Deep learning image analysis quantifies tumor heterogeneity and identifies microsatellite instability in colon cancer.

BACKGROUND AND OBJECTIVES: Deep learning utilizing convolutional neural networks (CNNs) applied to hematoxylin & eosin (H&E)-stained slides numerically encodes histomorphological tumor features. Tumor heterogeneity is an emerging biomarker in colon cancer that is, captured by these features, whereas microsatellite instability (MSI) is an established biomarker traditionally assessed by immunohistochemistry or polymerase chain reaction. METHODS: H&E-stained slides from The Cancer Genome Atlas (TCGA) colon cohort are passed through the CNN. Resulting imaging features are used to cluster morphologically similar slide regions. Tile-level pairwise similarities are calculated and used to generate a tumor heterogeneity score (THS). Patient-level THS is then correlated with TCGA-reported biomarkers, including MSI-status. RESULTS: H&E-stained images from 313 patients generated 534 771 tiles. Deep learning automatically identified and annotated cells by type and clustered morphologically similar slide regions. MSI-high tumors demonstrated significantly higher THS than MSS/MSI-low (p < 0.001). THS was higher in MLH1-silent versus non-silent tumors (p < 0.001). The sequencing derived MSIsensor score also correlated with THS (r = 0.51, p < 0.0001). CONCLUSIONS: Deep learning provides spatially resolved visualization of imaging-derived biomarkers and automated quantification of tumor heterogeneity. Our novel THS correlates with MSI-status, indicating that with expanded training sets, translational tools could be developed that predict MSI-status using H&E-stained images alone.

Significance of Lymphovascular Space Invasion by the Sarcomatous Component in Uterine Carcinosarcoma.

OBJECTIVE: The aim of this study was to examine the significance of lymphovascular space invasion (LVSI) with a sarcomatous component on the tumor characteristics and clinical outcomes of women with uterine carcinosarcoma (UCS). METHODS: This was a secondary analysis of a prior multicenter retrospective study that examined women with stage I-IV UCS who underwent primary hysterectomy. Archived histopathology slides were reviewed and LVSI was scored as follows: LVSI with a carcinomatous component alone (LVSI-carcinoma; n = 375, 76.8%) or LVSI containing a sarcomatous component with or without a carcinomatous component (LVSI-sarcoma; n = 113, 23.2%). Qualitative metrics of LVSI were correlated to clinicopathological factors and survival outcome. RESULTS: Tumors in the LVSI-sarcoma group were more likely to have sarcoma dominance (82.1 vs. 26.4%) heterologous sarcomatous component (51.3 vs. 37.9%), low-grade carcinoma (42.5 vs. 22.4%), and large tumor size (81.0 vs. 70.2%) in the primary tumor site compared with tumors in the LVSI-carcinoma group (all p < 0.05). On multivariate analysis, LVSI-sarcoma was independently associated with decreased progression-free survival (5-year rates: 34.9 vs. 40.8%, adjusted hazard ratio [HR] 1.84, 95% confidence interval [CI] 1.36-2.50, p < 0.001), and cause-specific survival (5-year rates: 41.8 vs. 55.9%, adjusted HR 1.95, 95% CI 1.39-2.75, p < 0.001) compared with LVSI-carcinoma. Postoperative radiotherapy for women with LVSI-sarcoma had a higher reduction rate of recurrence/progression of disease (54% reduction, p = 0.04) compared with postoperative radiotherapy for women with LVSI-carcinoma (26% reduction, p = 0.08). CONCLUSION: In UCS, the presence of a sarcomatous component in LVSI is particularly prevalent when a tumor has sarcoma dominance. Our study suggests that LVSI containing a sarcomatous component may be a predictor of decreased survival for women with UCS.

Proposal for a Risk-Based Categorization of Uterine Carcinosarcoma.

