Radiomics systematic review in cervical cancer: gynecological oncologists' perspective.

OBJECTIVE: Radiomics is the process of extracting quantitative features from radiological images, and represents a relatively new field in gynecological cancers. Cervical cancer has been the most studied gynecological tumor for what concerns radiomics analysis. The aim of this study was to report on the clinical applications of radiomics combined and/or compared with clinical-pathological variables in patients with cervical cancer. METHODS: A systematic review of the literature from inception to February 2023 was performed, including studies on cervical cancer analysing a predictive/prognostic radiomics model, which was combined and/or compared with a radiological or a clinical-pathological model. RESULTS: A total of 57 of 334 (17.1%) screened studies met inclusion criteria. The majority of studies used magnetic resonance imaging (MRI), but positron emission tomography (PET)/computed tomography (CT) scan, CT scan, and ultrasound scan also underwent radiomics analysis. In apparent early-stage disease, the majority of studies (16/27, 59.3%) analysed the role of radiomics signature in predicting lymph node metastasis; six (22.2%) investigated the prediction of radiomics to detect lymphovascular space involvement, one (3.7%) investigated depth of stromal infiltration, and one investigated (3.7%) parametrial infiltration. Survival prediction was evaluated both in early-stage and locally advanced settings. No study focused on the application of radiomics in metastatic or recurrent disease. CONCLUSION: Radiomics signatures were predictive of pathological and oncological outcomes, particularly if combined with clinical variables. These may be integrated in a model using different clinical-pathological and translational characteristics, with the aim to tailor and personalize the treatment of each patient with cervical cancer.

O-RADS MRI: A Systematic Review and Meta-Analysis of Diagnostic Performance and Category-wise Malignancy Rates.

Background US-indeterminate adnexal lesions remain an important indication for gynecologic surgery. MRI can serve as a problem-solving tool through the use of the Ovarian-Adnexal Imaging Reporting and Data System (O-RADS) MRI lexicon, which is based on the ADNEX MR scoring system. Purpose To perform a systematic review and meta-analysis of the diagnostic performance of pelvic MRI interpreted using the ADNEX or O-RADS MRI stratification systems to characterize US-indeterminate adnexal lesions and of the category-wise malignancy rates. Materials and Methods A systematic literature search from May 2013 (publication of the ADNEX MR score) to September 2022 was performed. Studies reporting the use of pelvic MRI interpreted with the ADNEX or O-RADS MRI systems to characterize US-indeterminate adnexal lesions, with pathologic examination and/or follow-up as the reference standard, were included. Summary estimates of diagnostic performance were obtained with the bivariate random-effects model, while category-wise summary malignancy rates of O-RADS MRI 2, 3, 4, and 5 lesions were obtained with a random-effects model. Effects of covariates on heterogeneity and diagnostic performance were investigated through meta-regression. Results Thirteen study parts from 12 studies (3731 women, 4520 adnexal lesions) met the inclusion criteria. Diagnostic performance meta-analysis for 4012 lesions found a 92% summary sensitivity (95% CI: 88, 95) and a 91% summary specificity (95% CI: 89, 93). The meta-analysis of malignancy rates for 3641 lesions showed summary malignancy rates of 0.1% (95% CI: 0, 1) among O-RADS MRI 2 lesions, 6% (95% CI: 3, 9) among O-RADS MRI 3 lesions, 60% (95% CI: 52, 67) among O-RADS MRI 4 lesions, and 96% (95% CI: 92, 99) among O-RADS MRI 5 lesions. Conclusion Pelvic MRI interpreted with the Ovarian-Adnexal Reporting and Data System (O-RADS) MRI lexicon had high diagnostic performance for the characterization of US-indeterminate adnexal lesions. Summary estimates of malignancy rates in the O-RADS MRI 4 and O-RADS MRI 5 categories were higher than predicted ones. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Lee and Kang in this issue.

The contribution of the 1H-MRS lipid signal to cervical cancer prognosis: a preliminary study.

