Using rADioMIcs and machine learning with ultrasonography for the differential diagnosis of myometRiAL tumors (the ADMIRAL pilot study). Radiomics and differential diagnosis of myometrial tumors

Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review

The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.

Prevalence of sonographic signs in women with uterine sarcoma: a systematic review and meta-analysis

OBJECTIVE

To assess the prevalence of sonographic signs in women with uterine sarcoma.

MATERIALS AND METHODS

A systematic review and meta-analysis were performed. Five electronic databases were searched from inception to June 2022 for all studies allowing calculation of the prevalence of sonographic signs in women with uterine sarcoma. Pooled prevalence with 95% confidence intervals was calculated for each sonographic sign and was a priori defined as "very high" when it was ≥ 80%, "high" when it ranged from 80% to 70%, and less relevant when it was ≤ 70%.

RESULTS

6 studies with 317 sarcoma patients were included. The pooled prevalence was: · 25.0% (95%CI:15.4-37.9%) for absence of visibility of the myometrium. · 80.5% (95%CI:74.8-85.2%) for solid component. · 78.3% (95%CI:59.3-89.9%) for inhomogeneous echogenicity of solid component. · 47.9% (95%CI:41.1-54.8%) for cystic areas. · 80.7% (95%CI:68.3-89.0%) for irregular walls of cystic areas. · 72.3% (95%CI:16.7-97.2%) for anechoic cystic areas. · 54.8% (95%CI:34.0-74.1%) for absence of shadowing. · 73.5% (95%CI:43.3-90.9%) for absence of calcifications. · 48.7% (95%CI:18.6-79.8%) for color score 3 or 4. · 47.3% (95%CI:37.0-57.8%) for irregular tumor borders. · 45.4% (95%CI:27.6-64.3%) for endometrial cavity not visualizable. · 10.9% (95%CI:3.5-29.1%) for free pelvic fluid. · 6.4% (95%CI:1.1-30.2%) for ascites. · 21.2% (95%CI:2.1-76.8%) for intracavitary process. · 81.5% (95%CI:56.1-93.8%) for singular lesion..

CONCLUSION

Solid component, irregular walls of cystic areas, and singular lesions are signs with very high prevalence, while inhomogeneous echogenicity of solid component, anechoic cystic areas, and absence of calcifications are signs with high prevalence. The remaining signs were less relevant.

Ultrasound features using MUSA terms and definitions in uterine sarcoma and leiomyoma: cohort study

OBJECTIVES

Timely and accurate preoperative diagnosis of uterine sarcoma will increase patient survival. The primary aim of this study was to describe the ultrasound features of uterine sarcoma compared with those of uterine leiomyoma based on the terms and definitions of the Morphological Uterus Sonographic Assessment (MUSA) group. A secondary aim was to assess the interobserver agreement for reporting on ultrasound features according to MUSA terminology.

METHODS

This was a retrospective cohort study of patients with uterine sarcoma or uterine leiomyoma treated in a single tertiary center during the periods 1997-2019 and 2016-2019, respectively. Demographic characteristics, presenting symptoms and surgical outcomes were extracted from patients' files. Ultrasound images were re-evaluated independently by two sonologists using MUSA terms and definitions. Descriptive statistics were calculated and interobserver agreement was assessed using Cohen's κ (with squared weights) or intraclass correlation coefficient, as appropriate.

RESULTS

A total of 107 patients were included, of whom 16 had a uterine sarcoma and 91 had a uterine leiomyoma. Abnormal uterine bleeding was the most frequent presenting symptom (69/107 (64%)). Compared with leiomyoma cases, patients with uterine sarcoma were older (median age, 65 (interquartile range (IQR), 60-70) years vs 48 (IQR, 43-52) years) and more likely to be postmenopausal (13/16 (81%) vs 15/91 (16%)). In the uterine sarcoma cohort, leiomyosarcoma was the most frequent histological type (6/16 (38%)), followed by adenosarcoma (4/16 (25%)). On ultrasound evaluation, according to Observers 1 and 2, the tumor border was irregular in most sarcomas (11/16 (69%) and 13/16 (81%) cases, respectively), but regular in most leiomyomas (65/91 (71%) and 82/91 (90%) cases, respectively). Lesion echogenicity was classified as non-uniform in 68/91 (75%) and 51/91 (56%) leiomyomas by Observers 1 and 2, respectively, and 15/16 (94%) uterine sarcomas by both observers. More than 60% of the uterine sarcomas showed acoustic shadows (11/16 (69%) and 10/16 (63%) cases by Observers 1 and 2, respectively), whereas calcifications were reported in a small minority (0/16 (0%) and 2/16 (13%) cases by Observers 1 and 2, respectively). In uterine sarcomas, intralesional vascularity was reported as moderate to abundant in 13/16 (81%) cases by Observer 1 and 15/16 (94%) cases by Observer 2, while circumferential vascularity was scored as moderate to abundant in 6/16 (38%) by both observers. Interobserver agreement for the presence of cystic areas, calcifications, acoustic shadow, central necrosis, color score (overall, intralesional and circumferential) and maximum diameter of the lesion was moderate. The agreement for shape of lesion, tumor border and echogenicity was fair.

