Recent Imaging Updates and Advances in Gynecologic Malignancies

Estimating pathological prognostic factors in epithelial ovarian cancers using apparent diffusion coefficients of functional tumor volume

PURPOSE

To assess the utility of apparent diffusion coefficients (ADCs) of whole tumor volume (WTV) and functional tumor volume (FTV) in determining the pathologicalprognostic factors in epithelial ovarian cancers (EOCs).

METHODS

A total of 155 consecutive patients who were diagnosed with EOC between January 2017 and August 2022 and underwent both conventional magnetic resonance imaging and diffusion-weighted imaging were assessed in this study. The maximum, minimum, and mean ADC values of the whole tumor (ADCwmax, ADCwmin, and ADCwmean, respectively) and functional tumor (ADCfmax, ADCfmin, and ADCfmean, respectively) as well as the WTV and FTV were derived from the ADC maps. The univariate and multivariate logistic regression analyses and receiver operating characteristic curve (ROC) analysis were used to assess the correlation between these ADC values and the pathological prognostic factors, namely subtypes, lymph node metastasis (LNM), Ki-67 index, and p53 expression.

RESULTS

The ADCfmean value was significantly lower in type II EOC, LNM-positive, and high-Ki-67 index groups compared to the type I EOC, LNM-negative, and low-Ki-67 index groups (p ≤ 0.001). Similarly, the ADCwmean and ADCfmean values were lower in the mutant-p53 group compared to the wild-type-p53 group (p ≤ 0.001). Additionally, the ADCfmean showed the highest area under the ROC curve (AUC) for evaluating type II EOC (0.725), LNM-positive (0.782), and high-Ki-67 index (0.688) samples among the given ROC curves, while both ADCwmean and ADCfmean showed high AUCs for assessing p53 expression (0.694 and 0.678, respectively).

CONCLUSION

The FTV-derived ADC values, especially ADCfmean, can be used to assess preoperative prognostic factors in EOCs.

Endometrial Cancer: 2023 Revised FIGO Staging System and the Role of Imaging

The FIGO endometrial cancer staging system recently released updated guidance based on clinical evidence gathered after the previous version was published in 2009. Different imaging modalities are beneficial across various stages of endometrial cancer (EC) management. Additionally, ongoing research studies are aimed at improving imaging in EC. Gynecological cancer is a crucial element in the practice of a body radiologist. With a new staging system in place, it is important to address the role of radiology in the EC diagnostic pathway. This article is a comprehensive review of the changes made to the FIGO endometrial cancer staging system and the impact of imaging in the staging of this disease.

Survival time prediction in patients with high-grade serous ovarian cancer based on 18F-FDG PET/CT- derived inter-tumor heterogeneity metrics

BACKGROUND

The presence of heterogeneity is a significant attribute within the context of ovarian cancer. This study aimed to assess the predictive accuracy of models utilizing quantitative 18F-FDG PET/CT derived inter-tumor heterogeneity metrics in determining progression-free survival (PFS) and overall survival (OS) in patients diagnosed with high-grade serous ovarian cancer (HGSOC). Additionally, the study investigated the potential correlation between model risk scores and the expression levels of p53 and Ki-67.

METHODS

A total of 292 patients diagnosed with HGSOC were retrospectively enrolled at Shengjing Hospital of China Medical University (median age: 54 ± 9.4 years). Quantitative inter-tumor heterogeneity metrics were calculated based on conventional measurements and texture features of primary and metastatic lesions in 18F-FDG PET/CT. Conventional models, heterogeneity models, and integrated models were then constructed to predict PFS and OS. Spearman's correlation coefficient (ρ) was used to evaluate the correlation between immunohistochemical scores of p53 and Ki-67 and model risk scores.