PURPOSE: To propose a categorization model of uterine carcinosarcoma (UCS) based on tumor cell types (carcinoma and sarcoma) and sarcoma dominance. METHODS: This secondary analysis of a prior multicenter retrospective study examined 889 cases of UCS with available histologic evaluation. Based on survival outcome, cases were clustered into three groups: low-grade carcinoma with nondominant homologous sarcoma [type A, n = 96 (10.8%)], (1) low-grade carcinoma with heterologous sarcoma or any sarcoma dominance and (2) high-grade carcinoma with nondominant homologous sarcoma [type B, n = 412 (46.3%)], and high-grade carcinoma with heterologous sarcoma or any sarcoma dominance [type C, n = 381 (42.9%)]. Tumor characteristics and outcome were examined based on the categorization. RESULTS: Women in type C category were more likely to be older, obese, and Caucasian, whereas those in type A category were younger, less obese, Asian, and nulligravid (all P < 0.01). Type C tumors were more likely to have metastatic implants, large tumor size, lymphovascular space invasion with sarcoma cells, and higher lymph node ratio, whereas type A tumors were more likely to be early-stage disease and small (all P < 0.05). On multivariate analysis, tumor categorization was independently associated with progression-free survival (5-year rates: 70.1% for type A, 48.3% for type B, and 35.9% for type C, adjusted P < 0.01) and cause-specific survival (5-year rates: 82.8% for type A, 63.0% for type B, and 47.1% for type C, adjusted P < 0.01). CONCLUSION: Characteristic differences in clinicopathological factors and outcomes in UCS imply that different underlying etiologies and biological behaviors may be present, supporting a new classification system.

Survival outcome of stage I ovarian clear cell carcinoma with lympho-vascular space invasion.

BACKGROUND: The clinical impact of lympho-vascular space invasion (LVSI) in early-stage ovarian clear cell carcinoma (OCCC) is not well understood. Given the distinct tumor biology and survival patterns of OCCC, the significance of LVSI on survival outcome and treatment response was examined in OCCC. METHODS: A multicenter study was conducted to examine stage IA-IC3 OCCC cases that underwent primary surgical staging including lymphadenectomy. LVSI status was determined from archived histopathology slides, correlated with clinico-pathological results, chemotherapy patterns, and survival outcomes. RESULTS: LVSI was observed in 47 (20.3%) among 232 cases. In univariate analysis, LVSI was associated with older age (p=0.042), large tumor size (p=0.048), and stage IC (p=0.035). In survival analysis, LVSI was associated with decreased disease-free survival (DFS, 5-year rate, 70.6% versus 92.1%, p=0.0004) and overall survival (OS, 78.8% versus 93.3%, p=0.008) on univariate analysis. After controlling for age, tumor size, stage, and chemotherapy use, LVSI remained an independent prognostic factor for decreased survival outcomes (DFS, hazard ratio [HR] 4.35, 95% confidence interval [CI] 1.73-10.9, p=0.002; and OS, HR 4.73, 95%CI 1.60-14.0, p=0.015). Among 210 cases who received postoperative chemotherapy, while regimen type did not impact survival outcome regardless of LVSI status (DFS, p=0.63), the number of administered cycles showed a survival benefit towards ≥6cycles for patients with LVSI-positive tumors (DFS, p=0.009; and OS, p=0.016). CONCLUSION: LVSI is an important marker to predict survival outcome of stage I OCCC. Regardless of chemotherapy type, patients with stage I OCCC showing LVSI may benefit from receiving postoperative chemotherapy.

Estrogen receptor expression and increased risk of lymphovascular space invasion in high-grade serous ovarian carcinoma.

OBJECTIVE: Recent studies have demonstrated that lymphovascular space invasion (LVSI) is associated with increased risk of hematogenous and lymphatic metastasis and poor clinical outcome of women with epithelial ovarian cancer. Given the suspected role of estrogen in promoting ovarian cancer metastasis, we examined potential links between estrogen receptor and LVSI in high-grade serous ovarian carcinoma. METHODS: Tumoral expression of ER, PR, p53, MDR1, EGFR, HER2, DNA ploidy, and S-phase fraction was examined for 121 cases of stage I-IV high-grade serous ovarian carcinoma samples obtained at primary cytoreductive surgery. Biomarker expression was correlated to LVSI and survival outcomes. RESULTS: LVSI was observed in 101 (83.5%) of all cases. Immunohistochemistry of tested biomarkers showed ER (86.7%) to be the most commonly expressed followed by p53 (71.4%), HER2 (68.3%), EGFR (52.1%), MDR-1 (14.3%), and PR (8.9%). ER expression was positively correlated to PR expression (r=0.31, p=0.001). LVSI was only correlated with ER (odds ratio 6.27, 95%CI 1.93-20.4, p=0.002) but not with other biomarkers. In multivariate analysis, ER remained significantly associated with LVSI (p=0.039). LVSI remained a significant prognostic factor for decreased progression-free survival (HR 3.01, 95%CI 1.54-5.88, p=0.001) and overall survival (HR 2.69, 95%CI 1.18-6.23, p=0.021) while ER-expression did not remain as a significant variable in multivariate analysis. CONCLUSION: Our data demonstrated that estrogen receptor was positively correlated with LVSI that was an independent prognostic indicator of poor survival outcomes of high-grade serous ovarian carcinoma. This study emphasizes the importance of estrogen pathway in promoting lymphatic or vascular spread of high-grade serous ovarian carcinoma.

SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images.

BACKGROUND: Deep learning has revolutionized digital pathology, allowing automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs are broken into smaller images called tiles, and a neural network encodes each tile. Many recent works use supervised attention-based models to aggregate tile-level features into a slide-level representation, which is then used for downstream analysis. Training supervised attention-based models is computationally intensive, architecture optimization of the attention module is non-trivial, and labeled data are not always available. Therefore, we developed an unsupervised and fast approach called SAMPLER to generate slide-level representations. METHODS: Slide-level representations of SAMPLER are generated by encoding the cumulative distribution functions of multiscale tile-level features. To assess effectiveness of SAMPLER, slide-level representations of breast carcinoma (BRCA), non-small cell lung carcinoma (NSCLC), and renal cell carcinoma (RCC) WSIs of The Cancer Genome Atlas (TCGA) were used to train separate classifiers distinguishing tumor subtypes in FFPE and frozen WSIs. In addition, BRCA and NSCLC classifiers were externally validated on frozen WSIs. Moreover, SAMPLER's attention maps identify regions of interest, which were evaluated by a pathologist. To determine time efficiency of SAMPLER, we compared runtime of SAMPLER with two attention-based models. SAMPLER concepts were used to improve the design of a context-aware multi-head attention model (context-MHA). FINDINGS: SAMPLER-based classifiers were comparable to state-of-the-art attention deep learning models to distinguish subtypes of BRCA (AUC = 0.911 ± 0.029), NSCLC (AUC = 0.940 ± 0.018), and RCC (AUC = 0.987 ± 0.006) on FFPE WSIs (internal test sets). However, training SAMLER-based classifiers was >100 times faster. SAMPLER models successfully distinguished tumor subtypes on both internal and external test sets of frozen WSIs. Histopathological review confirmed that SAMPLER-identified high attention tiles contained subtype-specific morphological features. The improved context-MHA distinguished subtypes of BRCA and RCC (BRCA-AUC = 0.921 ± 0.027, RCC-AUC = 0.988 ± 0.010) with increased accuracy on internal test FFPE WSIs. INTERPRETATION: Our unsupervised statistical approach is fast and effective for analyzing WSIs, with greatly improved scalability over attention-based deep learning methods. The high accuracy of SAMPLER-based classifiers and interpretable attention maps suggest that SAMPLER successfully encodes the distinct morphologies within WSIs and will be applicable to general histology image analysis problems. FUNDING: This study was supported by the National Cancer Institute (Grant No. R01CA230031 and P30CA034196).

Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma.

Resistance of BRAF-mutant melanomas to targeted therapy arises from the ability of cells to enter a persister state, evade treatment with relative dormancy, and repopulate the tumor when reactivated. Using spatial transcriptomics in patient derived xenograft models, we capture clonal lineage evolution during treatment, finding the persister state to show increased oxidative phosphorylation, decreased proliferation, and increased invasive capacity, with central-to-peripheral gradients. Phylogenetic tracing identifies intrinsic- and acquired-resistance mechanisms (e.g. dual specific phosphatases, Reticulon-4, CDK2) and suggests specific temporal windows of potential therapeutic efficacy. Using deep learning to analyze histopathological slides, we find morphological features of specific cell states, demonstrating that juxtaposition of transcriptomics and histology data enables identification of phenotypically-distinct populations using imaging data alone. In summary, we define state change and lineage selection during melanoma treatment with spatiotemporal resolution, elucidating how choice and timing of therapeutic agents will impact the ability to eradicate resistant clones. Statement of Significance: Tumor evolution is accelerated by application of anti-cancer therapy, resulting in clonal expansions leading to dormancy and subsequently resistance, but the dynamics of this process are incompletely understood. Tracking clonal progression during treatment, we identify conserved, global transcriptional changes and local clone-clone and spatial patterns underlying the emergence of resistance.

SAMPLER: Empirical distribution representations for rapid analysis of whole slide tissue images.

