAC group. The frequency of EUAS was similar between NAC and PDS group (P = .60). Among NAC group, gross upper abdominal metastasis requiring EUAS was found in 51 patients (44%, 95% confidence interval = 35.3-53.1%). CONCLUSIONS: A significant proportion of patients who received NAC still have gross metastatic tumors requiring EUAS. Gynecologic oncologists should be familiar with EUAS and be ready to perform any required procedures together with multidisciplinary teams, even in the patients who have received NAC.

Protein and lipid MALDI profiles classify breast cancers according to the intrinsic subtype.

BACKGROUND: Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) has been demonstrated to be useful for molecular profiling of common solid tumors. Using recently developed MALDI matrices for lipid profiling, we evaluated whether direct tissue MALDI MS analysis on proteins and lipids may classify human breast cancer samples according to the intrinsic subtype. METHODS: Thirty-four pairs of frozen, resected breast cancer and adjacent normal tissue samples were analyzed using histology-directed, MALDI MS analysis. Sinapinic acid and 2,5-dihydroxybenzoic acid/α-cyano-4-hydroxycinnamic acid were manually deposited on areas of each tissue section enriched in epithelial cells to identify lipid profiles, and mass spectra were acquired using a MALDI-time of flight instrument. RESULTS: Protein and lipid profiles distinguish cancer from adjacent normal tissue samples with the median prediction accuracy of 94.1%. Luminal, HER2+, and triple-negative tumors demonstrated different protein and lipid profiles, as evidenced by permutation P values less than 0.01 for 0.632+ bootstrap cross-validated misclassification rates with all classifiers tested. Discriminatory proteins and lipids were useful for classifying tumors according to the intrinsic subtype with median prediction accuracies of 80.0-81.3% in random test sets. CONCLUSIONS: Protein and lipid profiles accurately distinguish tumor from adjacent normal tissue and classify breast cancers according to the intrinsic subtype.