BACKGROUND: The aim of this study was to investigate the role of the lipid peak derived from 1H magnetic resonance (MR) spectroscopy in assessing cervical cancer prognosis, particularly in assessing response to neoadjuvant chemotherapy (NACT) of locally advanced cervical cancer (LACC). METHODS: We enrolled 17 patients with histologically proven cervical cancer who underwent 3-T MR imaging at baseline. In addition to conventional imaging sequences for pelvic assessment, the protocol included a single-voxel point-resolved spectroscopy (PRESS) sequence, with repetition time of 1,500 ms and echo times of 28 and 144 ms. Spectra were analysed using the LCModel fitting routine, thus extracting multiple metabolites, including lipids (Lip) and total choline (tCho). Patients with LACC were treated with NACT and reassessed by MRI at term. Based on tumour volume reduction, patients were classified as good responder (GR; tumour volume reduction > 50%) and poor responder or nonresponder (PR-or-NR; tumour volume reduction ≤ 50%). RESULTS: Of 17 patients, 11 were LACC. Of these 11, only 6 had both completed NACT and had good-quality 1H-MR spectra; 3 GR and 3 PR-or-NR. A significant difference in lipid values was observed in the two groups of patients, particularly with higher Lip values and higher Lip/tCho ratio in PR-NR patients (p =0.040). A significant difference was also observed in choline distribution (tCho), with higher values in GR patients (p = 0.040). CONCLUSIONS: Assessment of lipid peak at 1H-MR spectroscopy could be an additional quantitative parameter in predicting the response to NACT in patients with LACC.

MRI in the Evaluation of Locally Advanced Vulvar Cancer Treated with Chemoradiotherapy and Vulvar Cancer Recurrence: The 2021 Revision of FIGO Classification and the Need for Multidisciplinary Management.

Magnetic resonance imaging (MRI) plays an essential role in the management of patients with locally advanced vulvar cancer (LAVC), who frequently benefit from a multidisciplinary approach. Accordingly, chemoradiotherapy (CRT) with radical or neoadjuvant intent seems to provide a better quality of life and less morbidity than extensive surgery alone. In this overview, we discuss the role of MRI in the post-CRT assessment of LAVC, emphasizing the evaluation of primary tumor response. In order to assess treatment response and select candidates for post-CRT local excision, the MRI findings are described according to signal intensity, restricted diffusion, enhancement, and invasion of adjacent organs. We also focus on the role of MRI in detecting vulvar cancer recurrence. It occurs in 30-50% of patients within two years after initial treatment, the majority appearing near the original resection margins or in ipsilateral inguinal or pelvic lymph nodes. Finally, we describe early and delayed complications of CRT, such as cellulitis, urethritis, vulvar edema, bone changes, myositis, and fistulization. By describing the role of MRI in assessing LAVC response to CRT and detecting recurrence, we hope to provide suitable indications for a personalized approach.

CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset.

PURPOSE: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. METHODS: Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables. RESULTS: We included 218 patients. Radiomic models showed low performance in predicting both BRCA mutation (AUC in test set between 0.46 and 0.59) and 1-year relapse (AUC in test set between 0.46 and 0.56); deep learning models demonstrated similar results (AUC in the test of 0.48 for BRCA and 0.50 for relapse). The inclusion of clinical variables improved the performance of the radiomic models to predict BRCA mutation (AUC in the test set of 0.74). CONCLUSIONS: In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status, with both traditional radiomics and deep learning, but the combination of clinical and radiomic models improved model performance for the prediction of BRCA mutation. These findings highlight the need for standardization through the whole radiomic pipelines and robust multicentric external validations of results.

MRI and Adenomyosis: What Can Radiologists Evaluate?

Uterine adenomyosis is a common benign condition defined by the presence of heterotopic endometrial glands and stroma within the myometrium. Adenomyosis is often related to infertility and other adverse pregnancy outcomes. Modern imaging techniques allow the non-invasive diagnosis of adenomyosis and, in this framework, Magnetic Resonance Imaging (MRI) has assumed a central role due to its high diagnostic accuracy in the detection of adenomyosis. Currently, there is still a lack of international consensus on adenomyosis diagnostic criteria and classification, despite the fact that an agreed reporting system would promote treatment outcomes and research. This review aims to emphasize the important contribution of MRI to the diagnosis of adenomyosis and to highlight how, thanks to the great tissue differentiation provided by MRI, it is possible to identify the main direct (cystic component) and indirect (junctional zone features) signs of adenomyosis and to distinguish its various subtypes according to different MRI-based classifications. We also explored the main MRI criteria to identify the most common pitfalls and differential diagnoses of adenomyosis, whose features should be considered to avoid misdiagnosis.

Role of radiomics in predicting lung cancer spread through air spaces in a heterogeneous dataset.