Deep learning has revolutionized digital pathology, allowing for automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. In such analyses, WSIs are typically broken into smaller images called tiles, and a neural network backbone encodes each tile in a feature space. Many recent works have applied attention based deep learning models to aggregate tile-level features into a slide-level representation, which is then used for slide-level prediction tasks. However, training attention models is computationally intensive, necessitating hyperparameter optimization and specialized training procedures. Here, we propose SAMPLER, a fully statistical approach to generate efficient and informative WSI representations by encoding the empirical cumulative distribution functions (CDFs) of multiscale tile features. We demonstrate that SAMPLER-based classifiers are as accurate or better than state-of-the-art fully deep learning attention models for classification tasks including distinction of: subtypes of breast carcinoma (BRCA: AUC=0.911 ± 0.029); subtypes of non-small cell lung carcinoma (NSCLC: AUC=0.940±0.018); and subtypes of renal cell carcinoma (RCC: AUC=0.987±0.006). A major advantage of the SAMPLER representation is that predictive models are >100X faster compared to attention models. Histopathological review confirms that SAMPLER-identified high attention tiles contain tumor morphological features specific to the tumor type, while low attention tiles contain fibrous stroma, blood, or tissue folding artifacts. We further apply SAMPLER concepts to improve the design of attention-based neural networks, yielding a context aware multi-head attention model with increased accuracy for subtype classification within BRCA and RCC (BRCA: AUC=0.921±0.027, and RCC: AUC=0.988±0.010). Finally, we provide theoretical results identifying sufficient conditions for which SAMPLER is optimal. SAMPLER is a fast and effective approach for analyzing WSIs, with greatly improved scalability over attention methods to benefit digital pathology analysis.

Deep learning features encode interpretable morphologies within histological images.

Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&E-derived CNN features (the feature weights in the final layer of a transfer-learning-based architecture). While many studies have incorporated CNN features into predictive models, there has been little empirical study of their properties. We show such features can be construed as abstract morphological genes ("mones") with strong independent associations to biological phenotypes. Many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC = [Formula: see text] for each class prediction), and linear classifiers are also highly effective for universal tumor detection (AUC = [Formula: see text]). This linearity provides evidence that individual mones or correlated mone clusters may be associated with interpretable histopathological features or other patient characteristics. In particular, the statistical similarity of mones to gene expression values allows integrative mone analysis via expression-based bioinformatics approaches. We observe strong correlations between individual mones and individual gene expression values, notably mones associated with collagen gene expression in ovarian cancer. Mone-expression comparisons also indicate that immunoglobulin expression can be identified using mones in colon adenocarcinoma and that immune activity can be identified across multiple cancer types, and we verify these findings by expert histopathological review. Our work demonstrates that mones provide a morphological H&E decomposition that can be effectively associated with diverse phenotypes, analogous to the interpretability of transcription via gene expression values. Our work also demonstrates mones can be interpreted without using a classifier as a proxy.

Cytologic processing of ureteral microbiopsies is associated with higher sensitivity for detection of urothelial carcinoma compared with conventional biopsy processing.

INTRODUCTION: Because of the high rates of false-negative or nondiagnostic ureteral Piranha microbiopsies associated with low cellularity, we assessed the effect of processing these using cytology. MATERIALS AND METHODS: We included 2 groups of 44 consecutive microbiopsies processed from formalin as a standard surgical biopsy and 22 processed by cytology. All samples were from the ureter or renal pelvis or calyx. The cytology samples were collected in alcohol-based media and were prepared with a Cellient cell block only (n = 9) or with a Cellient cell block for the visible particles, together with ThinPrep, to capture the remaining desquamated cells (n = 13). RESULTS: Malignancy was diagnosed in 5 of 44 conventionally processed microbiopsies (11%) compared with 14 of 22 cytologically processed microbiopsies (64%; P < 0.001), including 1 case with invasion. Nineteen site-matched biopsies from 2 patients had undergone both cytologic and surgical processing, with 8 of 8 cytologically processed biopsies diagnosed as malignant. None of the 11 surgically processed biopsies from the same patients matched for site were diagnosed as malignant. Of the 11, 2 (18%) were suspicious for high-grade urothelial carcinoma and 6 (55%) were considered atypical. Increased sensitivity from cytologic processing appears related to increased cell recovery; large numbers of well-preserved urothelial cells were identified in the ThinPrep (range, 1000-25,000 cells/slide), and a nonsignificant trend was found toward increased urothelium (defined as >200 cells/profile) in the Cellient cell blocks (14 of 22 [64%]) compared with the histologic biopsies (17 of 44 [39%]; P = 0.070). CONCLUSIONS: Cytologic processing of ureteral microbiopsies showed superior sensitivity for detecting high-grade urothelial carcinoma, apparently owing to the increased cellular recovery.

