A clinical decision support system optimising adjuvant chemotherapy for colorectal cancers by integrating deep learning and pathological staging markers: a development and validation study.

BACKGROUND: The DoMore-v1-CRC marker was recently developed using deep learning and conventional haematoxylin and eosin-stained tissue sections, and was observed to outperform established molecular and morphological markers of patient outcome after primary colorectal cancer resection. The aim of the present study was to develop a clinical decision support system based on DoMore-v1-CRC and pathological staging markers to facilitate individualised selection of adjuvant treatment. METHODS: We estimated cancer-specific survival in subgroups formed by pathological tumour stage (pT<4 or pT4), pathological nodal stage (pN0, pN1, or pN2), number of lymph nodes sampled (≤12 or >12) if not pN2, and DoMore-v1-CRC classification (good, uncertain, or poor prognosis) in 997 patients with stage II or III colorectal cancer considered to have no residual tumour (R0) from two community-based cohorts in Norway and the UK, and used these data to define three risk groups. An external cohort of 1075 patients with stage II or III R0 colorectal cancer from the QUASAR 2 trial was used for validation; these patients were treated with single-agent capecitabine. The proposed risk stratification system was evaluated using Cox regression analysis. We similarly evaluated a risk stratification system intended to reflect current guidelines and clinical practice. The primary outcome was cancer-specific survival. FINDINGS: The new risk stratification system provided a hazard ratio of 10·71 (95% CI 6·39-17·93; p<0·0001) for high-risk versus low-risk patients and 3·06 (1·73-5·42; p=0·0001) for intermediate versus low risk in the primary analysis of the validation cohort. Estimated 3-year cancer-specific survival was 97·2% (95% CI 95·1-98·4; n=445 [41%]) for the low-risk group, 94·8% (91·7-96·7; n=339 [32%]) for the intermediate-risk group, and 77·6% (72·1-82·1; n=291 [27%]) for the high-risk group. The guideline-based risk grouping was observed to be less prognostic and informative (the low-risk group comprised only 142 [13%] of the 1075 patients). INTERPRETATION: Integrating DoMore-v1-CRC and pathological staging markers provided a clinical decision support system that risk stratifies more accurately than its constituent elements, and identifies substantially more patients with stage II and III colorectal cancer with similarly good prognosis as the low-risk group in current guidelines. Avoiding adjuvant chemotherapy in these patients might be safe, and could reduce morbidity, mortality, and treatment costs. FUNDING: The Research Council of Norway.

Deep learning for prediction of colorectal cancer outcome: a discovery and validation study.

BACKGROUND: Improved markers of prognosis are needed to stratify patients with early-stage colorectal cancer to refine selection of adjuvant therapy. The aim of the present study was to develop a biomarker of patient outcome after primary colorectal cancer resection by directly analysing scanned conventional haematoxylin and eosin stained sections using deep learning. METHODS: More than 12 000 000 image tiles from patients with a distinctly good or poor disease outcome from four cohorts were used to train a total of ten convolutional neural networks, purpose-built for classifying supersized heterogeneous images. A prognostic biomarker integrating the ten networks was determined using patients with a non-distinct outcome. The marker was tested on 920 patients with slides prepared in the UK, and then independently validated according to a predefined protocol in 1122 patients treated with single-agent capecitabine using slides prepared in Norway. All cohorts included only patients with resectable tumours, and a formalin-fixed, paraffin-embedded tumour tissue block available for analysis. The primary outcome was cancer-specific survival. FINDINGS: 828 patients from four cohorts had a distinct outcome and were used as a training cohort to obtain clear ground truth. 1645 patients had a non-distinct outcome and were used for tuning. The biomarker provided a hazard ratio for poor versus good prognosis of 3·84 (95% CI 2·72-5·43; p<0·0001) in the primary analysis of the validation cohort, and 3·04 (2·07-4·47; p<0·0001) after adjusting for established prognostic markers significant in univariable analyses of the same cohort, which were pN stage, pT stage, lymphatic invasion, and venous vascular invasion. INTERPRETATION: A clinically useful prognostic marker was developed using deep learning allied to digital scanning of conventional haematoxylin and eosin stained tumour tissue sections. The assay has been extensively evaluated in large, independent patient populations, correlates with and outperforms established molecular and morphological prognostic markers, and gives consistent results across tumour and nodal stage. The biomarker stratified stage II and III patients into sufficiently distinct prognostic groups that potentially could be used to guide selection of adjuvant treatment by avoiding therapy in very low risk groups and identifying patients who would benefit from more intensive treatment regimes. FUNDING: The Research Council of Norway.