CONCLUSIONS

A postmenopausal patient presenting with abnormal uterine bleeding and a new or growing mesenchymal mass with irregular tumor borders, moderate-to-abundant intralesional vascularity, cystic areas and an absence of calcifications on ultrasonography is at a higher risk of having a uterine sarcoma. Interobserver agreement for most MUSA terms and definitions is moderate. Future studies should validate the abovementioned clinical and ultrasound findings on uterine mesenchymal tumors in a prospective multicenter fashion. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study

BACKGROUND

The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas.

METHODS

Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data.

RESULTS

A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62-0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65-0.85).

CONCLUSIONS

CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.

Diagnostic accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas: A systematic review and meta-analysis

BACKGROUND

Differential diagnosis between uterine leiomyomas and sarcomas is challenging. Magnetic resonance imaging (MRI) represents the second-line diagnostic method after ultrasound for the assessment of uterine masses.

OBJECTIVES

To assess the accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas.

SEARCH STRATEGY

A systematic review and meta-analysis was performed searching five electronic databases from their inception to June 2023.

SELECTION CRITERIA

All peer-reviewed observational or randomized clinical trials that reported an unbiased postoperative histologic diagnosis of uterine leiomyoma or uterine sarcoma, which also comprehended a preoperative MRI evaluation of the uterine mass.

DATA COLLECTION AND ANALYSIS

Sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the curve on summary receiver operating characteristic of MRI in differentiating uterine leiomyomas and sarcomas were calculated as individual and pooled estimates, with 95% confidence intervals (CI).

RESULTS

Eight studies with 2495 women (2253 with uterine leiomyomas and 179 with uterine sarcomas), were included. MRI showed pooled sensitivity of 0.90 (95% CI 0.84-0.94), specificity of 0.96 (95% CI 0.96-0.97), positive likelihood ratio of 13.55 (95% CI 6.20-29.61), negative likelihood ratio of 0.08 (95% CI 0.02-0.32), diagnostic odds ratio of 175.13 (95% CI 46.53-659.09), and area under the curve of 0.9759.

CONCLUSIONS

MRI has a high diagnostic accuracy in the differential diagnosis between uterine leiomyomas and sarcomas.

Diagnostic Accuracy of Transvaginal Ultrasound and Magnetic Resonance Imaging for the Detection of Myometrial Infiltration in Endometrial Cancer: A Systematic Review and Meta-Analysis

In endometrial cancer (EC), deep myometrial invasion (DMI) is a prognostic factor that can be evaluated by various imaging methods; however, the best method of choice is uncertain. We aimed to compare the diagnostic performance of two-dimensional transvaginal ultrasound (TVS) and magnetic resonance imaging (MRI) in the preoperative detection of DMI in patients with EC. Pubmed, Embase and Cochrane Library were systematically searched in May 2023. We included original articles that compared TVS to MRI on the same cohort of patients, with final histopathological confirmation of DMI as reference standard. Several subgroup analyses were performed. Eighteen studies comprising 1548 patients were included. Pooled sensitivity and specificity were 76.6% (95% confidence interval (CI), 70.9-81.4%) and 87.4% (95% CI, 80.6-92%) for TVS. The corresponding values for MRI were 81.1% (95% CI, 74.9-85.9%) and 83.8% (95% CI, 79.2-87.5%). No significant difference was observed (sensitivity: p = 0.116, specificity: p = 0.707). A non-significant difference between TVS and MRI was observed when no-myometrium infiltration vs. myometrium infiltration was considered. However, when only low-grade EC patients were evaluated, the specificity of MRI was significantly better (p = 0.044). Both TVS and MRI demonstrated comparable sensitivity and specificity. Further studies are needed to assess the presence of myometrium infiltration in patients with fertility-sparing wishes.

Diagnostic Accuracy of Ultrasound in the Diagnosis of Uterine Leiomyomas and Sarcomas

BACKGROUND

Differential diagnosis between uterine leiomyomas and sarcomas is challenging. Ultrasound shows an uncertain role in the clinical practice given that pooled estimates about its diagnostic accuracy are lacking.

