Development and validation of a novel model incorporating MRI-based radiomics signature with clinical biomarkers for distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis

PurposeIt is difficult to make a clear differential diagnosis of pancreatic carcinoma (PC) and mass-forming chronic pancreatitis (MFCP) via conventional examinations. We aimed to develop a novel model incorporating an MRI-based radiomics signature with clinical biomarkers for distinguishing the two lesions.MethodsA total of 102 patients were retrospectively enrolled and randomly divided into the training and validation cohorts. Radiomics features were extracted from four different sequences. Individual imaging modality radiomics signature, multiparametric MRI (mp-MRI) radiomics signature, and a final mixed model based on mp-MRI and clinically independent risk factors were established to discriminate between PC and MFCP. The diagnostic performance of each model and model discrimination were assessed in both the training and validation cohorts.ResultsADC had the best predictive performance among the four individual radiomics models, but there were no significant differences between the pairs of models (all p > 0.05). Six potential radiomics features were finally selected from the 960 texture features to formulate the radiomics score (rad-score) of the mp-MRI model. In addition, the boxplot results of the distributions of rad-scores identified the rad-score as an independent predictive factor for the differentiation of PC and MFCP (p< 0.001). Notably, the nomogram integrating rad-score and clinically independent risk factors had a better diagnostic performance than the mp-MRI and clinical models. These results were further confirmed by the validation group.ConclusionThe mixed model was developed and preliminarily validated to distinguish PC from MFCP, which may benefit the formulation of treatment strategies and nonsurgical procedures.     

AI applications to medical images: From machine learning to deep learning

PurposeArtificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in the real-world context.MethodsA narrative review has been performed including a critical assessment of articles published between 1989 and 2021 that guided challenging sections.ResultsWe first illustrate the architectural characteristics of machine learning (ML)/radiomics and deep learning (DL) approaches. For ML/radiomics, the phases of feature selection and of training, validation, and testing are described. DL models are presented as multi-layered artificial/convolutional neural networks, allowing us to directly process images. The data curation section includes technical steps such as image labelling, image annotation (with segmentation as a crucial step in radiomics), data harmonization (enabling compensation for differences in imaging protocols that typically generate noise in non-AI imaging studies) and federated learning. Thereafter, we dedicate specific sections to: sample size calculation, considering multiple testing in AI approaches; procedures for data augmentation to work with limited and unbalanced datasets; and the interpretability of AI models (the so-called black box issue). Pros and cons for choosing ML versus DL to implement AI applications to medical imaging are finally presented in a synoptic way.ConclusionsBiomedicine and healthcare systems are one of the most important fields for AI applications and medical imaging is probably the most suitable and promising domain. Clarification of specific challenging points facilitates the development of such systems and their translation to clinical practice.     

Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography

ObjectivesThe subtype classification of lung adenocarcinoma is important for treatment decision. This study aimed to investigate the deep learning and radiomics networks for predicting histologic subtype classification and survival of lung adenocarcinoma diagnosed through computed tomography (CT) images.MethodsA dataset of 1222 patients with lung adenocarcinoma were retrospectively enrolled from three medical institutions. The anonymised preoperative CT images and pathological labels of atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, invasive adenocarcinoma (IAC) with five predominant components were obtained. These pathological labels were divided into 2-category classification (IAC; non-IAC), 3-category and 8-category. We modeled the classification task of histological subtypes based on modified ResNet-34 deep learning network, radiomics strategies and deep radiomics combined algorithm. Then we established the prognostic models in lung adenocarcinoma patients with survival outcomes. The accuracy (ACC), area under ROC curves (AUCs) and C-index were primarily performed to evaluate the algorithms.ResultsThis study included a training set (n = 802) and two validation cohorts (internal, n = 196; external, n = 224). The ACC of deep radiomics algorithm in internal validation achieved 0.8776, 0.8061 in the 2-category, 3-category classification, respectively. Even in 8 classifications, the AUC ranged from 0.739 to 0.940 in internal set. Further, we constructed a prognosis model that C-index was 0.892(95% CI: 0.846–0.937) in internal validation set.ConclusionsThe automated deep radiomics based triage system has achieved the great performance in the subtype classification and survival predictability in patients with CT-detected lung adenocarcinoma nodules, providing the clinical guide for treatment strategies.     