Prognostic value of DNA ploidy and automated assessment of stroma fraction in prostate cancer.

The combination of DNA ploidy and automatically estimated stroma fraction has been shown to correlate with recurrence and cancer death in colorectal cancer. We aimed to extend this observation and evaluate the prognostic importance of this combined marker in prostate cancer. DNA ploidy status was determined by image cytometry and the stroma fraction was estimated automatically on hematoxylin and eosin stained sections in three tumor samples from each patient to account for tumor heterogeneity. The optimal threshold for low (≤56%) and high (>56%) stroma fraction was identified in a discovery cohort (n = 253). The combined marker was validated in an independent patient cohort (n = 259) with biochemical recurrence as endpoint. The combined marker predicted biochemical recurrence independently in the validation cohort. Multivariable analysis showed that the highest risk of recurrence was observed for patients with samples that had both non-diploid ploidy status and a high stroma fraction (hazard ratio: 2.51, 95% confidence interval: 1.18-5.34). In conclusion, we suggest the combination of DNA ploidy and automatically estimated stroma fraction as a prognostic marker for the risk stratification of prostate cancer patients. It may also be a potential generic marker as concurrent results have been described in colorectal cancer.

Prognostic value of mitotic checkpoint protein BUB3, cyclin B1, and pituitary tumor-transforming 1 expression in prostate cancer.

The mitotic checkpoint protein BUB3, cyclin B1 (CCNB1) and pituitary tumor-transforming 1 (PTTG1) regulates cell division, and are sparsely studied in prostate cancer. Deregulation of these genes can lead to genomic instability, a characteristic of more aggressive tumors. We aimed to determine the expression levels of BUB3, CCNB1, and PTTG1 as potential prognostic markers of recurrence after radical prostatectomy. Protein levels were determined by immunohistochemistry on three formalin-fixed paraffin-embedded tissue sections from each of the 253 patients treated with radical prostatectomy. Immunohistochemistry scores were obtained by automated image analysis for CCNB1 and PTTG1. Recurrence, defined as locoregional recurrence, distant metastasis or death from prostate cancer, was used as endpoint for survival analysis. Tumors having both positive and negative tumor areas for cytoplasmic BUB3 (30%), CCNB1 (28%), or PTTG1 (35%) were considered heterogeneous. Patients with ≥1 positive tumor area had significantly increased risk of disease recurrence in univariable analysis compared with patients where all tumor areas were negative for cytoplasmic BUB3 (hazard ratio [HR] = 2.18, 95% confidence interval [CI] 1.41-3.36), CCNB1 (HR = 2.98, 95% CI 1.93-4.61) and PTTG1 (HR = 1.91, 95% CI 1.23-2.97). Combining the scores of cytoplasmic BUB3 and CCNB1 improved risk stratification when integrated with the Cancer of the Prostate Risk Assessment post-Surgical (CAPRA-S) score (difference in concordance index = 0.024, 95% CI 0.001-0.05). In analysis of multiple tumor areas, prognostic value was observed for cytoplasmic BUB3, CCNB1, and PTTG1.

Chromatin organisation and cancer prognosis: a pan-cancer study.