Change in prostate cancer grade over time in men followed expectantly for stage T1c disease.

PURPOSE: We assessed whether the Gleason grade changes in men followed expectantly with nonpalpable prostate cancer diagnosed on needle biopsy (stage T1c). MATERIALS AND METHODS: We studied 241 men with stage T1c prostate cancer who were treated expectantly with repeat yearly needle biopsy sampling to assess for cancer progression. Following the initial cancer diagnosis all men had at least 1 other biopsy demonstrating cancer. RESULTS: Median patient age was 66 years. The number of biopsies showing cancer over time was 2 in 119 (49.4%), 3 in 74 (30.7%), 4 in 33 (13.7%) and 5 or greater in 15 (6.2%). The average followup for those without progression was 32.3 months. Of 241 cases 45 (18.7%) showed a significant change in grade from Gleason score 6 or less to Gleason score 7 or greater (Gleason score 7 in 41 cases, Gleason score 8 in 4 cases). Of 45 (53.5%) cases 24 that showed progression did so within 24 months of diagnosis. CONCLUSIONS: Within the first 3 years after diagnosis of Gleason score 6 prostate cancer, there is a relatively low risk of grade progression. Within the first 3 years, our data suggest that in most cases tumor grade did not evolve but rather that the higher grade component was not initially sampled since most grade changes occurred relatively soon after biopsy. Grade progression does appear to occur in some men with long-term followup who had multiple biopsies showing Gleason score 6 followed by higher grade cancer.

Characterizing sarcoma dominance pattern in uterine carcinosarcoma: Homologous versus heterologous element.

OBJECTIVE: To examine significance of sarcoma dominance (SD) patterns in uterine carcinosarcoma (UCS). METHODS: This is a secondary analysis of multicenter retrospective study examining women with stages I-IV UCS who underwent primary surgery. SD was defined as >50% of sarcoma component in uterine tumor. SD patterns were grouped as homologous sarcoma without SD (homo/non-dominance, n = 351), heterologous sarcoma without SD (hetero/non-dominance, n = 174), homologous sarcoma with SD (homo/dominance, n = 175), and heterologous sarcoma with SD (hetero/dominance, n = 189), and correlated to tumor characteristics and survival. RESULTS: SD patterns were significantly associated with age, body habitus, carcinoma type, tumor size, depth of myometrial invasion, and nodal metastasis (all, P < 0.05). On univariate analysis, SD was associated with decreased progression-free survival (PFS) and cause-specific survival (CSS) in homologous cases (both, P < 0.05) but not in heterologous cases. On multivariate models, both homologous and heterologous SD patterns remained independent prognostic factors for decreased PFS (adjusted-hazard ratio [HR] ranges: homo/dominance 1.35-1.69, and hetero/dominance 1.47-1.64) and CSS (adjusted-HR ranges: 1.52-1.84 and 1.66-1.81, respectively) compared to homo/non-dominance (all, P < 0.05). Among stage I-III disease, when tumors had SD, adding radiotherapy to chemotherapy was significantly associated with improved PFS (adjusted-HR: homo/dominance 0.49, and hetero/dominance 0.45) and CSS (0.36 and 0.31, respectively) compared to chemotherapy alone (all, P < 0.05); contrary, this association was not observed with absence of SD (all, P > 0.05). CONCLUSION: In UCS, SD impacts survival in homologous but not in heterologous type. Regardless of sarcoma types, SD was associated with decreased survival in UCS; adding radiotherapy to chemotherapy may be an effective postoperative strategy.

Sessile serrated adenomas with low- and high-grade dysplasia and early carcinomas: an immunohistochemical study of serrated lesions "caught in the act".

