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Pediatric sarcomas, rare malignancies of mesenchymal origin, pose diagnostic and therapeutic challenges. In this review, we explore the role of radiomics in reshaping our understanding of pediatric sarcomas, emphasizing methodological considerations and applications such as diagnostics and predictive modeling. A systematic review conducted up to November 2023 identified 72 papers on radiomics analysis in pediatric sarcoma from PubMed/MEDLINE, Web of Knowledge, and Scopus. Following inclusion and exclusion criteria, 10 reports were included in this review. The studies, predominantly retrospective, focus on Ewing sarcoma and osteosarcoma, utilizing diverse imaging modalities, including CT, MRI, PET/CT, and PET/MRI. Manual segmentation is common, with a median of 35 features extracted. Radiomics Quality Score (RQS) and Methodological Radiomics Score (METRICS) assessments reveal a consistent emphasis on non-radiomic features, validation criteria, and improved methodological rigor in recent publications. Diagnostic applications dominate, with innovative studies exploring prognostic and treatment response aspects. Challenges include feature heterogeneity and sample size variations. The evolving landscape underscores the need for standardized methodologies. Despite challenges, the diagnostic and predictive potential of radiomics in pediatric oncology is evident, paving the way for precision medicine advancements.
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Introduction: Immune checkpoint inhibitors (ICIs) represent the standard therapy for metastatic melanoma. However, a few patients do not respond to ICIs and reliable predictive biomarkers are needed. Methods: This pilot study investigates the association between mRNA levels of programmed cell death-1 (PD-1) ligand 1 (PD-L1), interferon-gamma (IFN-γ), and transforming growth factor-ß (TGF-ß) in circulating extracellular vesicles (EVs) and survival in 30 patients with metastatic melanoma treated with first line anti-PD-1 antibodies. Blood samples were collected at baseline and RNA extracted from EVs; the RNA levels of PD-L1, IFN-γ, and TGF-ß were analysed by digital droplet PCR (ddPCR). A biomarker-radiomic correlation analysis was performed in a subset of patients. Results: Patients with high TGF-ß expression (cut-off fractional abundance [FA] >0.19) at baseline had longer median progression-free survival (8.4 vs. 1.8 months; p = 0.006) and overall survival (17.9 vs. 2.63 months; p = 0.0009). Moreover, radiomic analysis demonstrated that patients with high TGF-ß expression at baseline had smaller lesions (2.41 ± 3.27 mL vs. 42.79 ± 101.08 mL, p < 0.001) and higher dissimilarity (12.01 ± 28.23 vs. 5.65 ± 8.4; p = 0.018). Discussion: These results provide evidence that high TGF-ß expression in EVs is associated with a better response to immunotherapy. Further investigation on a larger patient population is needed to validate the predictive power of this potential biomarker of response to ICIs.
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Breast cancer, a major contributor to female mortality globally, presents challenges in detection, prompting exploration beyond digital mammography. Contrast-Enhanced Mammography (CEM), integrating morphological and functional information, emerges as a promising alternative, offering advantages in cost-effectiveness and reduced anxiety compared to MRI. This study investigates CEM's correlation with breast cancer prognostic factors, encompassing histology, grade, and molecular markers. In a retrospective analysis involving 114 women, CEM revealed diverse lesion characteristics. Statistical analyses identified correlations between specific CEM features, such as spiculated margins and irregular shape, and prognostic factors like tumor grade and molecular markers. Notably, spiculated margins predicted lower grade and HER2 status, while irregular shape correlated with PgR and Ki-67 status. The study emphasizes CEM's potential in predicting breast cancer prognosis, shedding light on tumor behavior. Despite the limitations, including sample size and single-observer analysis, the findings advocate for CEM's role in stratifying breast cancers based on biological characteristics. CEM features, particularly spiculated margins, irregular shape, and enhancement dynamics, may serve as valuable indicators for personalized treatment decisions. Further research is crucial to validate these correlations and enhance CEM's clinical utility in breast cancer assessment.
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Pleural mesothelioma (PM) is an aggressive disease that has a strong causal relationship with asbestos exposure and represents a major challenge from both a diagnostic and therapeutic viewpoint. Despite recent improvements in patient care, PM typically carries a poor outcome, especially in advanced stages. Therefore, a timely and effective diagnosis taking advantage of currently available imaging techniques is essential to perform an accurate staging and dictate the most appropriate treatment strategy. Our aim is to provide a brief, but exhaustive and up-to-date overview of the role of multimodal medical imaging in the management of PM.