BACKGROUND: Chromatin organisation affects gene expression and regional mutation frequencies and contributes to carcinogenesis. Aberrant organisation of DNA has been correlated with cancer prognosis in analyses of the chromatin component of tumour cell nuclei using image texture analysis. As yet, the methodology has not been sufficiently validated to permit its clinical application. We aimed to define and validate a novel prognostic biomarker for the automatic detection of heterogeneous chromatin organisation. METHODS: Machine learning algorithms analysed the chromatin organisation in 461 000 images of tumour cell nuclei stained for DNA from 390 patients (discovery cohort) treated for stage I or II colorectal cancer at the Aker University Hospital (Oslo, Norway). The resulting marker of chromatin heterogeneity, termed Nucleotyping, was subsequently independently validated in six patient cohorts: 442 patients with stage I or II colorectal cancer in the Gloucester Colorectal Cancer Study (UK); 391 patients with stage II colorectal cancer in the QUASAR 2 trial; 246 patients with stage I ovarian carcinoma; 354 patients with uterine sarcoma; 307 patients with prostate carcinoma; and 791 patients with endometrial carcinoma. The primary outcome was cancer-specific survival. FINDINGS: In all patient cohorts, patients with chromatin heterogeneous tumours had worse cancer-specific survival than patients with chromatin homogeneous tumours (univariable analysis hazard ratio [HR] 1·7, 95% CI 1·2-2·5, in the discovery cohort; 1·8, 1·0-3·0, in the Gloucester validation cohort; 2·2, 1·1-4·5, in the QUASAR 2 validation cohort; 3·1, 1·9-5·0, in the ovarian carcinoma cohort; 2·5, 1·8-3·4, in the uterine sarcoma cohort; 2·3, 1·2-4·6, in the prostate carcinoma cohort; and 4·3, 2·8-6·8, in the endometrial carcinoma cohort). After adjusting for established prognostic patient characteristics in multivariable analyses, Nucleotyping was prognostic in all cohorts except for the prostate carcinoma cohort (HR 1·7, 95% CI 1·1-2·5, in the discovery cohort; 1·9, 1·1-3·2, in the Gloucester validation cohort; 2·6, 1·2-5·6, in the QUASAR 2 cohort; 1·8, 1·1-3·0, for ovarian carcinoma; 1·6, 1·0-2·4, for uterine sarcoma; 1·43, 0·68-2·99, for prostate carcinoma; and 1·9, 1·1-3·1, for endometrial carcinoma). Chromatin heterogeneity was a significant predictor of cancer-specific survival in microsatellite unstable (HR 2·9, 95% CI 1·0-8·4) and microsatellite stable (1·8, 1·2-2·7) stage II colorectal cancer, but microsatellite instability was not a significant predictor of outcome in chromatin homogeneous (1·3, 0·7-2·4) or chromatin heterogeneous (0·8, 0·3-2·0) stage II colorectal cancer. INTERPRETATION: The consistent prognostic prediction of Nucleotyping in different biological and technical circumstances suggests that the marker of chromatin heterogeneity can be reliably assessed in routine clinical practice and could be used to objectively assist decision making in a range of clinical settings. An immediate application would be to identify high-risk patients with stage II colorectal cancer who might have greater absolute benefit from adjuvant chemotherapy. Clinical trials are warranted to evaluate the survival benefit and cost-effectiveness of using Nucleotyping to guide treatment decisions in multiple clinical settings. FUNDING: The Research Council of Norway, the South-Eastern Norway Regional Health Authority, the National Institute for Health Research, and the Wellcome Trust.

Tumour heterogeneity poses a significant challenge to cancer biomarker research.