CT radiomics combined with clinical variables for predicting the overall survival of hepatocellular carcinoma patients after hepatectomy

PurposeTo establish a model for assessing the overall survival (OS) of the hepatocellular carcinoma (HCC) patients after hepatectomy based on the clinical and radiomics features.MethodsThis study recruited a total of 267 patients with HCC, which were randomly divided into the training (N = 188) and validation (N = 79) cohorts. In the training cohort, radiomic features were selected with the intra-reader and inter-reader correlation coefficient (ICC), Spearman's correlation coefficient, and the least absolute shrinkage and selection operator (LASSO). The radiomics signatures were built by COX regression analysis and compared the predictive potential in the different phases (arterial, portal, and double-phase) and regions of interest (tumor, peritumor 3 mm, peritumor 5 mm). A clinical-radiomics model (CR model) was established by combining the radiomics signatures and clinical risk factors. The validation cohort was used to validate the proposed models.ResultsA total of 267 patients 86 (45.74%) and 37 (46.84%) patients died in the training and validation cohorts, respectively. Among all the radiomics signatures, those based on the tumor and peritumor (5 mm) (AP-TP5-Signature) showed the best prognostic potential (training cohort 1–3 years AUC:0.774–0.837; validation cohort 1–3 years AUC:0.754–0.810). The CR model showed better discrimination, calibration, and clinical applicability as compared to the clinical model and radiomics features. In addition, the CR model could perform risk-stratification and also allowed for significant discrimination between the Kaplan-Meier curves in most of the subgroups.ConclusionsThe CR model could predict the OS of the HCC patients after hepatectomy.     

Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients

BackgroundThis study aimed to identify a series of prognostically relevant immune features by immunophenoscore. Immune features were explored using MRI radiomics features to prediction the overall survival (OS) of lower-grade glioma (LGG) patients and their response to immune checkpoints.MethodLGG data were retrieved from TCGA and categorized into training and internal validation datasets. Patients attending the First Affiliated Hospital of Harbin Medical University were included in an external validation cohort. An immunophenoscore-based signature was built to predict malignant potential and response to immune checkpoint inhibitors in LGG patients. In addition, a deep learning neural network prediction model was built for validation of the immunophenoscore-based signature.ResultsImmunophenotype-associated mRNA signatures (IMriskScore) for outcome prediction and ICB therapeutic effects in LGG patients were constructed. Deep learning of neural networks based on radiomics showed that MRI radiomic features determined IMriskScore. Enrichment analysis and ssGSEA correlation analysis were performed. Mutations in CIC significantly improved the prognosis of patients in the high IMriskScore group. Therefore, CIC is a potential therapeutic target for patients in the high IMriskScore group. Moreover, IMriskScore is an independent risk factor that can be used clinically to predict LGG patient outcomes.ConclusionsThe IMriskScore model consisting of a sets of biomarkers, can independently predict the prognosis of LGG patients and provides a basis for the development of personalized immunotherapy strategies. In addition, IMriskScore features were predicted by MRI radiomics using a deep learning approach using neural networks. Therefore, they can be used for the prognosis of LGG patients.     

Intravoxel incoherent motion radiomics nomogram for predicting tumor treatment responses in nasopharyngeal carcinoma