Background: Spread through air spaces (STAS) has been reported as a negative prognostic factor in patients with lung cancer undergoing sublobar resection. Radiomics has been recently proposed to predict STAS using preoperative computed tomography (CT). However, limitations of previous studies included the strict selection of imaging acquisition protocols, leading to results hardly applicable to daily clinical practice. The aim of this study is to test a radiomics-based prediction model of STAS in a practice-based dataset. Methods: A training cohort of 99 consecutive patients (65 STAS+ and 34 STAS-) with resected lung adenocarcinoma (ADC) was retrospectively collected. Preoperative CT images were collected from different centers regardless model and scanner manufacture, acquisition and reconstruction protocol, contrast phase and pixel size. Radiomics features were selected according to separation power and P value stability within different preprocessing setups and bootstrapping resampling. A prospective cohort of 50 patients (33 STAS+ and 17 STAS-) was enrolled for the external validation. Results: Only the five features with the highest stability were considered for the prediction model building. Radiomics, radiological and mixed radiomics-radiological prediction models were created, showing an accuracy of 0.66±0.02 after internal validation and reaching an accuracy of 0.78 in the external validation. Conclusions: Radiomics-based prediction models of STAS may be useful to properly plan surgical treatment and avoid oncological ineffective sublobar resections. This study supports a possible application of radiomics-based models on data with high variance in acquisition, reconstruction and preprocessing, opening a new chance for the use of radiomics in the prediction of STAS. Trial Registration: ClinicalTrials.gov identifier: NCT04893200.

Intravoxel Incoherent Motion (IVIM) MR Quantification in Locally Advanced Cervical Cancer (LACC): Preliminary Study on Assessment of Tumor Aggressiveness and Response to Neoadjuvant Chemotherapy.

The aim of this study was to determine whether quantitative parameters obtained from intravoxel incoherent motion (IVIM) model at baseline magnetic resonance imaging (MRI) correlate with histological parameters and response to neoadjuvant chemotherapy in patients with locally advanced cervical cancer (LACC). METHODS: Twenty patients with biopsy-proven cervical cancer, staged as LACC on baseline MRI and addressed for neoadjuvant chemotherapy were enrolled. At treatment completion, tumor response was assessed with a follow-up MRI evaluated using the revised response evaluation criteria in solid tumors (RECIST; version 1.1), and patients were considered good responders (GR) if they had complete response or partial remission, and poor responders/non-responders (PR/NR) if they had stable or progressive disease. MRI protocol included conventional diffusion-weighted imaging (DWI; b = 0 and 1000 s/mm2) and IVIM acquisition using eight b-values (range: 0-1500 s/mm2). MR-images were analyzed using a dedicated software to obtain quantitative parameters: diffusion (D), pseudo-diffusion (D*), and perfusion fraction (fp) from the IVIM model; apparent diffusion coefficient (ADC) from conventional DWI. Histologic subtype, grading, and tumor-infiltrating lymphocytes (TILs) were assessed in each LACC. RESULTS: D showed significantly higher values in GR patients (p = 0.001) and in moderate/high TILs (p = 0.018). Fp showed significantly higher values in squamous cell tumors (p = 0.006). CONCLUSIONS: D extracted from the IVIM model could represent a promising tool to identify tumor aggressiveness and predict response to therapy.

Can New ENZIAN Score 2020 Represent a Staging System Improving MRI Structured Report?

Structured reporting systems for endometriotic disease are gaining a central role in diagnostic imaging: our aim is to evaluate applicability and the feasibility of the recent ENZIAN score (2020) assessed by MRI. A total of 60 patients with suspected tubo-ovarian/deep endometriosis were retrospectively included in our study according to the following criteria: availability of MR examination; histopathological results from laparoscopic or surgical treatment; patients were not assuming estro-progestin or progestin therapy. Three different readers (radiologists with 2-, 5-, and 20-years of experience in pelvic imaging) have separately assigned a score according to the ENZIAN score (revised 2020) for all lesions detected by magnetic resonance imaging (MRI). Our study showed a high interobserver agreement and feasibility of the recent ENZIAN score applied to MRI; on the other hand, our experience highlighted some limitations mainly due to MRI's inability to assess tubal patency and mobility, as required by the recent score (2020). In view of the limitations which arose from our study, we propose a modified MRI-ENZIAN score that provides a complete structured reporting system, more suitable for MRI. The high interobserver agreement of the recent ENZIAN score applied to MRI confirms its validity as a complete staging system for endometriosis, offering a shared language between radiologists and surgeons.

Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients.