BACKGROUND: The high degree of genomic diversity in cancer represents a challenge for identifying objective prognostic markers. We aimed to examine the extent of tumour heterogeneity and its effect on the evaluation of a selected prognostic marker using prostate cancer as a model. METHODS: We assessed Gleason Score (GS), DNA ploidy status and phosphatase and tensin homologue (PTEN) expression in radical prostatectomy specimens (RP) from 304 patients followed for a median of 10 years (interquartile range 6-12). GS was assessed for every tumour-containing block and DNA ploidy for a median of four samples for each RP. In a subgroup of 40 patients we assessed DNA ploidy and PTEN status in every tumour-containing block. In 102 patients assigned to active surveillance (AS), GS and DNA ploidy were studied in needle biopsies. RESULTS: Extensive heterogeneity was observed for GS (89% of the patients) and DNA ploidy (40% of the patients) in the cohort, and DNA ploidy (60% of the patients) and PTEN expression (75% of the patients) in the subgroup. DNA ploidy was a significant prognostic marker when heterogeneity was taken into consideration. In the AS cohort we found heterogeneity in GS (24%) and in DNA ploidy (25%) specimens. CONCLUSIONS: Multi-sample analysis should be performed to support clinical treatment decisions.

Chromatin changes predict recurrence after radical prostatectomy.

BACKGROUND: Pathological evaluations give the best prognostic markers for prostate cancer patients after radical prostatectomy, but the observer variance is substantial. These risk assessments should be supported and supplemented by objective methods for identifying patients at increased risk of recurrence. Markers of epigenetic aberrations have shown promising results in several cancer types and can be assessed by automatic analysis of chromatin organisation in tumour cell nuclei. METHODS: A consecutive series of 317 prostate cancer patients treated with radical prostatectomy at a national hospital between 1987 and 2005 were followed for a median of 10 years (interquartile range, 7-14). On average three tumour block samples from each patient were included to account for tumour heterogeneity. We developed a novel marker, termed Nucleotyping, based on automatic assessment of disordered chromatin organisation, and validated its ability to predict recurrence after radical prostatectomy. RESULTS: Nucleotyping predicted recurrence with a hazard ratio (HR) of 3.3 (95% confidence interval (CI), 2.1-5.1). With adjustment for clinical and pathological characteristics, the HR was 2.5 (95% CI, 1.5-4.1). An updated stratification into three risk groups significantly improved the concordance with patient outcome compared with a state-of-the-art risk-stratification tool (P<0.001). The prognostic impact was most evident for the patients who were high-risk by clinical and pathological characteristics and for patients with Gleason score 7. CONCLUSION: A novel assessment of epigenetic aberrations was capable of improving risk stratification after radical prostatectomy.

Computerized image analysis of the Ki-67 proliferation index in mantle cell lymphoma.

AIMS: Manual counting of the fraction of Ki-67-positive cells (the Ki-67 index) in 1000 tumour cells is considered the 'gold standard' to predict prognosis in mantle cell lymphoma (MCL). This time-consuming method is replaced by the faster, but less accurate, semiquantitative estimation in routine practice. The aim of this study was to investigate the use of computerized image analysis software for scoring of Ki-67 in MCL. METHODS AND RESULTS: We developed an automated method for determining the Ki-67 index by computerized image analysis and tested it using a cohort of 62 MCL patients. The data were compared to Ki-67 scores obtained by semiquantitative estimation and image-based manual counting. When using the Ki-67 index as a continuous parameter, both image-based manual counting and computerized image analysis were related inversely to survival (P = 0.020 and P = 0.025, respectively). Ki-67 index obtained by semiquantitative estimation was not associated significantly with survival (P = 0.093). The results were validated in a second patient cohort with similar results. CONCLUSION: Computerized image analysis of the Ki-67 index in MCL is an attractive alternative to semiquantitative estimation and can be introduced easily in a routine diagnostic setting for risk stratification in MCL.

Deep learning for automated scoring of immunohistochemically stained tumour tissue sections - Validation across tumour types based on patient outcomes.

