RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren

Radiomics Analysis of Multiparametric PET/MRI for N- and M-Staging in Patients with Primary Cervical Cancer

Zielsetzung Ziel dieser Studie war die Evaluierung des prädiktiven Potenzials der Radiomics-Analyse zur Bestimmung des N- und M-Stadiums des primären Zervixkarzinoms anhand multiparametrischer 18F-FDG-PET/MRT-Bildgebung. Material und Methoden 30 Patientinnen mit einem histologisch gesicherten, primären und therapienaiven Zervixkarzinom unterzogen sich einer multiparametrischen 18F-FDG-PET/MRT-Untersuchung unter Verwendung eines dedizierten Untersuchungsprotokolls des weiblichen Beckens. Nach Segmentierung der Primärtumoren wurden quantitative Bildparameter mittels der Radiomic-Image-Processing-Toolbox bestimmt. Insgesamt wurden 45 verschiedene quantitative Bildmerkmale jeweils anhand der T2-gewichteten TSE-Sequenzen, der nativen und kontrastmittelgestützten T1-gewichteten TSE-Sequenzen, der ADC-Map, verschiedenen Perfusionsparametern (Ktrans, Kep, Ve and iAUC) und den 18F-FDG-PET-Datensätzen für jeden Tumor extrahiert. Die statistische Analyse zur Bestimmung des N- und M-Stadiums erfolgte unter der Verwendung der Python 3.5 und Scikit-learn-Software-Bibliothek für maschinelles Lernen. Ergebnisse Insgesamt zeigte sich eine höhere Genauigkeit zur Prädiktion des korrekten M-Stadiums im Vergleich zum N-Stadium. Zur Prädiktion des korrekten M-Stadiums zeigten sich unter der Verwendung von SVM und SVM-RFE zur Feature-Auswahl die besten Ergebnisse mit einer Sensitivität von 91 %, einer Spezifität von 92 % und einer Fläche unter der Kurve (AUC) von 0,97. Die höchste Genauigkeit für die Bestimmung des N-Stadiums erfolgte unter der Verwendung von RBF-SVM und MIFS zur Feature-Auswahl mit einer Sensitivität von 83 %, einer Spezifität von 67 % und einer Fläche unter der Kurve (AUC) von 0,82. Schlussfolgerung Die Radiomics-Analyse von multiparametrischen PET/MR-Datensätzen ermöglicht eine präzise Prädiktion des M- und N-Stadiums von Patientinnen mit primärem Zervixkarzinom und könnte damit supportiv zur nichtinvasiven Tumor-Phänotypisierung und Patientenstratifizierung eingesetzt werden. Kernaussagen:  Citation Format

Ovarian Cancer Screening: Recommendations and Future Prospects

Abstract Ovarian cancer remains a significant cause of mortality among women, largely due to challenges in early detection. Current screening strategies, including transvaginal ultrasound and CA125 testing, have limited sensitivity and specificity, particularly in asymptomatic women or those with early-stage disease. The European Society of Gynaecological Oncology, the European Society for Medical Oncology, the European Society of Pathology, and other health organizations currently do not recommend routine population-based screening for ovarian cancer due to the high rates of false-positives and the absence of a reliable early detection method. This review examines existing ovarian cancer screening guidelines and explores recent advances in diagnostic technologies including radiomics, artificial intelligence, point-of-care testing, and novel detection methods. Emerging technologies show promise with respect to improving ovarian cancer detection by enhancing sensitivity and specificity compared to traditional methods. Artificial intelligence and radiomics have potential for revolutionizing ovarian cancer screening by identifying subtle diagnostic patterns, while liquid biopsy-based approaches and cell-free DNA profiling enable tumor-specific biomarker detection. Minimally invasive methods, such as intrauterine lavage and salivary diagnostics, provide avenues for population-wide applicability. However, large-scale validation is required to establish these techniques as effective and reliable screening options.

Unraveling Tumor Heterogeneity in Gynecological Cancer Using a Radiogenomics Approach

Abstract Ovarian cancer (OC) and endometrial cancer (EC) are highly heterogeneous gynecological malignancies with distinct molecular subtypes, therapeutic responses, and clinical outcomes. Traditional biopsy-based profiling often fails to capture the spatial and temporal complexity of these tumors. Radiogenomics, integrating imaging features with genomic and molecular data, has emerged as a promising approach to non-invasively analyze tumor heterogeneity. The purpose of this abstract is to critically examine and synthesize existing research on the application of radiogenomics in OC and EC, focusing on its ability to correlate imaging phenotypes with molecular biomarkers. This narrative review aims to demonstrate how radiogenomics can enhance tumor characterization, support biomarker prediction, and inform prognosis and therapeutic decision-making with non-invasive methods. This narrative review critically synthesizes current literature on radiogenomics applications in OC and EC. Studies using CT, MRI, and PET imaging were evaluated for their ability to correlate imaging phenotypes with molecular biomarkers, gene expression profiles, and clinical outcomes. The analysis emphasizes the role of radiogenomics in enhancing tumor characterization, predicting biomarker status, forecasting treatment response and prognosis. Radiogenomics has successfully identified associations between imaging features and key molecular alterations, such as BRCA mutations, homologous recombination deficiency (HRD), and immune-related biomarkers in OC, as well as POLE mutations, microsatellite instability (MSI), and tumor mutational burden (TMB) in EC. Predictive models incorporating radiomic features have demonstrated notable performance in estimating prognosis, treatment response, and recurrence risk across both cancer types. Radiogenomics has a strong potential to enhance personalized cancer care by analyzing tumor heterogeneity. However, clinical application requires methodological standardization, prospective validation, and integration into precision oncology workflows.

18F-FDG PET/CT in assessment of paraneoplastic cerebellar degeneration as the first sign of occult fallopian tube serous cystadenocarcinoma: Case report

Revised FIGO Staging for Cervical Cancer – A New Role for MRI

Cervical cancer is still the fourth most common malignancy in women worldwide and has a high mortality rate. The prognosis as well as the therapy depends largely on the extent of the tumor at the time of initial diagnosis. This shows the importance of correct staging of cervical cancer. In order to promote a globally uniform approach, staging of cervical cancer in the past was based on widespread examinations such as exam under anesthesia, histology from cervical conization or biopsy, systematic lymphadenectomy, cystoscopy, proctoscopy, i. v.-pyelogram and chest X-ray. However, as the primary tumor stage was often underestimated, the 2018 revised FIGO classification now permits cross-sectional imaging techniques and pathological findings to be incorporated into disease staging or an already existing stage to be adapted based on radiological findings. Thanks to its excellent soft tissue contrast, magnetic resonance imaging (MRI) is the method of choice for local-regional staging of cervical cancer, evaluating the response to treatment, detecting tumor recurrence and for follow-up examinations. It is important that radiologists interpreting pelvic MRI in case of suspected cervical cancer are familiar with the current FIGO staging system. This is the only way to determine the tumor stage as precisely as possible and thus lay the foundation for the success of therapy for patients. The aim of this review is to present the changes of the revised FIGO classification as well as to show the importance of MRI as the method of choice for local-regional tumor staging as a complement to clinical examination. Key Points:  Citation Format

Große Ovarialmetastase beim Mammakarzinom: ein seltener Fall