PURPOSE: The assessment of Programmed death-ligand 1 (PD-L1) expression has become a game changer in the treatment of patients with advanced non-small cell lung cancer (NSCLC). We aimed to investigate the ability of Radiomics applied to computed tomography (CT) in predicting PD-L1 expression in patients with advanced NSCLC. METHODS: By applying texture analysis, we retrospectively analyzed 72 patients with advanced NSCLC. The datasets were randomly split into a training cohort (2/3) and a validation cohort (1/3). Forty radiomic features were extracted by manually drawing tumor volumes of interest (VOIs) on baseline contrast-enhanced CT. After selecting features on the training cohort, two predictive models were created using binary logistic regression, one for PD-L1 values ≥ 50% and the other for values between 1 and 49%. The two models were analyzed with ROC curves and tested in the validation cohort. RESULTS: The Radiomic Score (Rad-Score) for PD-L1 values ≥ 50%, which consisted of Skewness and Low Gray-Level Zone Emphasis (GLZLM_LGZE), presented a cut-off value of - 0.745 with an area under the curve (AUC) of 0.811 and 0.789 in the training and validation cohort, respectively. The Rad-Score for PD-L1 values between 1 and 49% consisted of Sphericity, Skewness, Conv_Q3 and Gray Level Non-Uniformity (GLZLM_GLNU), showing a cut-off value of 0.111 with AUC of 0.763 and 0.806 in the two population, respectively. CONCLUSION: Rad-Scores obtained from CT texture analysis could be useful for predicting PD-L1 expression and guiding the therapeutic choice in patients with advanced NSCLC.

Radiomics in cervical and endometrial cancer.

Radiomics is an emerging field of research that aims to find associations between quantitative information extracted from imaging examinations and clinical data to support the best clinical decision. In the last few years, some papers have been evaluating the role of radiomics in gynecological malignancies, mainly focusing on ovarian cancer. Nonetheless, cervical cancer is the most frequent gynecological malignancy in developing countries and endometrial cancer is the most common in western countries. The purpose of this narrative review is to give an overview of the latest published papers evaluating the role of radiomics in cervical and endometrial cancer, mostly evaluating association with tumor prognostic factors, with response to therapy and with prediction of recurrence and distant metastasis.

Quantitative diffusion and perfusion MRI in the evaluation of endometrial cancer: validation with histopathological parameters.

OBJECTIVES: To investigate the role of quantitative Magnetic Resonance Imaging (MRI) in preoperative assessment of tumour aggressiveness in patients with endometrial cancer, correlating multiple parameters obtained from diffusion and dynamic contrast-enhanced (DCE) MR sequences with conventional histopathological prognostic factors and inflammatory tumour infiltrate. METHODS: Forty-four patients with biopsy-proven endometrial cancer underwent preoperative MR imaging at 3T scanner, including DCE imaging, diffusion-weighted imaging (DWI) and intravoxel incoherent motion imaging (IVIM). Images were analysed on dedicated post-processing workstations and quantitative parameters were extracted: Ktrans, Kep, Ve and AUC from the DCE; ADC from DWI; diffusion D, pseudo diffusion D*, perfusion fraction f from IVIM and tumour volume from DWI. The following histopathological data were obtained after surgery: histological type, grading (G), lympho-vascular invasion (LVI), lymph node status, FIGO stage and inflammatory infiltrate. RESULTS: ADC was significantly higher in endometrioid histology, G1-G2 (low grade), and stage IA. Significantly higher D* were found in endometrioid subptype, negative lymph nodes and stage IA. The absence of LVI is associated with higher f values. Ktrans and Ve values were significantly higher in low grade. Higher D*, f and AUC occur with the presence of chronic inflammatory cells, D * was also able to distinguish chronic from mixed type of inflammation. Larger volume was significantly correlated with the presence of mixed-type inflammation, LVI, positive lymph nodes and stage ≥IB. CONCLUSIONS: Quantitative biomarkers obtained from pre-operative DWI, IVIM and DCE-MR examination are an in vivo representation of the physiological and microstructural characteristics of endometrial carcinoma allowing to obtain the fundamental parameters for stratification into Risk Classes. ADVANCES IN KNOWLEDGE: Quantitative imaging biomarkers obtained from DWI, DCE and IVIM may improve preoperative prognostic stratification in patients with endometrial cancer leading to a more informed therapeutic choice.

When to ask for an MRI of the scrotum.