We aimed to develop deep learning (DL) models to detect protein expression in immunohistochemically (IHC) stained tissue-sections, and to compare their accuracy and performance with manually scored clinically relevant proteins in common cancer types. Five cancer patient cohorts (colon, two prostate, breast, and endometrial) were included. We developed separate DL models for scoring IHC-stained tissue-sections with nuclear, cytoplasmic, and membranous staining patterns. For training, we used images with annotations of cells with positive and negative staining from the colon cohort stained for Ki-67 and PMS2 (nuclear model), the prostate cohort 1 stained for PTEN (cytoplasmic model) and ß-catenin (membranous model). The nuclear DL model was validated for MSH6 in the colon, MSH6 and PMS2 in the endometrium, Ki-67 and CyclinB1 in prostate, and oestrogen and progesterone receptors in the breast cancer cohorts. The cytoplasmic DL model was validated for PTEN and Mapre2, and the membranous DL model for CD44 and Flotillin1, all in prostate cohorts. When comparing the results of manual and DL scores in the validation sets, using manual scores as the ground truth, we observed an average correct classification rate of 91.5 % (76.9-98.5 %) for the nuclear model, 85.6 % (73.3-96.6 %) for the cytoplasmic model, and 78.4 % (75.5-84.3 %) for the membranous model. In survival analyses, manual and DL scores showed similar prognostic impact, with similar hazard ratios and p-values for all DL models. Our findings demonstrate that DL models offer a promising alternative to manual IHC scoring, providing efficiency and reproducibility across various data sources and markers.

Endometrial Carcinoma: Molecular Cytogenetics and Transcriptomic Profile.

Endometrial carcinomas (ECs) are histologically classified as endometrioid and nonendometrioid tumors, with each subgroup displaying different molecular profiles and clinical outcomes. Considerable biological and clinical heterogeneity exists within this scheme, however, reflecting its imperfection. We aimed to gather additional data that might help clarify the tumors' pathogenesis and contribute toward a more meaningful classification scheme. In total, 33 ECs were examined for the presence of chromosomal aberrations, genomic imbalances, pathogenic variants, microsatellite instability, and expression profiles at both gene and miRNA levels. Chromosome 1 was the most frequently rearranged chromosome, showing a gain of all or part of the long arm. Pathogenic variants were found for PTEN (53%), PDGFRA (37%), PIK3CA (34%), and KIT (31%). High microsatellite instability was identified in 15 ECs. Comparing tumors and controls, we identified 23 differentially expressed genes of known importance in carcinogenesis, 15 genes involved in innate and adaptative immune responses, and altered expression of 7 miRNAs. miR-32-5p was the most upregulated. Our series showed a high degree of heterogeneity. Tumors were well-separated from controls, but there was no clear-cut separation between endometrioid and nonendometrioid ECs. Whether this means that the current phenotypic classification is of little relevance or if one still has not detected which genomic parameters to enter into correlation analyses remains unknown.

Cytogenetic and molecular analyses of 291 gastrointestinal stromal tumors: site-specific cytogenetic evolution as evidence of pathogenetic heterogeneity.

Gastrointestinal stromal tumor (GIST) is a mesenchymal neoplasm with variable behavior. An increased understanding of the tumor pathogenesis may improve clinical decision-making. Our aim was to obtain more data about the overall chromosome aberrations and intratumor cytogenetic heterogeneity in GIST. We analyzed 306 GIST samples from 291 patients using G-banding, direct sequencing, and statistics. Clonal chromosome aberrations were found in 81% of samples, with 34% of 226 primary tumors demonstrating extensive cytogenetic heterogeneity. 135 tumors had simple (≤5 changes) and 91 had complex (>5 changes) karyotypes. The karyotypically complex tumors more often were non-gastric (P < 0.001), larger (P < 0.001), more mitotically active (P = 0.009) and had a higher risk of rupture (P < 0.001) and recurrence (P < 0.001). Significant differences between gastric and non-gastric tumors were found also in the frequency of main chromosome losses: of 14q (79% vs. 63%), 22q (38% vs. 67%), 1p (23% vs. 88%), and 15q (18% vs. 77%). Gastric PDGFRA-mutated tumors, compared with gastric KIT-mutated, had a lower incidence of 22q losses (18% vs. 43%) but a higher rate of 1p losses (42% vs. 22%). The present, largest by far karyotypic study of GISTs provides further evidence for the existence of variable pathogenetic pathways operating in these tumors' development.