Sessile serrated adenomas (SSAs) show serrations typical of hyperplastic polyps but display architectural differences and lack traditional dysplasia. SSAs with foci of low- (LGD) or high-grade dysplasia (HGD) or early invasive carcinoma are seldom biopsied and, thus, are not well studied. Immunohistochemical analysis for MLH1, MSH2, MSH6, and PMS2 (mismatch repair gene products) was performed on colon biopsy specimens from 11 patients (age range, 54-87 years; 4 men and 7 women) showing SSA with LGD (n = 1), HGD (n = 5), or focal invasive carcinoma (n = 5). All 11 cases showed intact nuclear staining for MSH2 and MSH6 in the SSA component; in foci of LGD, HGD, or carcinoma; and in background normal mucosa. In contrast, there was tandem loss of MLH1 and PMS2 in zones of LGD (1/1) or HGD (3/5) and early carcinoma (2/4; with concordant loss in associated HGD) but retention in SSA areas (11/11) and normal mucosa (11/11). No patient was known to have hereditary nonpolyposis colorectal cancer/Lynch syndrome. This study offers additional strong evidence that SSA is truly a precursor to at least a subset of sporadic microsatellite-unstable colorectal cancer.

The c-myc x E2F-1/p21 interactive gene expression index augments cytomorphologic diagnosis of lung cancer in fine-needle aspirate specimens.

Morphological analysis of cytologic samples obtained by fine-needle aspirate (FNA) or bronchoscopy is an important method for diagnosing bronchogenic carcinoma. However, this approach has only about 65 to 80% diagnostic sensitivity. Based on previous studies, the c-myc x E2F-1/p21WAF1/CIP1 (p21 hereafter) gene expression index is highly sensitive and specific for distinguishing normal from malignant bronchial epithelial tissues. In an effort to improve sensitivity of diagnosing lung cancer in cytologic specimens, we used Standardized Reverse Transcriptase Polymerase Chain Reaction (StaRT-PCR) to measure the c-myc x E2F-1/p21 index in cDNA samples from 14 normal lung samples (6 normal lung parenchyma and 8 normal bronchial epithelial cell [NBEC] biopsies), and 16 FNA biopsies from 14 suspected tumors. Based on cytomorphologic criteria, 11 of the 14 suspected tumors were diagnosed as bronchogenic carcinoma and three specimens were non-diagnostic. Subsequent biopsy samples confirmed that the three non-diagnostic samples were derived from lung carcinomas. The index value for each bronchogenic carcinoma was above a cut-off value of 7000 and the index value of all but one normal sample was below 7000. Thus the c-myc x E2F-1/p21 index may augment cytomorphologic diagnosis of bronchogenic carcinoma biopsy samples, particularly those considered non-diagnostic by cytomorphologic criteria.

Clinical characteristics and outcomes of uterine tumors resembling ovarian sex-cord tumors (UTROSCT): a systematic review of literature.

Uterine tumor resembling ovarian sex-cord tumors (UTROSCT) is an extremely rare type of uterine tumor, and its clinical characteristics are not fully understood. A systematic literature search was conducted in PubMed and MEDLINE using the keywords, "uterine tumors resembling ovarian sex cord tumors", limited to case reports. Clinico-pathological characteristics and survival data were abstracted and evaluated for the analysis. Among 43 cases reporting UTROSCT, Type I (endometrial stromal tumors with sex cord-like elements, ESTSCLE) and Type II (classic UTROSCT) were reported in 5 (11.6%) and 17 (39.5%), respectively, and nearly half of reported UTROSCT did not subcategorize the histology pattern into Type I or II (unspecified, n=21, 48.8%). Mean age was 52.2. The two most common symptoms were postmenopausal vaginal bleeding (44.2%) and abnormal menstruation (39.5%). The majority underwent total hysterectomy with adnexectomy (65.1%) followed by hysterectomy alone (18.6%) and tumor resection alone (14.0%). Mean tumor size was 6.2cm, and extra-uterine spread was seen in 7.0%. By immunohistochemistry, calretinin expression was significantly correlated with CAM5.2, inhibin, and progesterone receptor expression (all, p<0.05). In survival analysis, disease-free survival (DFS) rates for all 43 cases at 1, 2, and 5 years for all cases were 97.0%, 92.7%, and 69.7%, respectively. Among recurrent cases, median time to recur was 24 months (range 9-48). Decreased DFS was significantly associated with pelvic pain (2-year rate, 81.8% versus 94.7%, p=0.006), histology subcategory (Type I versus II, 23.8% versus 100%, p=0.006), tumor size ≥10cm (75.0% versus 100%, p=0.046), cervical/extra-uterine metastasis (46.7% versus 100%, p=0.024), and lymphovascular space involvement (50% versus 100%, p=0.002). Treatment patterns were not statistically associated with DFS (hysterectomy, p=0.28; and adnexectomy, p=0.38). When histology patterns were examined, Type II disease was associated with less aggressive tumor behavior when compared to Type I disease: extra-uterine spread (Type I versus II, 40% versus 5.9%, p=0.007) and lymphovascular space invasion (50% versus 6.7%, p=0.012). Among 17 cases of Type II disease, disease recurrence was reported in 1 (5.9%) case at 3 years after the initial treatment. In conclusion, our study showed that UTROSCT was often not subcategorized. Because classic UTROSCT has a distinct clinical outcome and characteristic histological patterns when compared to ESTSCLE, distinguishing UTROSCT from ESTSCLE is an integral component of the diagnosis. While classic UTROSCT typically has a favorable prognosis, it has been known to develop a late recurrence. If risk factors for recurrence are absent, both hysterectomy and mass resection alone are possible options for management.