BACKGROUND: Multiparametric MRI (mpMRI) of the scrotum has been established as a useful second-line diagnostic tool for the investigation of scrotal diseases. Recently, recommendations on clinical indications for scrotal MRI were issued by the Scrotal and Penile Imaging Working Group of the European Society of Urogenital Radiology. OBJECTIVE: To update current research on when to ask for an MRI of the scrotum. METHODS: PubMed database was searched for original articles and reviews published during 2010-2021. RESULTS: Eighty-three articles fulfilled the search criteria. Scrotal MRI is mainly recommended after inconclusive US findings or inconsistent with the clinical examination and should be asked in the following cases: differentiation between intratesticular and paratesticular lesions (in rare cases of uncertain US findings), characterization of paratesticular and intratesticular lesions (when US findings are indeterminate), discrimination between germ cell and sex cord-stromal testicular tumors, local staging of testicular malignancies (in patients planned for testis-sparing surgery), differentiation between seminomas and non-seminomatous tumors (when immediate chemotherapy is planned and orchiectomy is delayed), assessment of acute scrotum and scrotal trauma (rarely needed, in cases of non-diagnostic US findings) and detection and localization of undescended testes (in cases of inconlusive US findings). Although preliminary data show promising results in the evaluation of male infertility, no established role for mpMRI still exists. CONCLUSION: Multiparametric MRI of the scrotum, by assessing morphologic and functional data represents a valuable problem-solving tool, helping to improve our understanding on the nature of scrotal pathology and the process of spermatogenesis. The technique may improve patient care and reduce the number of unnecessary surgical procedures.

Magnetic Resonance Imaging in endometriosis-associated pain.

INTRODUCTION: Endometriosis affects 10%-15% of women in reproductive age and may cause no-cyclic chronic pelvic pain, dysmenorrhea, dyspareunia, urinary tract symptoms, and it is frequently associated with infertility. The peak of incidence is between 24 and 29 years old and the clinical diagnosis of endometriosis is generally delayed by 6-7 years. Laparoscopy with surgical biopsies is the "gold standard" for the diagnosis of endometriosis, with histological verification of endometrial ectopic glands and/or stroma. However, nowadays two different non-invasive modalities are routinely used for a presumptive diagnosis: Transvaginal Ultrasound (TVUS) and Magnetic Resonance Imaging (MRI). EVIDENCE ACQUISITION: A structured search using PubMed was performed starting from October 2020 and including all relevant original and review articles published since 2000. The search used the following key word combinations: "endometriosis MRI" AND "DIE and MRI" (45); "MRI endometriosis and pelvic pain" OR "endometriosis and MRI technical development" (296). Ultimately, 87 articles were deemed relevant and used as the literature basis of this review. EVIDENCE SYNTHESIS: TVUS represents the first imaging approach for endometriosis showing a good diagnostic performance, but it is highly operator dependent. MRI is a second level examination often used in complex cases indeterminate after TVUS and in preoperative planning. MRI is considered the best imaging technique for mapping endometriosis since it provides a more reliable map of deep infiltrating endometriosis than physical examination and transvaginal ultrasound. We have analyzed and described the main forms of endometriosis: adnexal endometriosis, adenomyosis, peritoneal implants and deep infiltrating endometriosis, showing their appearance in the two imaging modalities. CONCLUSIONS: Endometriosis is one of the most common gynecologic disorders correlated to chronic pelvic pain whose treatment is still today complex and controversial. In this context, MRI has become an important additional non-invasive tool to investigate cases of chronic pelvic pain related to deep infiltrating endometriosis (DIE) with or without neural involvement.

Computed Tomography Based Radiomics as a Predictor of Survival in Ovarian Cancer Patients: A Systematic Review.

The objective of this systematic review was to assess the results of radiomics for prediction of overall survival (OS) and progression free survival (PFS) in ovarian cancer (OC) patients. A secondary objective was to evaluate the findings of papers that based their analyses on inter-site heterogeneity. This systematic review was conducted according to the PRISMA statement. After the initial retrieval of 145 articles, the final systematic review comprised six articles. Association between radiomic features and OS was evaluated in 3/6 studies (50%); all articles showed a significant association between radiomic features and OS. Association with PFS was evaluated in 5/6 (83%) articles; the period of follow-up ranged between six and 36 months. All the articles showed significant association between radiomic models and PFS. Inter-site textural features were used for analysis in 2/6 (33%) articles. They demonstrated that high levels of inter-site textural heterogeneity were significantly associated with incomplete surgical resection in breast cancer gene-negative patients, and that lower heterogeneity was associated with complete resectability. There were some differences among papers in methodology; for example, only 3/6 (50%) articles included validation cohorts. In conclusion, radiomic models have demonstrated promising results as predictors of survival in OC patients, although larger studies are needed to allow clinical applicability.