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Mesotelioma , Neoplasias Pleurais , Humanos , Estadiamento de Neoplasias , Mesotelioma/diagnóstico por imagem , Mesotelioma/etiologia , Neoplasias Pleurais/diagnóstico por imagem , Neoplasias Pleurais/patologia , Fatores de Risco , Imagem MultimodalRESUMO
Prognosis in advanced gastric cancer (aGC) is predicted by clinical factors, such as stage, performance status, metastasis location, and the neutrophil-to-lymphocyte ratio. However, the role of body composition and sarcopenia in aGC survival remains debated. This study aimed to evaluate how abdominal visceral and subcutaneous fat volumes, psoas muscle volume, and the visceral-to-subcutaneous (VF/SF) volume ratio impact overall survival (OS) and progression-free survival (PFS) in aGC patients receiving first-line palliative chemotherapy. We retrospectively examined CT scans of 65 aGC patients, quantifying body composition parameters (BCPs) in 2D and 3D. Normalized 3D BCP volumes were determined, and the VF/SF ratio was computed. Survival outcomes were analyzed using the Cox Proportional Hazard model between the upper and lower halves of the distribution. Additionally, response to first-line chemotherapy was compared using the χ2 test. Patients with a higher VF/SF ratio (N = 33) exhibited significantly poorer OS (p = 0.02) and PFS (p < 0.005) and had a less favorable response to first-line chemotherapy (p = 0.033), with a lower Disease Control Rate (p = 0.016). Notably, absolute BCP measures and sarcopenia did not predict survival. In conclusion, radiologically assessed VF/SF volume ratio emerged as a robust and independent predictor of both survival and treatment response in aGC patients.
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PURPOSE: The objective of this study was to assess the inappropriateness rate of oncological follow-up CT examinations. METHODS: Out of 7.000 oncology patients referred for follow-up CT examinations between March and October 2022, a random sample of 10 % was included. Radiology residents assessed the appropriateness using the Italian Society of Medical Oncology (AIOM) guidelines, supervised by senior radiologists. Association between inappropriateness and clinical variables was investigated and variables influencing inappropriateness were analyzed through a binary logistic regression. RESULTS: Three-hundred-eighty-eight examinations (56.1 %) were consistent with AIOM guidelines. An additional 100 (14.5 %) examinations did not follow the recommended schedule but were nevertheless considered appropriate because of suspected recurrence/progression (10.7 %) or adverse event requiring imaging assessment (3.8 %). Two-hundred-four (29.4 %) examinations were rated as inappropriate. Inappropriateness causes were as follows: CT not included in the relevant guideline (n = 47); CT extended to additional anatomical regions (n = 59); CT requested at a shorter time-interval (n = 98). No statistically significant difference was found in age, sex, scan region, and primary cancer between appropriate and inappropriate examinations. The only variable significantly associated with inappropriateness was being referred by a specific hospital unit named "unit 2" in the study (p = 0.009), which was demonstrated to be the only appropriateness independent predictor (OR 1.952). CONCLUSION: This study shows that majority of oncological patients referred for follow-up CT follows standard guidelines. However, a non-negligible proportion was rated as inappropriate, mainly due to the shorter time-interval. No clinical variable was associated with inappropriateness, except for referral by a specific hospital unit.
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Oncologia , Exame Físico , Humanos , Estudos Transversais , Seguimentos , Tomografia Computadorizada por Raios XRESUMO
Aims: Evaluating the prognostic role of radiomic features in liver-limited metastatic colorectal cancer treated with first-line therapy at baseline and best response among patients undergoing resection. Patients & methods: Among patients enrolled in TRIBE2 (NCT02339116), the association of clinical and radiomic data, extracted by SOPHiA-DDM™ with progression-free and overall survival (OS) in the overall population and with disease-free survival/postresection OS in those undergoing resection was investigated. Results: Among 98 patients, radiomic parameters improved the prediction accuracy of our model for OS (area under the curve: 0.83; sensitivity: 0.85; specificity: 0.73; accuracy: 0.78), but not progression-free survival. Of 46 resected patients, small-distance high gray-level emphasis was associated with shorter disease-free survival and high gray-level zone emphasis/higher kurtosis with shorter postresection OS. Conclusion: Radiomic features should be implemented as tools of outcome prediction for liver-limited metastatic colorectal cancer.