RESULTS

The C-indices of the integrated models were the highest for both PFS and OS models. The C-indices of the training set and testing set of the integrated PFS model were 0.898 (95% confidence interval [CI]: 0.881-0.914) and 0.891 (95% CI: 0.860-0.921), respectively. For the integrated OS model, the C-indices of the training set and testing set were 0.894 (95% CI: 0.871-0.917) and 0.905 (95% CI: 0.873-0.936), respectively. The integrated PFS model showed the strongest correlation with the expression levels of p53 (ρ = 0.859, p < 0.001) and Ki-67 (ρ = 0.829, p < 0.001).

CONCLUSIONS

The models based on 18F-FDG PET/CT quantitative inter-tumor heterogeneity metrics exhibited good performance for predicting the PFS and OS of patients with HGSOC. p53 and Ki-67 expression levels were strongly correlated with the risk scores of the integrated predictive models.

Nuclear Medicine and Molecular Imaging Applications in Gynecologic Malignancies: A Comprehensive Review

Gynecologic malignancies, consisting of endometrial, cervical, ovarian, vulvar, and vaginal cancers, pose significant diagnostic and management challenges due to their complex anatomic location and potential for rapid progression. These tumors cause substantial morbidity and mortality, often because of their delayed diagnosis and treatment. An estimated 19% of newly diagnosed cancers among women are gynecologic in origin. In recent years, there has been growing evidence supporting the integration of nuclear medicine imaging modalities in the diagnostic work-up and management of gynecologic cancers. The sensitivity of fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG PET) combined with the anatomical specificity of computed tomography (CT) and magnetic resonance imaging (MRI) allows for the hybrid evaluation of metabolic activity and structural abnormalities that has become an indispensable tool in oncologic imaging. Lymphoscintigraphy, using technetium 99m (99mTc) based radiotracers along with single photon emission computed tomography/ computed tomography (SPECT/CT), holds a vital role in the identification of sentinel lymph nodes to minimize the surgical morbidity from extensive lymph node dissections. While not yet standard for gynecologic malignancies, promising therapeutic nuclear medicine agents serve as specialized treatment options for patients with advanced or recurrent disease. This article aims to provide a comprehensive review on the nuclear medicine applications in gynecologic malignancies through the following objectives: 1) To describe the role of nuclear medicine in the initial staging, lymph node mapping, response assessment, and recurrence/surveillance imaging of common gynecologic cancers, 2) To review the limitations of 18F-FDG PET/CT and promising applications of 18F-FDG PET/MRI in gynecologic malignancy, 3) To underscore the promising theragnostic applications of nuclear medicine, 4) To highlight the current role of nuclear medicine imaging in gynecologic cancers as per the National Comprehensive Cancer Network (NCCN), European Society of Surgical Oncology (ESGO), and European Society of Medical Oncology (ESMO) guidelines.

Additional Value of FDG-PET/MRI Complementary to Sentinel Lymphonodectomy for Minimal Invasive Lymph Node Staging in Patients with Endometrial Cancer: A Prospective Study

BACKGROUND

Lymph node metastases (LNM) are rare in early-stage endometrial cancer, but a diagnostic systematic lymphadenectomy (LNE) is often performed to achieve reliable N-staging. Therefore, this prospective study aimed to evaluate the benefit of [18F]-Fluorodeoxyglucose (FDG) PET/MRI complementary to SPECT/CT guided sentinel lymphonodectomy (SLNE) for a less invasive N-staging Methods: 79 patients underwent a whole-body FDG-PET/MRI, SLN mapping with 99mTc-Nanocolloid SPECT/CT and indocyanine green (ICG) fluoroscopy followed by LNE which served as ground truth.

RESULTS

FDG-PET/MRI was highly specific in N-staging (97.2%) but revealed limited sensitivity (66.7%) due to missed micrometastases. In contrast, bilateral SLN mapping failed more often in patients with macrometastases. The combination of SLN mapping and FDG-PET/MRI increased the sensitivity from 66.7% to 77.8%. Additional SLN labeling with dye (ICG) revealed a complete SLN mapping in 80% (8/10) of patients with failed or incomplete SLN detection in SPECT/CT, reducing the need for diagnostic systematic LNE up to 87%. FDG-PET/MRI detected para-aortic LNM in three out of four cases and a liver metastasis.