OBJECTIVES

To assess the accuracy of ultrasound in the differential diagnosis between uterine leiomyomas and sarcomas.

DATA SOURCES

A systematic review was performed searching 5 electronic databases (MEDLINE, Web of Sciences, Google Scholar, Scopus, and ClinicalTrial.gov) from their inception to June 2023.

METHODS OF STUDY SELECTION

All peer-reviewed observational or randomized clinical trials that reported an unbiased postoperative histologic diagnosis of uterine leiomyoma or uterine sarcoma that also comprised a preoperative ultrasonographic evaluation of the uterine mass.

TABULATION, INTEGRATION, AND RESULTS

Sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the curve on summary receiver operating characteristic were calculated for each included study and as pooled estimate, with 95% confidence interval (CI); 972 women (694 with uterine leiomyomas and 278 with uterine sarcomas) were included. Ultrasound showed pooled sensitivity of 0.76 (95% CI, 0.70-0.81), specificity of 0.89 (95% CI, 0.87-0.92), positive and negative likelihood ratios of 6.65 (95% CI, 4.45-9.93) and 0.26 (95% CI, 0.07-1.0) respectively, diagnostic odds ratio of 23.06 (95% CI, 4.56-116.53), and area under the curve of 0.8925.

CONCLUSIONS

Ultrasound seems to have only a moderate diagnostic accuracy in the differential diagnosis between uterine leiomyomas and sarcomas, with a lower sensitivity than specificity.

Survivin-Sodium Iodide Symporter Reporter as a Non-Invasive Diagnostic Marker to Differentiate Uterine Leiomyosarcoma from Leiomyoma

Leiomyosarcoma (LMS) has been challenging to diagnose because of limitations in clinical and radiographic predictors, as well as the lack of reliable serum or urinary biomarkers. Most uterine masses consist of benign leiomyoma (LM). However, it is currently a significant challenge in gynecology practice to differentiate LMS from LM. This inability poses grave consequences for patients, leading to a high number of unnecessary hysterectomies, infertility, and other major morbidities and possible mortalities. This study aimed to evaluate the use of Survivin-Sodium iodide symporter (Ad-Sur-NIS) as a reporter gene biomarker to differentiate malignant LMS from benign LM by using an F18-NaBF4 PET/CT scan. The PET/CT scan images showed a significantly increased radiotracer uptake and a decreased radiotracer decay attributable to the higher abundance of Ad-Sur-NIS in the LMS tumors compared to LM (p < 0.05). An excellent safety profile was observed, with no pathological or metabolic differences detected in Ad-Sur-NIS-treated animal versus the vehicle control. Ad-Sur-NIS as a PET scan reporter is a promising imaging biomarker that can differentiate uterine LMS from LM using F18-NaBF4 as a radiotracer. As a new diagnostic method, the F18 NaBF4 PET/CT scan can provide a much-needed tool in clinical practices to effectively triage women with suspicious uterine masses and avoid unnecessary invasive interventions.

Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology

As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.

Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer

Neoadjuvant chemotherapy plus radical surgery could be a safe alternative to chemo-radiation in cervical cancer patients who are not willing to receive radiotherapy. The response to neoadjuvant chemotherapy is the main factor influencing the need for adjunctive treatments and survival. In the present paper we aim to develop a machine learning model based on cervix magnetic resonance imaging (MRI) images to stratify the single-subject risk of cervical cancer. We collected MRI images from 72 subjects. Among these subjects, 28 patients (38.9%) belonged to the "Not completely responding" class and 44 patients (61.1%) belonged to the 'Completely responding' class according to their response to treatment. This image set was used for the training and cross-validation of different machine learning models. A robust radiomic approach was applied, under the hypothesis that the radiomic features could be able to capture the disease heterogeneity among the two groups. Three models consisting of three ensembles of machine learning classifiers (random forests, support vector machines, and k-nearest neighbor classifiers) were developed for the binary classification task of interest ("Not completely responding" vs. "Completely responding"), based on supervised learning, using response to treatment as the reference standard. The best model showed an ROC-AUC (%) of 83 (majority vote), 82.3 (mean) [79.9-84.6], an accuracy (%) of 74, 74.1 [72.1-76.1], a sensitivity (%) of 71, 73.8 [68.7-78.9], and a specificity (%) of 75, 74.2 [71-77.5]. In conclusion, our preliminary data support the adoption of a radiomic-based approach to predict the response to neoadjuvant chemotherapy.