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Neoplasias do Colo , Neoplasias Colorretais , Neoplasias Hepáticas , Neoplasias Retais , Humanos , Bevacizumab , Prognóstico , Neoplasias Colorretais/patologia , Neoplasias do Colo/tratamento farmacológico , Neoplasias Hepáticas/terapia , Neoplasias Hepáticas/tratamento farmacológico , Neoplasias Retais/tratamento farmacológicoRESUMO
The spleen, often referred to as the "forgotten organ", plays numerous important roles in various diseases. Recently, there has been an increased interest in the application of radiomics in different areas of medical imaging. This systematic review aims to assess the current state of the art and evaluate the methodological quality of radiomics applications in spleen imaging. A systematic search was conducted on PubMed, Scopus, and Web of Science. All the studies were analyzed, and several characteristics, such as year of publication, research objectives, and number of patients, were collected. The methodological quality was evaluated using the radiomics quality score (RQS). Fourteen articles were ultimately included in this review. The majority of these articles were published in non-radiological journals (78%), utilized computed tomography (CT) for extracting radiomic features (71%), and involved not only the spleen but also other organs for feature extraction (71%). Overall, the included papers achieved an average RQS total score of 9.71 ± 6.37, corresponding to an RQS percentage of 27.77 ± 16.04. In conclusion, radiomics applications in spleen imaging demonstrate promising results in various clinical scenarios. However, despite all the included papers reporting positive outcomes, there is a lack of consistency in the methodological approaches employed.
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The aim of this systematic review was to evaluate the state of the art of radiomics in testicular imaging by assessing the quality of radiomic workflow using the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). A systematic literature search was performed to find potentially relevant articles on the applications of radiomics in testicular imaging, and 6 final articles were extracted. The mean RQS was 11,33 ± 3,88 resulting in a percentage of 31,48% ± 10,78%. Regarding QUADAS-2 criteria, no relevant biases were found in the included papers in the patient selection, index test, reference standard criteria and flow-and-timing domain. In conclusion, despite the publication of promising studies, radiomic research on testicular imaging is in its very beginning and still hindered by methodological limitations, and the potential applications of radiomics for this field are still largely unexplored.
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Background: The new immunotherapies have not only changed the oncological therapeutic approach but have also made it necessary to develop new imaging methods for assessing the response to treatment. Delta radiomics consists of the analysis of radiomic features variation between different medical images, usually before and after therapy. Purpose: This review aims to evaluate the role of delta radiomics in the immunotherapy response assessment. Methods: A systematic search was performed in PubMed, Scopus, and Web Of Science using "delta radiomics AND immunotherapy" as search terms. The included articles' methodological quality was measured using the Radiomics Quality Score (RQS) tool. Results: Thirteen articles were finally included in the systematic review. Overall, the RQS of the included studies ranged from 4 to 17, with a mean RQS total of 11,15 ± 4,18 with a corresponding percentage of 30.98 ± 11.61 %. Eleven articles out of 13 performed imaging at multiple time points. All the included articles performed feature reduction. No study carried out prospective validation, decision curve analysis, or cost-effectiveness analysis. Conclusions: Delta radiomics has been demonstrated useful in evaluating the response in oncologic patients undergoing immunotherapy. The overall quality was found law, due to the lack of prospective design and external validation. Thus, further efforts are needed to bring delta radiomics a step closer to clinical implementation.
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We sought to compare native T1 mapping to conventional late gadolinium enhancement (LGE) and T2-STIR techniques in a cohort of consecutive patients undergoing cardiac MRI (CMR). CMR was performed in 323 patients, 206 males (64%), mean age 54 ± 8 years, and in 27 age- and sex- matched healthy controls. In T2-STIR images, myocardial hyperintensity suggesting edema was found in 41 patients (27%). LGE images were positive in 206 patients (64%). T1 mapping was abnormal in 171 (49%). In 206 patients (64%), a matching between LGE and native T1 was found. T1 was abnormal in 32 out of 41 (78%) with edema in T2-STIR images. Overall, LGE and/or T2-STIR were abnormal in 209 patients, whereas native T1 was abnormal in 154 (52%). Conventional techniques and T1 mapping were concordant in 208 patients (64%). In 39 patients, T1 mapping was positive despite negative conventional techniques (12%). T1 mapping was able in conditions with diffuse myocardial damage such as cardiac amyloidosis, scleroderma, and Fabry disease (additive role in 42%). In contrast, T1 mapping was less effective in cardiac disease with regional distribution of myocardial damage such as myocardial infarction, HCM, and myocarditis. In conclusion, conventional LGE/T2-STIR and T1 mapping are complementary techniques and should be used together in every CMR examination.