Effect of lymphovascular space invasion on survival of stage I epithelial ovarian cancer.

OBJECTIVE: To evaluate the effect of lymphovascular space invasion on survival of patients with early-stage epithelial ovarian cancer. METHODS: A multicenter retrospective study was conducted for patients with stage IA-C epithelial ovarian cancer who underwent primary comprehensive surgery including lymphadenectomy. Histopathology slides for ovarian tumors were examined by gynecologic pathologists for the presence or absence of lymphovascular space invasion. Survival analysis was performed examining tumoral factors. RESULTS: A total of 434 patients were included in the analysis. Lymphovascular space invasion was detected in 76 (17.5%) patients associated with histology (P=.042) and stage (P=.044). Lymphovascular space invasion was significantly associated with decreased survival outcomes (disease-free survival, 5-year rate 78.4% compared with 90.7%, P=.024 and overall survival, 84.9% compared with 93.2%, P=.031) in univariate analysis. In multivariate analysis, lymphovascular space invasion did not remain a significant variable for disease-free survival (hazard ratio [HR] 1.98, 95% confidence interval [CI] 0.97-3.97, P=.059) or overall survival (HR 2.41, 95% CI 0.99-5.85, P=.052). Lymphovascular space invasion was associated with increased risk of hematogenous and lymphatic metastasis (HR 4.79, 95% CI 1.75-13.2, P=.002) but not peritoneal metastasis (P=.33) in multivariate analysis. Among lymphovascular space invasion-expressing tumors, patients who received fewer than six cycles of postoperative chemotherapy had significantly poorer disease-free survival than those who received six or more cycles (HR 4.59, 95% CI 1.20-17.5, P=.015). CONCLUSION: Lymphovascular space invasion is an important histologic feature to identify a subgroup of patients with increased risk of recurrence in stage I epithelial ovarian cancer. LEVEL OF EVIDENCE: III.

Significance of lymphovascular space invasion in epithelial ovarian cancer.

While the prognostic significance of lymphovascular space invasion (LVSI) is well established in endometrial and cervical cancer, its role in ovarian cancer is not fully understood. First, a training cohort was conducted to explore whether the presence and quantity of LVSI within the ovarian tumor correlated with nodal metastasis and survival (n = 127). Next, the results of the training cohort were applied to a different study population (validation cohort, n = 93). In both cohorts, histopathology slides of epithelial ovarian cancer cases that underwent primary cytoreductive surgery including pelvic and/or aortic lymphadenectomy were examined. In a post hoc analysis, the significance of LVSI was evaluated in apparent stage I cases (n = 53). In the training cohort, the majority of patients had advanced-stage disease (82.7%). LVSI was observed in 79.5% of cases, and nodal metastasis was the strongest variable associated with the presence of LVSI (odds ratio [OR]: 7.99, 95% confidence interval [CI]: 1.98-32.1, P = 0.003) in multivariate analysis. The presence of LVSI correlated with a worsened progression-free survival on multivariate analysis (hazard ratio [HR]: 2.06, 95% CI: 1.01-4.24, P = 0.048). The significance of the presence of LVSI was reproduced in the validation cohort (majority, early stage 61.3%). In apparent stage I cases, the presence of LVSI was associated with a high negative predictive value for nodal metastasis (100%, likelihood ratio, P = 0.034) and with worsened progression-free survival (HR: 5.16, 95% CI: 1.00-26.6, P = 0.028). The presence of LVSI is an independent predictive indicator of nodal metastasis and is associated with worse clinical outcome of patients with epithelial ovarian cancer.