Automated assessment of CD8+ T-lymphocytes and stroma fractions complement conventional staging of colorectal cancer.

BACKGROUND: Tumor development is critically dependent on the supporting stroma consisting of inflammatory cells and fibroblasts. This study intended to improve prognostic prediction for early colorectal cancer (CRC) by combined estimation of T-lymphocyte and stroma fractions with conventional markers. METHODS: In total 509 and 1041 stage II/ΙΙΙ CRC from the VICTOR and QUASAR 2 trials were included as a training set and a validation set, respectively. Intratumoral CD8+ T-lymphocytes and stroma were identified and quantified by machine-based learning on digital sections. The primary endpoint was to evaluate the prognostic value of the combined marker for time to recurrence (TTR). FINDINGS: For low-risk patients (n = 598; stage Ⅱ, and stage ΙΙΙ pT1-3 pN1 with neither lymphatic (L-) nor vascular (V-) invasion), low stroma fraction (n = 511) identified a good prognostic subgroup with 5-year TTR of 86% (95% CI 83-89), versus the high stroma subgroup TTR of 78% (HR = 1.75, 95% CI 1.05-2.92; P = 0.029). For high-risk patients (n = 394; stage ΙΙΙ pT3 pN1 L+/V+, pT4, or pN2), combined low CD8+ and high stroma fraction identified a poor prognostic subgroup (n = 34) with 5-year TTR of 29% (95% CI 17-50), versus the high CD8+ fraction and low stroma fraction subgroup (n = 138) of 64% (HR = 2.86, 95% CI 1.75-4.69; P < 0.001). INTERPRETATION: Quantification of intratumoral CD8+ T-lymphocyte and stroma fractions can be combined with conventional prognostic markers to improve patient stratification.

PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer.

Machine learning (ML) is expected to improve biomarker assessment. Using convolution neural networks, we developed a fully-automated method for assessing PTEN protein status in immunohistochemically-stained slides using a radical prostatectomy (RP) cohort (n = 253). It was validated according to a predefined protocol in an independent RP cohort (n = 259), alone and by measuring its prognostic value in combination with DNA ploidy status determined by ML-based image cytometry. In the primary analysis, automatically assessed dichotomized PTEN status was associated with time to biochemical recurrence (TTBCR) (hazard ratio (HR) = 3.32, 95% CI 2.05 to 5.38). Patients with both non-diploid tumors and PTEN-low had an HR of 4.63 (95% CI 2.50 to 8.57), while patients with one of these characteristics had an HR of 1.94 (95% CI 1.15 to 3.30), compared to patients with diploid tumors and PTEN-high, in univariable analysis of TTBCR in the validation cohort. Automatic PTEN scoring was strongly predictive of the PTEN status assessed by human experts (area under the curve 0.987 (95% CI 0.968 to 0.994)). This suggests that PTEN status can be accurately assessed using ML, and that the combined marker of automatically assessed PTEN and DNA ploidy status may provide an objective supplement to the existing risk stratification factors in prostate cancer.

Novel GTF2I-PDGFRB and IKZF1-TYW1 fusions in pediatric leukemia with normal karyotype.