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Late gadolinium enhancement (LGE) is the most relevant tool of cardiac magnetic resonance for tissue characterization, and it plays a pivotal role for diagnostic and prognostic assessment of cardiomyopathies. The pattern of presentation of LGE allows differential diagnosis between ischaemic and non-ischaemic heart disease with high diagnostic accuracy, and among different cardiomyopathies, specific presentation of LGE may help to make a diagnosis. Late gadolinium enhancement may be caused by conditions that significantly increase the interstitial space or, less frequently, that slow down Gd exit, like myocardial fibrosis. In chronic myocardial infarction, hypertrophic cardiomyopathies (HCM), dilated cardiomyopathy, Fabry disease, and other conditions, LGE is a marker of myocardial fibrosis, but also in patients with acute myocarditis where LGE may be also explained by the increase of interstitial space caused by interstitial oedema or by tissue infiltration of inflammatory cells. In cardiac amyloidosis, LGE represents myocardial fibrosis but the interstitial overload of amyloid proteins should also be considered as a potential cause of LGE. The identification of the pattern of presentation of LGE is also very important. In the ischaemic pattern, LGE always involves the subendocardial layer with more or less transmural extent, it is confluent, and every single scar should be located in the territory of one coronary artery. In the non-ischaemic pattern, LGE does not fulfil the previous criteria, being midwall, subepicardial, or mixed, not necessarily confluent or confined to a territory of one coronary artery. For cardiomyopathies, the exact pattern of non-ischaemic LGE is important. Quantitative analysis of LGE is required in some specific conditions as in HCM. Magnetic resonance imaging with LGE technique should be performed in every patient with suspect of cardiomyopathy. The lack of standardization of pulse sequence and mostly of quantification methods is the main limitation of LGE technique.
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PURPOSE: To assess the ability of apparent diffusion coefficient (ADC) measurements in predicting the histological grade of endometrial cancer. A secondary goal was to assess the agreement between MRI and surgical staging as an accurate measurement. METHODS: Patients with endometrial cancers diagnosed between 2018-2020 and having received both MRI and surgical staging were retrospectively enrolled. Patients were characterized according to histology, tumor size, FIGO stage (MRI and surgical stage), and functional MRI parameters (DCE and DWI/ADC). Statistical analysis was performed to determine if an association could be identified between ADC variables and histology grade. Secondarily, we assessed the degree of agreement between the MRI and surgical stages according to the FIGO classification. RESULTS: The cohort included 45 women with endometrial cancer. Quantitative analysis of ADC variables did not find a statistically significant association with histological tumor grades. DCE showed higher sensitivity than DWI/ADC in the assessment of myometrial invasion (85.00% versus 65.00%) with the same specificity (80.00%). A good agreement between MRI and histopathology for the FIGO stage was found (kappa of 0.72, p < 0.01). Differences in staging between MRI and surgery were detected in eight cases, which could not be justified by the interval between MRI and surgery. CONCLUSIONS: ADC values were not useful for predicting endometrial cancer grade, despite the good agreement between MRI interpretation and histopathology of endometrial cancer staging at our center.