BackgroundIntravoxel incoherent motion (IVIM) plays an important role in predicting treatment responses in patient with nasopharyngeal carcinoma (NPC). The goal of this study was to develop and validate a radiomics nomogram based on IVIM parametric maps and clinical data for the prediction of treatment responses in NPC patients.MethodsEighty patients with biopsy-proven NPC were enrolled in this study. Sixty-two patients had complete responses and 18 patients had incomplete responses to treatment. Each patient received a multiple b-value diffusion-weighted imaging (DWI) examination before treatment. Radiomics features were extracted from IVIM parametric maps derived from DWI image. Feature selection was performed by the least absolute shrinkage and selection operator method. Radiomics signature was generated by support vector machine based on the selected features. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) values were used to evaluate the diagnostic performance of radiomics signature. A radiomics nomogram was established by integrating the radiomics signature and clinical data.ResultsThe radiomics signature showed good prognostic performance to predict treatment response in both training (AUC = 0.906, P<0.001) and testing (AUC = 0.850, P<0.001) cohorts. The radiomic nomogram established by integrating the radiomic signature with clinical data significantly outperformed clinical data alone (C-index, 0.929 vs 0.724; P<0.0001).ConclusionsThe IVIM-based radiomics nomogram provided high prognostic ability to treatment responses in patients with NPC. The IVIM-based radiomics signature has the potential to be a new biomarker in prediction of the treatment responses and may affect treatment strategies in patients with NPC.     

Radiomics software for breast imaging optimization and simulation studies

Background and ObjectiveThe development, control and optimisation of new x-ray breast imaging modalities could benefit from a quantitative assessment of the resulting image textures. The aim of this work was to develop a software tool for routine radiomics applications in breast imaging, which will also be available upon request.MethodsThe tool (developed in MATLAB) allows image reading, selection of Regions of Interest (ROI), analysis and comparison. Requirements towards the tool also included convenient handling of common medical and simulated images, building and providing a library of commonly applied algorithms and a friendly graphical user interface. Initial set of features and analyses have been selected after a literature search. Being open, the tool can be extended, if necessary.ResultsThe tool allows semi-automatic extracting of ROIs, calculating and processing a total of 23 different metrics or features in 2D images and/or in 3D image volumes. Computations of the features were verified against computations with other software packages performed with test images. Two case studies illustrate the applicability of the tool – (i) features on a series of 2D ‘left’ and ‘right’ CC mammograms acquired on a Siemens Inspiration system were computed and compared, and (ii) evaluation of the suitability of newly proposed and developed breast phantoms for x-ray-based imaging based on reference values from clinical mammography images. Obtained results could steer the further development of the physical breast phantoms.ConclusionsA new image analysis toolbox was realized and can now be used in a multitude of radiomics applications, on both clinical and test images.     

Multiparametric magnetic resonance imaging-guided salvage radiotherapy in prostate cancer

AimTo analyse the efficacy and toxicity of postprostatectomy SRT in patients with a BCR evaluated with mpMRI.BackgroundMultiparametric magnetic resonance imaging (mpMRI) has the ability to detect the site of pelvic recurrence in patients with biochemical recurrence (BCR) after radical prostatectomy (RP). However, we do not know the oncological outcomes of mpMRI-guided savage radiotherapy (SRT).ResultsLocal, lymph node, and pelvic bone recurrence was observed in 13, 4 and 2 patients, respectively. PSA levels were significantly lower in patients with negative mpMRI (0.4 ng/mL [0.4]) vs. positive mpMRI (2.2 ng/mL [4.1], p = 0.003). Median planning target volume doses in patients with visible vs. non-visible recurrences were 76 Gy vs. 70 Gy. Overall, mean follow-up was 41 months (6–81). Biochemical relapse-free survival (bRFS) at 3 years was 82.3% and 82.5%, respectively, for the negative and positive mpMRI groups (p = 0.800). Three-year rates of late grade ≥2 urinary and rectal toxicity were 14.8% and 1.9%, respectively; all but one patient recovered without sequelae.ConclusionSRT to the macroscopic recurrence identified by mpMRI is a feasible and well-tolerated option. In this study, there were no differences in bRFS between MRI-positive and MRI-negative patients, indicating effective targeting of MRI-positive lesions.     

