Ina Marie Dueholm Hjorth

Circulating tumor DNA in the diagnosis of ovarian cancer: a systematic review

Ovarian cancer remains a leading cause of gynecologic cancer mortality worldwide, largely due to late-stage diagnosis and limited early detection tools. Circulating tumor DNA (ctDNA) has emerged as a promising non-invasive biomarker with the potential to improve diagnostic accuracy through detection of tumor-specific genetic and epigenetic alterations. This systematic review aimed to evaluate the diagnostic accuracy of ctDNA in detecting ovarian cancer compared to healthy controls or benign conditions. A comprehensive literature search was conducted across PubMed, Web of Science, and EBSCO databases through April 2024, including studies that assessed sensitivity, specificity of ctDNA assays in plasma or serum samples. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. PROSPERO registration number: CRD42024590089. Nineteen studies met inclusion criteria, employing a variety of molecular techniques including polymerase chain reaction-based methylation assays (73.7%) and sequencing methods (whole genome sequencing/next-generation sequencing) (21%), targeting single genes or multi-gene panels. Diagnostic accuracy of ctDNA varied, with sensitivity (40.6%-94.7%) and specificity (56%-100%) ranging broadly, but often outperforming CA125, particularly in early-stage. Concordance between ctDNA and tumor tissue ranged from moderate (r = 0.428) to strong (r = 0.771). Although heterogeneity across studies precluded meta-analysis, narrative synthesis suggests that ctDNA may offer an improved early detection capability over CA125, through methylation and copy number variation analyses. Further controlled prospective studies are needed to validate the clinical utility of ctDNA as a complementary tool in ovarian cancer detection.

Ultrasound Scoring to Predict High‐Risk Endometrial Cancer

Objectives To evaluate a scoring system using transvaginal ultrasound (TVS) to predict high‐risk endometrial cancer. Methods Consecutive patients with endometrial cancer/atypical hyperplasia (n = 266) were preoperatively examined by residents using TVS. Clinical parameters, endometrial morphology and Doppler scores were recorded using a gray scale and Doppler TVS and related to final histopathology at hysterectomy. Multivariate logistic regression was used to correlate imaging and clinical parameters to the presence of high‐risk endometrial cancer (defined as FIGO stage Ib‐IV or high‐grade tumors [grade 3/non‐endometroid]) to develop the High‐Risk Endometrial Cancer (HIREC) score. Results High‐risk endometrial cancer (n = 128) and lympho‐vascular space invasion (LVSI) (n = 43) were predicted by increased endometrial thickness (ET), age, and Doppler score. The HIREC scoring system, based on age, Doppler score, and ET performed well with an AUC of 78.5% (CI 95%: 73–84) to predict high‐risk cancer. By using a 2‐step strategy of (1) Preoperative identification of high‐grade tumors by biopsy, (2) Assessing the HIREC score, high‐risk endometrial cancer could be predicted at a HIREC score of ≥7 with sensitivity, specificity, and accuracy of 72.7, 88.4, and 80.8%. Low‐risk endometrial cancer was predicted at HIREC scores of <5 with sensitivity, specificity, and accuracy values of 91.4, 46.4 and 68.1%, respectively. Conclusions Low and high HIREC scores effectively predicted low‐ and high‐risk endometrial cancer. The score is a simple point system suitable for the first ultrasound assessment. It may be used in preoperative work‐up to select treatment and additional imaging, but it needs to be validated in further studies.

Preoperative prediction of high-risk endometrial cancer by expert and non-expert transvaginal ultrasonography, magnetic resonance imaging, and endometrial histology

To identify women with high-risk endometrial cancers using expert and non-expert transvaginal ultrasonography (TVS) and MRI. Myometrial involvement was prospectively evaluated in patients with atypical hyperplasia or endometrial cancer on ultrasound by non-experts at first visit (non-expert-TVS: n = 266) and experts (expert-TVS: n = 188) at second visit. MRI (n = 175) was performed when high-risk cancer was suspected on non-expert-TVS. Preoperatively, high-risk cancer was defined as myometrial involvement ≥50 %, or preoperative unfavorable tumor histology (grade 3 endometrioid, non-endometrioid tumors, or tumor in cervical biopsies) obtained by endometrial sampling or hysteroscopic biopsies. Preoperative evaluations were compared with final histopathology obtained at surgery, high-risk cancer being defined as unfavorable tumor histology or patients with FIGO stage ≥1b. Preoperative unfavorable tumor histology was seen in 64 women and correctly identified 63 of 128 high-risk cancers. Preoperative diagnosis of unfavorable tumor histology or myometrial involvement ≥50 %, i.e. judged high-risk, had an area under the curve (AUC), sensitivity, and specificity of 79.5 %, 93.8 %, 65.2 % on non-expert-TVS; 85.5 %, 84.4 %, 86.5 % on expert-TVS, and 85.4 %, 89.6 %, 81.2 % on MRI. AUC values were not significantly different between MRI and expert-TVS, but lower on non-expert-TVS (p < 0.02). However, sensitivity was highest on non-expert-TVS, where a low cutpoint for myometrial involvement was used (included potentially deep and difficult evaluations) in contrast to an exact cutpoint of myometrial involvement ≥50 % used on expert-TVS and MRI. The highest AUC, 88.6 %, was seen when MRI was performed in patients with myometrial involvement ≥50 %, determined on non-expert TVS. Sensitivity was reduced to 85.9 %, while specificity increased to 91.3 %. Thus, MRI was needed for risk classification in only 104 (39 %) patients. Diagnostically, expert-TVS and MRI were comparable and superior to non-expert-TVS. However, non-expert-TVS classified all patients with unclear myometrial involvement ≥50 %, and thereby only misdiagnosed 6.2 % of high-risk cases. Non-expert-TVS combined with MRI when myometrial involvement was ≥50 % on non-expert-TVS was a simple and effective method comparable with expert imaging to identify low- and high-risk cancer and select patients for SLND. Addition of MRI to the diagnostic regimen was needed in only 39 % of our patients.