CONCLUSIONS

The combination of FDG-PET/MRI and SLNE can reduce the need for diagnostic systematic LNE by up to 87%. PET/MRI complements the SLN technique particularly in the detection of para-aortic LNM and occasional distant metastases.

Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis

Introduction

Gynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications.

Methods

The study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gynecological cancer and used DL for diagnosis were selected. Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model.

Results

The review included 48 studies, and the meta-analysis included 24 studies. The studies used different images and models to diagnose different gynecological cancers. The most popular models were ResNet, VGGNet, and UNet. DL algorithms showed more sensitivity but less specificity compared to machine learning (ML) methods. The AUC of the summary receiver operating characteristic plot was higher for DL algorithms than for ML methods. Of the 48 studies included, 41 were at low risk of bias.

Conclusion

This review highlights the potential of DL in improving the screening and diagnosis of gynecological cancer, particularly in resource-limited settings. However, the high heterogeneity and quality of the studies could affect the validity of the results. Further research is necessary to validate the findings of this study and to explore the potential of DL in improving gynecological cancer diagnosis.

Tata Memorial Centre Evidence Based Use of Nuclear medicine diagnostic and treatment modalities in cancer

ABSTRACT

PET/CT and radioisotope therapy are diagnostic and therapeutic arms of Nuclear Medicine, respectively. With the emergence of better technology, PET/CT has become an accessible modality. Diagnostic tracers exploring disease-specific targets has led the clinicians to look beyond FDG PET. Moreover, with the emergence of theranostic pairs of radiopharmaceuticals, radioisotope therapy is gradually making it's way into treatment algorithm of common cancers in India. We therefore would like to discuss in detail the updates in PET/CT imaging and radionuclide therapy and generate a consensus-driven evidence based document which would guide the practitioners of Oncology.

Dual-Energy Computed Tomography Applications in the Genitourinary Tract

By virtue of material differentiation capabilities afforded through dedicated postprocessing algorithms, dual-energy CT (DECT) has been shown to provide benefit in the evaluation of various diseases. In this article, we review the diagnostic use of DECT in the assessment of genitourinary diseases, with emphasis on its role in renal stone characterization, incidental renal and adrenal lesion characterization, retroperitoneal trauma, reduction of radiation, and contrast dose and cost-effectiveness potential. We also discuss future perspectives of the DECT scanning mode, including the use of novel contrast injection strategies and photon-counting detector computed tomography.

Value of deep-learning image reconstruction at submillisievert CT for evaluation of the female pelvis

AIM

To assess the value of deep-learning reconstruction (DLR) at submillisievert computed tomography (CT) for the evaluation of the female pelvis, with standard dose (SD) hybrid iterative reconstruction (IR) images as reference.

MATERIALS AND METHODS

The present study enrolled 50 female patients consecutively who underwent contrast-enhanced abdominopelvic CT for clinically indicated reasons. Submillisievert pelvic images were acquired using a noise index of 15 for low-dose (LD) scans, which were reconstructed with DLR (body and body sharp), hybrid-IR, and model-based IR (MBIR). Additionally, SD scans were reconstructed with a noise index of 7.5 using hybrid-IR. Radiation dose, quantitative image quality, overall image quality, image appearance using a five-point Likert scale (1-5: worst to best), and lesion evaluation in both SD and LD images were analysed and compared.

RESULTS

The submillisievert pelvic CT examinations showed a 61.09 ± 4.13% reduction in the CT dose index volume compared to SD examinations. Among the LD images, DLR (body sharp) had the highest quantitative quality, followed by DLR (body), MBIR, and hybrid-IR. LD DLR (body) had overall image quality comparable to the reference (p=0.084) and favourable image appearance (p=0.209). In total, 40 pelvic lesions were detected in both SD and LD images. LD DLR (body and body sharp) exhibited similar diagnostic confidence (p=0.317 and 0.096) compared with SD hybrid-IR.

