Emerging Molecular Imaging Techniques in Gynecologic Oncology

Metabolic markers derived from 18F-FDG PET/CT in suspected recurrent ovarian carcinoma: predictive value for disease burden and prognosis

OBJECTIVE

This study aims to assess the role of 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) in suspected recurrent ovarian carcinoma. Several clinical and PET parameters were assessed to evaluate disease burden and prognosis.

METHODS

We did a single-center, retrospective study in patients with suspected recurrent ovarian carcinoma who underwent 18F-FDG PET/CT. The disease burden on the scan was evaluated. We calculated several semiquantitative markers, including standard uptake values (SUV), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). Survival analysis was done with clinical parameters, CA-125 levels, disease distribution, and metabolic markers.

RESULTS

Fifty-two patients were included in the study. Half of the patients had suspected recurrence within 12 months of primary diagnosis. PET/CT scan suggested disease in 35 (67.3%) patients. Multiple metastatic sites were noted in 21 (40.4%) patients. Extra-abdominal metastases were seen in 15 (28.8%) patients. Eight patients had 18F-FDG avid disease despite a low CA-125 level (<35 IU). Young patients (<50 years), extra-abdominal disease, multiple metastases, and higher restaging were associated with poor outcomes. Meanwhile, treatment history, CA-125 level, and post-PET/CT treatment had no significant effect on survival. MTV@40% SUV (>17.21) and TLG@40% SUV (>68.7) had the sensitivity of 87.5% and 75% for predicting disease outcome.

CONCLUSION

Recurrent ovarian carcinoma commonly presents with multiple metastasis and extra-abdominal metastases. 18F-FDG PET/CT-guided patterns of disease distribution were significant markers for poor prognosis. Disease burden on PET/CT-derived semiquantitative parameters was associated with poor outcomes.

PET/MR imaging in gynecologic cancer: tips for differentiating normal gynecologic anatomy and benign pathology versus cancer

Positron emission tomography/magnetic resonance imaging (PET/MR) is used in the pre-treatment and surveillance settings to evaluate women with gynecologic malignancies, including uterine, cervical, vaginal and vulvar cancers. PET/MR combines the excellent spatial and contrast resolution of MR imaging for gynecologic tissues, with the functional metabolic information of PET, to aid in a more accurate assessment of local disease extent and distant metastatic disease. In this review, the optimal protocol and utility of whole-body PET/MR imaging in patients with gynecologic malignancies will be discussed, with an emphasis on the advantages of PET/MR over PET/CT and how to differentiate normal or benign gynecologic tissues from cancer in the pelvis.

Role of MRI in diagnosing the primary site of origin in indeterminate cases of uterocervical carcinomas: a systematic review and meta-analysis

OBJECTIVE

To perform a literature review assessing role of MRI in predicting origin of indeterminate uterocervical carcinomas with emphasis on sequences and imaging parameters.

METHODS

Electronic literature search of PubMed was performed from its inception until May 2020 and PICO model used for study selection; population was female patients with known/clinical suspicion of uterocervical cancer, intervention was MRI, comparison was by histopathology and outcome was differentiation between primary endometrial and cervical cancers.

RESULTS

Eight out of nine reviewed articles reinforced role of MRI in uterocervical primary determination. T2 and Dynamic contrast were the most popular sequences determining tumor location, morphology, enhancement, and invasion patterns. Role of DWI and MR spectroscopy has been evaluated by even fewer studies with significant differences found in both apparent diffusion coefficient values and metabolite spectra. The four studies eligible for meta-analysis showed a pooled sensitivity of 88.4% (95% confidence interval 70.6 to 96.1%) and a pooled specificity of 39.5% (95% confidence interval 4.2 to 90.6%).

CONCLUSIONS

MRI plays a pivotal role in uterocervical primary determination with both conventional and newer sequences assessing important morphometric and functional parameters. Socioeconomic impact of both primaries, different management guidelines and paucity of existing studies warrants further research. Prospective multicenter trials will help bridge this gap. Meanwhile, individual patient database meta-analysis can help corroborate existing data.

ADVANCES IN KNOWLEDGE

MRI with its classical and functional sequences helps in differentiation of the uterine 'cancer gray zone' which is imperative as both primary endometrial and cervical tumors have different management protocols.

Radiomics Analysis in Ovarian Cancer: A Narrative Review

Ovarian cancer (OC) is the second most common gynecological malignancy, accounting for about 14,000 deaths in 2020 in the US. The recognition of tools for proper screening, early diagnosis, and prognosis of OC is still lagging. The application of methods such as radiomics to medical images such as ultrasound scan (US), computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) in OC may help to realize so-called “precision medicine” by developing new quantification metrics linking qualitative and/or quantitative data imaging to achieve clinical diagnostic endpoints. This narrative review aims to summarize the applications of radiomics as a support in the management of a complex pathology such as ovarian cancer. We give an insight into the current evidence on radiomics applied to different imaging methods.