A Radiomic-Based Machine Learning System to Diagnose Age-Related Macular Degeneration from Ultra-Widefield Fundus Retinography

The present study was conducted to investigate the potential of radiomics to develop an explainable AI-based system to be applied to ultra-widefield fundus retinographies (UWF-FRTs) with the objective of predicting the presence of the early signs of Age-related Macular Degeneration (AMD) and stratifying subjects with low- versus high-risk of AMD. The ultimate aim was to provide clinicians with an automatic classifier and a signature of objective quantitative image biomarkers of AMD. The use of Machine Learning (ML) and radiomics was based on intensity and texture analysis in the macular region, detected by a Deep Learning (DL)-based macular detector. Two-hundred and twenty six UWF-FRTs were retrospectively collected from two centres and manually annotated to train and test the algorithms. Notably, the combination of the ML-based radiomics model and the DL-based macular detector reported 93% sensitivity and 74% specificity when applied to the data of the centre used for external testing, capturing explainable features associated with drusen or pigmentary abnormalities. In comparison to the human operator's annotations, the system yielded a 0.79 Cohen κ, demonstrating substantial concordance. To our knowledge, these results are the first provided by a radiomic approach for AMD supporting the suitability of an explainable feature extraction method combined with ML for UWF-FRT.

Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors

BACKGROUND

To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors.

METHODS

We enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set.

RESULTS

 Among the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists.

CONCLUSIONS

 We developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.

Evaluating the Risk of Inguinal Lymph Node Metastases before Surgery Using the Morphonode Predictive Model: A Prospective Diagnostic Study in Vulvar Cancer Patients

Ultrasound examination is an accurate method in the preoperative evaluation of the inguinofemoral lymph nodes when performed by experienced operators. The purpose of the study was to build a robust, multi-modular model based on machine learning to discriminate between metastatic and non-metastatic inguinal lymph nodes in patients with vulvar cancer. One hundred and twenty-seven women were selected at our center from March 2017 to April 2020, and 237 inguinal regions were analyzed (75 were metastatic and 162 were non-metastatic at histology). Ultrasound was performed before surgery by experienced examiners. Ultrasound features were defined according to previous studies and collected prospectively. Fourteen informative features were used to train and test the machine to obtain a diagnostic model (Morphonode Predictive Model). The following data classifiers were integrated: (I) random forest classifiers (RCF), (II) regression binomial model (RBM), (III) decisional tree (DT), and (IV) similarity profiling (SP). RFC predicted metastatic/non-metastatic lymph nodes with an accuracy of 93.3% and a negative predictive value of 97.1%. DT identified four specific signatures correlated with the risk of metastases and the point risk of each signature was 100%, 81%, 16% and 4%, respectively. The Morphonode Predictive Model could be easily integrated into the clinical routine for preoperative stratification of vulvar cancer patients.

Machine learning applications in gynecological cancer: A critical review

Machine Learning (ML) represents a computer science capable of generating predictive models, by exposure to raw, training data, without being rigidly programmed. Over the last few years, ML has gained attention within the field of oncology, with considerable strides in both diagnostic, predictive, and prognostic spectrum of malignancies, but also as a catalyst of cancer research. In this review, we discuss the state of ML applications on gynecologic oncology and systematically address major technical and ethical concerns, with respect to their real-world medical practice translation. Undoubtedly, advances in ML will enable the analysis of large, rather complex, datasets for improved, cost-effective, and efficient clinical decisions.

Advances in the Preoperative Identification of Uterine Sarcoma

Simple Summary

As a lethal malignant tumor, uterine sarcomas lack specific diagnostic criteria due to their similar presentation with uterine fibroids, clinicians are prone to make the wrong diagnosis or adopt incorrect treatment methods, which leads to rapid tumor progression and increased metastatic propensity. In recent years, with the improvement of medical level and awareness of uterine sarcoma, more and more studies have proposed new methods for preoperative differentiation of uterine sarcoma and uterine fibroids. This review outlines the up-to-date knowledge about preoperative differentiation of uterine sarcoma and uterine fibroids, including laboratory tests, imaging examinations, radiomics and machine learning-related methods, preoperative biopsy, integrated model and other relevant emerging technologies, and provides recommendations for future research.