CONCLUSION

DLR algorithms, providing comparable image quality and diagnostic confidence, are feasible in submillisievert abdominopelvic CT. The DLR (body) algorithm with favourable image appearance is recommended in clinical settings.

18F-FDG PET/MRI and 18F-FDG PET/CT for the Management of Gynecological Malignancies: A Comprehensive Review of the Literature

OBJECTIVE

Positron emission tomography with 2-deoxy-2-[fluorine-18] fluoro- D-glucose integrated with computed tomography (18F-FDG PET/CT) or magnetic resonance imaging (18F-FDG PET/MRI) has emerged as a promising tool for managing various types of cancer. This review study was conducted to investigate the role of 18F- FDG PET/CT and FDG PET/MRI in the management of gynecological malignancies.

SEARCH STRATEGY

We searched for relevant articles in the three databases PubMed/MEDLINE, Scopus, and Web of Science.

SELECTION CRITERIA

All studies reporting data on the FDG PET/CT and FDG PET MRI in the management of gynecological cancer, performed anywhere in the world and published exclusively in the English language, were included in the present study.

DATA COLLECTION AND ANALYSIS

We used the EndNote software (EndNote X8.1, Thomson Reuters) to list the studies and screen them on the basis of the inclusion criteria. Data, including first author, publication year, sample size, clinical application, imaging type, and main result, were extracted and tabulated in Excel. The sensitivity, specificity, and diagnostic accuracy of the modalities were extracted and summarized.

MAIN RESULTS

After screening 988 records, 166 studies published between 2004 and 2022 were included, covering various methodologies. Studies were divided into the following five categories: the role of FDG PET/CT and FDG-PET/MRI in the management of: (a) endometrial cancer (n = 30); (b) ovarian cancer (n = 60); (c) cervical cancer (n = 50); (d) vulvar and vagina cancers (n = 12); and (e) gynecological cancers (n = 14).

CONCLUSIONS

FDG PET/CT and FDG PET/MRI have demonstrated potential as non-invasive imaging tools for enhancing the management of gynecological malignancies. Nevertheless, certain associated challenges warrant attention.

Clinical characterization and genomic landscape of gynecological cancers among patients attending a Chinese hospital

Background

Gynecological cancers are the most lethal malignancies among females, most of which are associated with gene mutations. Few studies have compared the differences in the genomic landscape among various types of gynecological cancers. In this study, we evaluated the diversity of mutations in different gynecological cancers.

Methods

A total of 184 patients with gynecological cancer, including ovarian, cervical, fallopian tube, and endometrial cancer, were included. Next-generation sequencing was performed to detect the mutations and tumor mutational burden (TMB). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were also conducted.

Results

We found that 94.57% of patients had at least one mutation, among which single nucleotide variants, insertions and InDels were in the majority. TP53, PIK3CA, PTEN, KRAS, BRCA1, BRCA2, ARID1A, KMT2C, FGFR2, and FGFR3 were the top 10 most frequently mutated genes. Patients with ovarian cancer tended to have higher frequencies of BRCA1 and BRCA2 mutations, and the frequency of germline BRCA1 mutations (18/24, 75.00%) was higher than that of BRCA2 (11/19, 57.89%). A new mutation hotspot in BRCA2 (I770) was firstly discovered among Chinese patients with gynecological cancer. Patients with TP53, PIK3CA, PTEN, and FGFR3 mutations had significantly higher TMB values than those with wild-type genes. A significant cross was discovered between the enriched KEGG pathways of gynecological and breast cancers. GO enrichment revealed that the mutated genes were crucial for the cell cycle, neuronal apoptosis, and DNA repair.

Conclusion

Various gynecological cancer types share similarities and differences both in clinical characterization and genomic mutations. Taken together with the results of TMB and enriched pathways, this study provided useful information on the molecular mechanism underlying gynecological cancers and the development of targeted drugs and precision medicine.