Nanomaterials for Antiangiogenic Therapies for Cancer: A Promising Tool for Personalized Medicine

Angiogenesis is one of the hallmarks of cancer. Several studies have shown that vascular endothelium growth factor (VEGF) plays a leading role in angiogenesis progression. Antiangiogenic medication has gained substantial recognition and is commonly administered in many forms of human cancer, leading to a rising interest in cancer therapy. However, this treatment method can lead to a deteriorating outcome of resistance, invasion, distant metastasis, and overall survival relative to its cytotoxicity. Furthermore, there are significant obstacles in tracking the efficacy of antiangiogenic treatments by incorporating positive biomarkers into clinical settings. These shortcomings underline the essential need to identify additional angiogenic inhibitors that target numerous angiogenic factors or to develop a new method for drug delivery of current inhibitors. The great benefits of nanoparticles are their potential, based on their specific properties, to be effective mechanisms that concentrate on the biological system and control various important functions. Among various therapeutic approaches, nanotechnology has emerged as a new strategy for treating different cancer types. This article attempts to demonstrate the huge potential for targeted nanoparticles and their molecular imaging applications. Notably, several nanoparticles have been developed and engineered to demonstrate antiangiogenic features. This nanomedicine could effectively treat a number of cancers using antiangiogenic therapies as an alternative approach. We also discuss the latest antiangiogenic and nanotherapeutic strategies and highlight tumor vessels and their microenvironments.

Whole-Volume Tumor MRI Radiomics for Prognostic Modeling in Endometrial Cancer

Background

In endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI‐based radiomic tumor profiling may aid in preoperative risk‐stratification and support clinical treatment decisions in EC.

Purpose

To develop MRI‐based whole‐volume tumor radiomic signatures for prediction of aggressive EC disease.

Study Type

Retrospective.

Population

A total of 138 women with histologically confirmed EC, divided into training (n_T = 108) and validation cohorts (n_V = 30).

Field Strength/Sequence

Axial oblique T₁‐weighted gradient echo volumetric interpolated breath‐hold examination (VIBE) at 1.5T (71/138 patients) and DIXON VIBE at 3T (67/138 patients) at 2 minutes postcontrast injection.

Assessment

Primary tumors were manually segmented by two radiologists with 4 and 8 years' of experience. Radiomic tumor features were computed and used for prediction of surgicopathologically‐verified deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), advanced stage (FIGO III + IV), nonendometrioid (NE) histology, and high‐grade endometrioid tumors (E3). Corresponding analyses were also conducted using radiomics extracted from the axial oblique image slice depicting the largest tumor area.

Statistical Tests

Logistic least absolute shrinkage and selection operator (LASSO) was applied for radiomic modeling in the training cohort. The diagnostic performances of the radiomic signatures were evaluated by area under the receiver operating characteristic curve in the training (AUC_T) and validation (AUC_V) cohorts. Progression‐free survival was assessed using the Kaplan–Meier and Cox proportional hazard model.

Results

The whole‐tumor radiomic signatures yielded AUC_T/AUC_V of 0.84/0.76 for predicting DMI, 0.73/0.72 for LNM, 0.71/0.68 for FIGO III + IV, 0.68/0.74 for NE histology, and 0.79/0.63 for high‐grade (E3) tumor. Single‐slice radiomics yielded comparable AUC_T but significantly lower AUC_V for LNM and FIGO III + IV (both P < 0.05). Tumor volume yielded comparable AUC_T to the whole‐tumor radiomic signatures for prediction of DMI, LNM, FIGO III + IV, and NE, but significantly lower AUC_T for E3 tumors (P < 0.05). All of the whole‐tumor radiomic signatures significantly predicted poor progression‐free survival with hazard ratios of 4.6–9.8 (P < 0.05 for all).

Data Conclusion

MRI‐based whole‐tumor radiomic signatures yield medium‐to‐high diagnostic performance for predicting aggressive EC disease. The signatures may aid in preoperative risk assessment and hence guide personalized treatment strategies in EC.

Level of Evidence

4

Technical Efficacy Stage

2

Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data

Artificial intelligence (AI) is currently regaining enormous interest due to the success of machine learning (ML), and in particular deep learning (DL). Image analysis, and thus radiomics, strongly benefits from this research. However, effectively and efficiently integrating diverse clinical, imaging, and molecular profile data is necessary to understand complex diseases, and to achieve accurate diagnosis in order to provide the best possible treatment. In addition to the need for sufficient computing resources, suitable algorithms, models, and data infrastructure, three important aspects are often neglected: (1) the need for multiple independent, sufficiently large and, above all, high-quality data sets; (2) the need for domain knowledge and ontologies; and (3) the requirement for multiple networks that provide relevant relationships among biological entities. While one will always get results out of high-dimensional data, all three aspects are essential to provide robust training and validation of ML models, to provide explainable hypotheses and results, and to achieve the necessary trust in AI and confidence for clinical applications.