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Tailoring treatment in patients with Hodgkin lymphoma (HL) is paramount to maximize outcomes while avoiding unnecessary toxicity. We aimed to compare the performance of SUVmax reduction (ΔSUVmax%) and the PET ratio (rPET) versus the Deauville score (DS) for assessing the chemotherapy response in pediatric HL patients undergoing 18F-FDG PET-CT. Fifty-two patients with biopsy-proven HL (aged 8-16 years) were enrolled at baseline, interim (after the second or third chemotherapy round) and post-therapy (on completion of first-line chemotherapy). Interim and post-therapy DS, ΔSUVmax% and rPET were compared as response predictors. Patients were classified as responders or non-responders based on a 24-month clinical follow-up. Interim DS showed a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of 100%, 80.4%, 100%, 40% and 82.7%, respectively, in predicting the therapy response. Post-therapy DS showed a sensitivity, specificity, PPV, NPV and accuracy of 66.7%, 97.8%, 95.7%, 80% and 94.2%, repsectively. Interim ΔSUVmax% showed a sensitivity, specificity, PPV, NPV and accuracy of 83.3%, 82.6%, 97.4%, 38.5% and 82.7%, respectively, with a 56.3% cutoff. Post-therapy ΔSUVmax% showed a sensitivity, specificity, PPV, NPV and accuracy of 83.3%, 84.8%, 97.5%, 41.7% and 84.6%, respectively, with a 76.8% cutoff. Compared to ΔSUVmax%, DS showed a significantly higher sensitivity, specificity (p < 0.05) and NPV (p < 0.01). The sensitivity, specificity, PPV, NPV and accuracy of rPET in predicting the therapy response at 24 months were 76.1%, 100%, 100%, 35.3% and 78.8%, respectively, with a cut-off of 1.31. DS and rPET showed comparable predictive performance (p > 0.58). In conclusion, DS is an easier method with better performance than ΔSUVmax% and rPET in predicting the chemotherapy response in pediatric HL patients.
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Hepatocellular carcinoma (HCC) remains not only a cause of a considerable part of oncologic mortality, but also a diagnostic and therapeutic challenge for healthcare systems worldwide. Early detection of the disease and consequential adequate therapy are imperative to increase patients' quality of life and survival. Imaging plays, therefore, a crucial role in the surveillance of patients at risk, the detection and diagnosis of HCC nodules, as well as in the follow-up post-treatment. The unique imaging characteristics of HCC lesions, deriving mainly from the assessment of their vascularity on contrast-enhanced computed tomography (CT), magnetic resonance (MR) or contrast-enhanced ultrasound (CEUS), allow for a more accurate, noninvasive diagnosis and staging. The role of imaging in the management of HCC has further expanded beyond the plain confirmation of a suspected diagnosis due to the introduction of ultrasound and hepatobiliary MRI contrast agents, which allow for the detection of hepatocarcinogenesis even at an early stage. Moreover, the recent technological advancements in artificial intelligence (AI) in radiology contribute an important tool for the diagnostic prediction, prognosis and evaluation of treatment response in the clinical course of the disease. This review presents current imaging modalities and their central role in the management of patients at risk and with HCC.
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OBJECTIVES: To explore radiologists' opinions regarding the shift from in-person oncologic multidisciplinary team meetings (MDTMs) to online MDTMs. To assess the perceived impact of online MDTMs, and to evaluate clinical and technical aspects of online meetings. METHODS: An online questionnaire including 24 questions was e-mailed to all European Society of Oncologic Imaging (ESOI) members. Questions targeted the structure and efficacy of online MDTMs, including benefits and limitations. RESULTS: A total of 204 radiologists responded to the survey. Responses were evaluated using descriptive statistical analysis. The majority (157/204; 77%) reported a shift to online MDTMs at the start of the pandemic. For the most part, this transition had a positive effect on maintaining and improving attendance. The majority of participants reported that online MDTMs provide the same clinical standard as in-person meetings, and that interdisciplinary discussion and review of imaging data were not hindered. Seventy three of 204 (35.8%) participants favour reverting to in-person MDTs, once safe to do so, while 7/204 (3.4%) prefer a continuation of online MDTMs. The majority (124/204, 60.8%) prefer a combination of physical and online MDTMs. CONCLUSIONS: Online MDTMs are a viable alternative to in-person meetings enabling continued timely high-quality provision of care with maintained coordination between specialties. They were accepted by the majority of surveyed radiologists who also favoured their continuation after the pandemic, preferably in combination with in-person meetings. An awareness of communication issues particular to online meetings is important. Training, improved software, and availability of support are essential to overcome technical and IT difficulties reported by participants. KEY POINTS: ⢠Majority of surveyed radiologists reported shift from in-person to online oncologic MDT meetings during the COVID-19 pandemic. ⢠The shift to online MDTMs was feasible and generally accepted by the radiologists surveyed with the majority reporting that online MDTMs provide the same clinical standard as in-person meetings. ⢠Most would favour the return to in-person MDTMs but would also accept the continued use of online MDTMs following the end of the current pandemic.