BACKGROUND: Many cases of acute lymphoblastic leukemia (ALL) carry visible acquired chromosomal changes of pathogenetic, diagnostic, and prognostic importance. Nevertheless, from one-fourth to half of newly diagnosed ALL patients have no visible chromosomal changes detectable by G-banding analysis at diagnosis. The introduction of powerful molecular methodologies has shown that many karyotypically normal ALLs carry clinically important submicroscopic aberrations. CASE PRESENTATION: We used fluorescence in situ hybridization (FISH), array comparative genomic hybridization (aCGH), RNA sequencing, reverse transcription (RT) and genomic polymerase chain reaction (PCR), as well as Sanger sequencing to investigate a case of pediatric ALL with a normal karyotype. FISH with a commercial PDGFRB breakapart probe showed loss of the distal part of the probe suggesting a breakpoint within the PDGFRB locus. aCGH revealed submicroscopic deletions in chromosome bands 5q32q35.3 (about 30 Mb long, starting within PDGFRB and finishing in the CANX locus), 7q34 (within TCRB), 9p13 (PAX5), 10q26.13 (DMBT1), 14q11.2 (TRAC), and 14q32.33 (within the IGH locus). RNA sequencing detected an in-frame GTF2I-PDGFRB and an out-of-frame IKZF1-TYW1 fusion transcript. Both fusion transcripts were verified by RT-PCR together with Sanger sequencing and interphase FISH. The GTF2I-PDGFRB fusion was also verified by genomic PCR and FISH. The corresponding GTF2I-PDGFRB fusion protein would consist of almost the entire GTF2I and that part of PDGFRB which harbors the catalytic domain of the tyrosine kinase. It would therefore seem to lead to abnormal tyrosine kinase activity in a manner similar to what has been seen for other PDGFRB fusion proteins. CONCLUSIONS: The examined pediatric leukemia is a Ph-like ALL which carries novel GTF2I-PDGFRB and IKZF1-TYW1 fusion genes together with additional submicroscopic deletions. Because hematologic neoplasms with PDGFRB-fusion genes can be treated with tyrosine kinase inhibitors, the detection of such novel fusions may be clinically important. Since the GTF2I-PDGFRB could be detected only after molecular studies of the leukemic cells, further investigations of ALL-cases, perhaps especially but not exclusively with a normal karyotype, are needed in order to determine the frequency of GTF2I-PDGFRB in leukemia, and also to find out which clinical impact the fusion may have.

Changes in Chromatin Structure in Curettage Specimens Identifies High-Risk Patients in Endometrial Cancer.

BACKGROUND: Most endometrial carcinoma patients are diagnosed at an early stage with a good prognosis. However, a relatively low fraction with lethal disease constitutes a substantial number of patients due to the high incidence rate. Preoperative identification of patients with high risk and low risk for poor outcome is necessary to tailor treatment. Nucleotyping refers to characterization of cell nuclei by image cytometry, including the assessment of chromatin structure by nuclear texture analysis. This method is a strong prognostic marker in many cancers but has not been evaluated in preoperative curettage specimens from endometrial carcinoma. METHODS: The prognostic impact of changes in chromatin structure quantified with Nucleotyping was evaluated in preoperative curettage specimens from 791 endometrial carcinoma patients prospectively included in the MoMaTEC multicenter trial. RESULTS: Nucleotyping was an independent prognostic marker of disease-specific survival in preoperative curettage specimens among patients with Federation Internationale des Gynaecologistes et Obstetristes (FIGO) stage I-II disease (HR=2.9; 95% CI, 1.2-6.5; P = 0.013) and significantly associated with age, FIGO stage, histologic type, histologic grade, myometrial infiltration, lymph node status, curettage histology type, and DNA ploidy. CONCLUSIONS: Nucleotyping in preoperative curettage specimens is an independent prognostic marker for disease-specific survival, with potential to supplement existing parameters for risk stratification to tailor treatment. IMPACT: This is the first study to evaluate the prognostic impact of Nucleotyping in curettage specimens from endometrial carcinoma and shows that this may be a clinically useful prognostic marker in endometrial cancer. External validation is warranted. Cancer Epidemiol Biomarkers Prev; 26(1); 61-67. ©2016 AACR.