Pretreatment CT and PET radiomics predicting rectal cancer patients in response to neoadjuvant chemoradiotherapy

BackgroundThe purpose of this study was to characterize pre-treatment non-contrast computed tomography (CT) and ¹⁸F-fluorodeoxyglucose positron emission tomography (PET) based radiomics signatures predictive of pathological response and clinical outcomes in rectal cancer patients treated with neoadjuvant chemoradiotherapy (NACR T).Materials and methodsAn exploratory analysis was performed using pre-treatment non-contrast CT and PET imaging dataset. The association of tumor regression grade (TRG) and neoadjuvant rectal (NAR) score with pre-treatment CT and PET features was assessed using machine learning algorithms. Three separate predictive models were built for composite features from CT + PET.ResultsThe patterns of pathological response were TRG 0 (n = 13; 19.7%), 1 (n = 34; 51.5%), 2 (n = 16; 24.2%), and 3 (n = 3; 4.5%). There were 20 (30.3%) patients with low, 22 (33.3%) with intermediate and 24 (36.4%) with high NAR scores. Three separate predictive models were built for composite features from CT + PET and analyzed separately for clinical endpoints. Composite features with α = 0.2 resulted in the best predictive power using logistic regression. For pathological response prediction, the signature resulted in 88.1% accuracy in predicting TRG 0 vs. TRG 1–3; 91% accuracy in predicting TRG 0–1 vs. TRG 2–3. For the surrogate of DFS and OS, it resulted in 67.7% accuracy in predicting low vs. intermediate vs. high NAR scores.ConclusionThe pre-treatment composite radiomics signatures were highly predictive of pathological response in rectal cancer treated with NACR T. A larger cohort is warranted for further validation.     

Diagnostic Challenges in Adrenocortical Carcinoma: Recommendations for Surveillance after Surgical Resection of Selected Adrenal Nodules

ObjectiveTo discuss challenges in the diagnosis of adrenocortical carcinoma and to suggest surveillance measures after removal of selected adrenal nodules.MethodsWe present the case of a 65-year-old man with worsening hypertension and new-onset hypokalemia attributed to primary hyperaldosteronism due to a 3-cm right adrenal nodule.ResultsA laparoscopic right adrenalectomy was performed, and the histologic diagnosis was a benign adenoma. The patient’s hypertension and hypokalemia improved postoperatively but recurred 8 months later, and florid Cushing’s syndrome developed. Magnetic resonance imaging showed an 8-cm mass in the right adrenal bed and multiple hepatic metastatic lesions. Fine-needle biopsy confirmed the presence of adrenocortical carcinoma.ConclusionDespite a comprehensive biochemical, radiologic, and histologic assessment, the diagnosis of adrenocortical carcinoma can be missed. Particularly, we caution against undue reliance on the initial tumor size. We recommend that abdominal imaging be performed every 3 months for the first year and every 6 months for the second year after surgical removal of selected adrenal nodules. (Endocr Pract. 2007;13:636-641)     

Performance of Polymerase Chain Reaction Analysis of the Amniotic Fluid of Pregnant Women for Diagnosis of Congenital Toxoplasmosis: A Systematic Review and Meta-Analysis

IntroductionCongenital infection caused by Toxoplasma gondii can cause serious damage that can be diagnosed in utero or at birth, although most infants are asymptomatic at birth. Prenatal diagnosis of congenital toxoplasmosis considerably improves the prognosis and outcome for infected infants. For this reason, an assay for the quick, sensitive, and safe diagnosis of fetal toxoplasmosis is desirable.GoalTo systematically review the performance of polymerase chain reaction (PCR) analysis of the amniotic fluid of pregnant women with recent serological toxoplasmosis diagnoses for the diagnosis of fetal toxoplasmosis.MethodA systematic literature review was conducted via a search of electronic databases; the literature included primary studies of the diagnostic accuracy of PCR analysis of amniotic fluid from pregnant women who seroconverted during pregnancy. The PCR test was compared to a gold standard for diagnosis.ResultsA total of 1.269 summaries were obtained from the electronic database and reviewed, and 20 studies, comprising 4.171 samples, met the established inclusion criteria and were included in the review. The following results were obtained: studies about PCR assays for fetal toxoplasmosis are generally susceptible to bias; reports of the tests’ use lack critical information; the protocols varied among studies; the heterogeneity among studies was concentrated in the tests’ sensitivity; there was evidence that the sensitivity of the tests increases with time, as represented by the trimester; and there was more heterogeneity among studies in which there was more time between maternal diagnosis and fetal testing. The sensitivity of the method, if performed up to five weeks after maternal diagnosis, was 87% and specificity was 99%.ConclusionThe global sensitivity heterogeneity of the PCR test in this review was 66.5% (I²). The tests show low evidence of heterogeneity with a sensitivity of 87% and specificity of 99% when performed up to five weeks after maternal diagnosis. The test has a known performance and could be recommended for use up to five weeks after maternal diagnosis, when there is suspicion of fetal toxoplasmosis.     