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COVID-19 , Humanos , Pandemias , Radiologistas , Inquéritos e Questionários , Equipe de Assistência ao PacienteRESUMO
Background: Radiomics of salivary gland imaging can support clinical decisions in different clinical scenarios, such as tumors, radiation-induced xerostomia and sialadenitis. This review aims to evaluate the methodological quality of radiomics studies on salivary gland imaging. Material and Methods: A systematic search was performed, and the methodological quality was evaluated using the radiomics quality score (RQS). Subgroup analyses according to the first author's professional role (medical or not medical), journal type (radiological journal or other) and the year of publication (2021 or before) were performed. The correlation of RQS with the number of patients was calculated. Results: Twenty-three articles were included (mean RQS 11.34 ± 3.68). Most studies well-documented the imaging protocol (87%), while neither prospective validations nor cost-effectiveness analyses were performed. None of the included studies provided open-source data. A statistically significant difference in RQS according to the year of publication was found (p = 0.009), with papers published in 2021 having slightly higher RQSs than older ones. No differences according to journal type or the first author's professional role were demonstrated. A moderate relationship between the overall RQS and the number of patients was found. Conclusions: Radiomics application in salivary gland imaging is increasing. Although its current clinical applicability can be affected by the somewhat inadequate quality of the papers, a significant improvement in radiomics methodologies has been demonstrated in the last year.
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NAVIGATOR is an Italian regional project boosting precision medicine in oncology with the aim of making it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project's goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e., standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence.
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Inteligência Artificial , Medicina de Precisão , Medicina de Precisão/métodos , Bancos de Espécimes Biológicos , Tomografia por Emissão de Pósitrons , BiomarcadoresRESUMO
BACKGROUND: This study aimed to identify optimal combinations between feature selection methods and machine-learning classifiers for predicting the metabolic response of individual metastatic breast cancer lesions, based on clinical variables and radiomic features extracted from pretreatment [18F]F-FDG PET/CT images. METHODS: A total of 48 patients with confirmed metastatic breast cancer, who received different treatments, were included. All patients had an [18F]F-FDG PET/CT scan before and after the treatment. From 228 metastatic lesions identified, 127 were categorized as responders (complete or partial metabolic response) and 101 as non-responders (stable or progressive metabolic response), by using the percentage changes in SULpeak (peak standardized uptake values normalized for body lean body mass). The lesion pool was divided into training (n = 182) and testing cohorts (n = 46); for each lesion, 101 image features from both PET and CT were extracted (202 features per lesion). These features, along with clinical and pathological information, allowed the prediction model's construction by using seven popular feature selection methods in cross-combination with another seven machine-learning (ML) classifiers. The performance of the different models was investigated with the receiver-operating characteristic curve (ROC) analysis, using the area under the curve (AUC) and accuracy (ACC) metrics. RESULTS: The combinations, least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM), or random forest (RF) had the highest AUC in the cross-validation, with 0.93 ± 0.06 and 0.92 ± 0.03, respectively, whereas Lasso + neural network (NN) or SVM, and mutual information (MI) + RF, had the higher AUC and ACC in the validation cohort, with 0.90/0.72, 0.86/0.76, and 87/85, respectively. On average, the models with Lasso and models with SVM had the best mean performance for both AUC and ACC in both training and validation cohorts. CONCLUSIONS: Image features obtained from a pretreatment [18F]F-FDG PET/CT along with clinical vaiables could predict the metabolic response of metastatic breast cancer lesions, by their incorporation into predictive models, whose performance depends on the selected combination between feature selection and ML classifier methods.
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Purpose: Differentiating Warthin tumor (WT) from pleomorphic adenoma (PA) is of primary importance due to differences in patient management, treatment and outcome. We sought to evaluate the performance of MRI-based radiomic features in discriminating PA from WT in the preoperative setting. Methods: We retrospectively evaluated 81 parotid gland lesions (48 PA and 33 WT) on T2-weighted (T2w) images and 52 of them on post-contrast fat-suppressed T1-weighted (pcfsT1w) images. All MRI examinations were carried out on a 1.5-Tesla MRI scanner, and images were segmented manually using the software ITK-SNAP (www.itk-snap.org). Results: The most discriminative feature on pcfsT1w images was GLCM_InverseVariance, yielding area under the curve (AUC), sensitivity and specificity of 0.9, 86 % and 87 %, respectively. Skewness was the feature extracted from T2w images with the highest specificity (88 %) in discriminating WT from PA. Conclusion: Radiomic analysis could be an important tool to improve diagnostic accuracy in differentiating PA from WT.