Abstract

Uterine sarcomas are rare malignant tumors of the uterus with a high degree of malignancy. Their clinical manifestations, imaging examination findings, and laboratory test results overlap with those of uterine fibroids. No reliable diagnostic criteria can distinguish uterine sarcomas from other uterine tumors, and the final diagnosis is usually only made after surgery based on histopathological evaluation. Conservative or minimally invasive treatment of patients with uterine sarcomas misdiagnosed preoperatively as uterine fibroids will shorten patient survival. Herein, we will summarize recent advances in the preoperative diagnosis of uterine sarcomas, including epidemiology and clinical manifestations, laboratory tests, imaging examinations, radiomics and machine learning-related methods, preoperative biopsy, integrated model and other relevant emerging technologies.

Deep learning for emergency ascites diagnosis using ultrasonography images

PURPOSE

The detection of abdominal free fluid or hemoperitoneum can provide critical information for clinical diagnosis and treatment, particularly in emergencies. This study investigates the use of deep learning (DL) for identifying peritoneal free fluid in ultrasonography (US) images of the abdominal cavity, which can help inexperienced physicians or non-professional people in diagnosis. It focuses specifically on first-response scenarios involving focused assessment with sonography for trauma (FAST) technique.

METHODS

A total of 2985 US images were collected from ascites patients treated from 1 January 2016 to 31 December 2017 at the Shenzhen Second People's Hospital. The data were categorized as Ascites-1, Ascites-2, or Ascites-3, based on the surrounding anatomy. A uniform standard for regions of interest (ROIs) and the lack of obstruction from acoustic shadow was used to classify positive samples. These images were then divided into training (90%) and test (10%) datasets to evaluate the performance of a U-net model, utilizing an encoder-decoder architecture and contracting and expansive paths, developed as part of the study.

RESULTS

Test results produced sensitivity and specificity values of 94.38% and 68.13%, respectively, in the diagnosis of Ascites-1 US images, with an average Dice coefficient of 0.65 (standard deviation [SD] = 0.21). Similarly, the sensitivity and specificity for Ascites-2 were 97.12% and 86.33%, respectively, with an average Dice coefficient of 0.79 (SD = 0.14). The accuracy and area under the curve (AUC) were 81.25% and 0.76 for Ascites-1 and 91.73% and 0.91 for Ascites-2.

CONCLUSION

The results produced by the U-net demonstrate the viability of DL for automated ascites diagnosis. This suggests the proposed technique could be highly valuable for improving FAST-based preliminary diagnoses, particularly in emergency scenarios.

Preoperative Differentiation of Uterine Leiomyomas and Leiomyosarcomas: Current Possibilities and Future Directions

The distinguishing of uterine leiomyosarcomas (ULMS) and uterine leiomyomas (ULM) before the operation and histopathological evaluation of tissue is one of the current challenges for clinicians and researchers. Recently, a few new and innovative methods have been developed. However, researchers are trying to create different scales analyzing available parameters and to combine them with imaging methods with the aim of ULMs and ULM preoperative differentiation ULMs and ULM. Moreover, it has been observed that the technology, meaning machine learning models and artificial intelligence (AI), is entering the world of medicine, including gynecology. Therefore, we can predict the diagnosis not only through symptoms, laboratory tests or imaging methods, but also, we can base it on AI. What is the best option to differentiate ULM and ULMS preoperatively? In our review, we focus on the possible methods to diagnose uterine lesions effectively, including clinical signs and symptoms, laboratory tests, imaging methods, molecular aspects, available scales, and AI. In addition, considering costs and availability, we list the most promising methods to be implemented and investigated on a larger scale.

A decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA-125

BACKGROUND

To evaluate the performance of a decision support system (DSS) based on radiomics and machine learning in predicting the risk of malignancy of ovarian masses (OMs) from transvaginal ultrasonography (TUS) and serum CA-125.

METHODS

A total of 274 consecutive patients who underwent TUS (by different examiners and with different ultrasound machines) and surgery, with suspicious OMs and known CA-125 serum level were used to train and test a DSS. The DSS was used to predict the risk of malignancy of these masses (very low versus medium-high risk), based on the US appearance (solid, liquid, or mixed) and radiomic features (morphometry and regional texture features) within the masses, on the shadow presence (yes/no), and on the level of serum CA-125. Reproducibility of results among the examiners, and performance accuracy, sensitivity, specificity, and area under the curve were tested in a real-world clinical setting.

RESULTS

The DSS showed a mean 88% accuracy, 99% sensitivity, and 77% specificity for the 239 patients used for training, cross-validation, and testing, and a mean 91% accuracy, 100% sensitivity, and 80% specificity for the 35 patients used for independent testing.

CONCLUSIONS

This DSS is a promising tool in women diagnosed with OMs at TUS, allowing to predict the individual risk of malignancy, supporting clinical decision making.

A review in radiomics: Making personalized medicine a reality via routine imaging

Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.