Fractionation analysis and bioavailability of manganese in spinach (Spinacia oleracea L.) leaves

Abstract

This paper introduces a fractionation scheme using water, acetone, chloroform, diethyl ether, ethanol, n-hexane, and methanol as extractants for the determination of manganese in spinach samples by inductively coupled plasma-mass spectrometry (ICP-MS). Simulated gastric and intestinal digestions as well as n-octanol extraction and activated carbon adsorption were performed for the bioavailability assessments. Comparative studies of the various extraction treatments were evaluated for confirmation analysis. The total elemental concentrations were determined after digesting the samples in a microwave digestion system. The method validation parameters were defined in terms of the detection limits, accuracy, and precision. Additional validation was performed by comparing the ICP-MS method with atomic absorption spectrometry. The limits of detection and quantification were 0.046 and 0.154 mg kg^-1, respectively. Additionally, the repeatability and reproducibility, calculated from the relative standard deviation (%RSD), were 2.4% and 3.7%, respectively.     

Ceruloplasmin and oxidative stress in severe eosinophilic asthma patients treated with Mepolizumab and Benralizumab

IntroductionSevere eosinophilic asthma has been associated with Th2 airway inflammation and elevated proinflammatory cytokines and chemokines, such as IL-5. Precision therapies have recently been shown to improve asthma symptoms with a steroid-sparing effect. Two such therapies, Benralizumab and Mepolizumab, humanized IgG antibodies directed against the IL-5 receptor and IL-5, have been approved for severe eosinophilic asthma.MethodsHere we used a differential proteomic approach to analyse serum from patients with severe eosinophilic asthma treated with Benralizumab and Mepolizumab in a search for differential molecular modifications responsible of their effects. Enrichment analysis of differential proteins was performed for the two treatments.Results and discussionAfter one month of Benralizumab treatment we detected up-regulation of certain protein species of the antioxidant ceruloplasmin. To investigate oxidative stress, we performed redox proteomics which detected lower oxidative burst after one month of Benralizumab treatment than in the pre-treatment phase or after one month of Mepolizumab therapy.     

User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions

PurposePrecision cancer medicine is dependent on accurate prediction of disease and treatment outcome, requiring integration of clinical, imaging and interventional knowledge. User controlled pipelines are capable of feature integration with varied levels of human interaction. In this work we present two pipelines designed to combine clinical, radiomic (quantified imaging), and RTx-omic (quantified radiation therapy (RT) plan) information for prediction of locoregional failure (LRF) in head and neck cancer (H&N).MethodsPipelines were designed to extract information and model patient outcomes based on clinical features, computed tomography (CT) imaging, and planned RT dose volumes. We predict H&N LRF using: 1) a highly user-driven pipeline that leverages modular design and machine learning for feature extraction and model development; and 2) a pipeline with minimal user input that utilizes deep learning convolutional neural networks to extract and combine CT imaging, RT dose and clinical features for model development.ResultsClinical features with logistic regression in our highly user-driven pipeline had the highest precision recall area under the curve (PR-AUC) of 0.66 (0.33–0.93), where a PR-AUC = 0.11 is considered random. CONCLUSIONS: Our work demonstrates the potential to aggregate features from multiple specialties for conditional-outcome predictions using pipelines with varied levels of human interaction. Most importantly, our results provide insights into the importance of data curation and quality, as well as user, data and methodology bias awareness as it pertains to result interpretation in user controlled pipelines.     

Estimation of extremely small field radiation dose for brain stereotactic radiotherapy using the Vero4DRT system

The purpose of this study was a dosimetric validation of the Vero4DRT for brain stereotactic radiotherapy (SRT) with extremely small fields calculated by the treatment planning system (TPS) iPlan (Ver.4.5.1; algorithm XVMC). Measured and calculated data (e.g. percentage depth dose [PDD], dose profile, and point dose) were compared for small square fields of 30 × 30, 20 × 20, 10 × 10 and 5 × 5 mm² using ionization chambers of 0.01 or 0.04 cm³ and a diamond detector. Dose verifications were performed using an ionization chamber and radiochromic film (EBT3; the equivalent field sizes used were 8.2, 8.7, 8.9, 9.5, and 12.9 mm²) for five brain SRT cases irradiated with dynamic conformal arcs.The PDDs and dose profiles for the measured and calculated data were in good agreement for fields larger than or equal to 10 × 10 mm² when an appropriate detector was chosen. The dose differences for point doses in fields of 30 × 30, 20 × 20, 10 × 10 and 5 × 5 mm² were +0.48%, +0.56%, −0.52%, and +11.2% respectively. In the dose verifications for the brain SRT plans, the mean dose difference between the calculated and measured doses were −0.35% (range, −0.94% to +0.47%), with the average pass rates for the gamma index under the 3%/2 mm criterion being 96.71%, 93.37%, and 97.58% for coronal, sagittal, and axial planes respectively.The Vero4DRT system provides accurate delivery of radiation dose for small fields larger than or equal to 10 × 10 mm².     

Building CT Radiomics Based Nomogram for Preoperative Esophageal Cancer Patients Lymph Node Metastasis Prediction

PURPOSE: To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS: A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS: Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell’s Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION: The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.     

The progress of multimodal imaging combination and subregion based radiomics research of cancers

In recent years, with the standardization of radiomics methods; development of tools; and popularization of the concept, radiomics has been widely used in all aspects of tumor diagnosis; treatment; and prognosis. As the study of radiomics in cancer has become more advanced, the currently used methods have revealed their shortcomings. The performance of cancer radiomics based on single-modality medical images, which based on their imaging principles, only partially reflects tumor information, has been necessarily compromised. Using the whole tumor as a region of interest to extract radiomic features inevitably leads to the loss of intra-tumoral heterogeneity of, which also affects the performance of radiomics. Radiomics of multimodal images extracts various aspects of information from images of each modality and then integrates them together for model construction; thus, avoiding missing information. Subregional segmentation based on multimodal medical image combinations allows radiomics features acquired from subregions to retain tumor heterogeneity, further improving the performance of radiomics. In this review, we provide a detailed summary of the current research on the radiomics of multimodal images of cancer and tumor subregion-based radiomics, and then raised some of the research problems and also provide a thorough discussion on these issues.     

In vitro inhibitory activity of sertraline against clinical isolates of Sporothrix schenckii

BackgroundSertraline (SRT) is an antidepressant that has proven its activity in vitro against Cryptococcus, Coccidioides, Trichosporon and other fungi. Disseminated sporotrichosis, although rare, has a high mortality and its treatment is difficult and prolonged, often relying in combining two or more antifungals.AimsIn our study we evaluate the antifungal activity of SRT, alone and in combination with itraconazole (ITC), voriconazole (VRC) and amphotericin B (AMB), against 15 clinical isolates of Sporothrix schenckii.MethodsWe used the broth microdilution method as described by the CLSI to test the susceptibility to antifungals, and the checkerboard microdilution method to evaluate drug interactions.ResultsThe minimum inhibitory concentration (MIC) with SRT was in the range of 4–8 μg/ml, while for AMB, VRC and ITC were 0.5–4 μg/ml, 0.5–8 μg/ml and 0.125–2 μg/ml, respectively. In addition, SRT showed synergy with ITC in one strain, mainly additivity with VRC, and indifference with AMB in others.ConclusionsThe MIC values with SRT for the isolates studied show the potential role of this drug as an adjuvant in the treatment of sporotrichosis, especially in disseminated or complicated cases.     

Radiomics in radiooncology – Challenging the medical physicist

PurposeNoticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions.MethodsBased on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach.ResultsClinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data (‘panomics’) challenges the medical physicist as member of the radiooncology team.ConclusionsThe new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology.