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BACKGROUND: Magnetic resonance elastography (MRE) can quantify tissue biomechanics noninvasively, including pathological hepatic states like metabolic dysfunction-associated steatohepatitis. PURPOSE: To compare the performance of 2D/3D-MRE using the gravitational (GT) transducer concept with the current commercial acoustic (AC) solution utilizing a 2D-MRE approach. Additionally, quality index markers (QIs) were proposed to identify image pixels with sufficient quality for reliably estimating tissue biomechanics. STUDY TYPE: Prospective. POPULATION: One hundred seventy participants with suspected or confirmed liver disease (median age, 57 years [interquartile range (IQR), 46-65]; 66 females), and 11 healthy volunteers (median age, 31 years [IQR, 27-34]; 5 females). FIELD STRENGTH/SEQUENCE: Participants were scanned twice at 1.5 T and 60 Hz vibration frequency: first, using AC-MRE (2D-MRE, spin-echo EPI sequence, 11 seconds breath-hold), and second, using GT-MRE (2D- and 3D-MRE, gradient-echo sequence, 14 seconds breath-hold). ASSESSMENT: Image analysis was performed by four independent radiologists and one biomedical engineer. Additionally, superimposed analytic plane shear waves of known wavelength and attenuation at fixed shear modulus were used to propose pertinent QIs. STATISTICAL TESTS: Spearman's correlation coefficient (r) was applied to assess the correlation between modalities. Interreader reproducibility was evaluated using Bland-Altman bias and reproducibility coefficients. P-values <0.05 were considered statistically significant. RESULTS: Liver stiffness quantified via GT-2D/3D correlated well with AC-2D (r ≥ 0.89 [95% CI: 0.85-0.92]) and histopathological grading (r ≥ 0.84 [95% CI: 0.72-0.91]), demonstrating excellent agreement in Bland-Altman plots and between readers (κ ≥ 0.86 [95% CI: 0.81-0.91]). However, GT-2D showed a bias in overestimating stiffness compared to GT-3D. Proposed QIs enabled the identification of pixels deviating beyond 10% from true stiffness based on a combination of total wave amplitude, temporal sinusoidal nonlinearity, and wave signal-to-noise ratio for GT-3D. CONCLUSION: GT-MRE represents an alternative to AC-MRE for noninvasive liver tissue characterization. Both GT-2D and 3D approaches correlated strongly with the established commercial approach, offering advanced capabilities in abdominal imaging compared to AC-MRE. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.
ABSTRACT
BACKGROUND: Although core needle biopsy is an important tool in minimally invasive tissue sampling and diagnostics for head and neck masses, comprehensive data about safety and outcomes is lacking. PURPOSE: To retrospectively evaluate the diagnostic performance and safety of computed tomography (CT)-guided percutaneous core needle biopsy of head and neck masses. MATERIAL AND METHODS: This retrospective single-center study included patients from 04/2007 to 12/2021, and a total of 156 core needle biopsies were evaluated. The initial histopathological results were compared with the long-term final diagnosis to evaluate the diagnostic yield of CT-guided core needle biopsies. The patients' age, sex, and history of malignancy, as well as procedural complications and radiation exposure were collected. RESULTS: A total of 156 biopsies of 150 patients (mean age 56 years ± 17; 89 men) were evaluated. 57.3% (86/150) of patients had a history of malignancy. 55.1% (86/156) of the lesions were accessed by an infrahyoid needle approach. 92.9% (145/156) of biopsies yielded conclusive results. There were no false positives and 4 false negatives, resulting in a total false negative rate of 2.7% (4/145) and a total diagnostic yield of 90.4% (141/156). There were nine puncture-related complications (9/156-5.7%). None of the complications required further reintervention. The average dose length product was 311.3 mGy × cm. CONCLUSION: CT-guided core needle biopsies of head and neck masses showed excellent results with high diagnostic yield and clinical safety. CLINICAL RELEVANCE STATEMENT: General anesthesia for open biopsy carries a higher risk for elderly patients, and fine needle aspiration has a poor reputation in terms of its diagnostic yield. This study focuses on safety and diagnostic yield of CT-guided core needle biopsies. KEY POINTS: ⢠CT-guided core needle biopsy in head and neck tumors was a reliable and safe procedure. ⢠The most common cause for an inconclusive biopsy result was a shortage of tissue collected during the biopsy. ⢠During our study period of nearly 15 years, the radiation exposure of head and neck biopsies decreased.
Subject(s)
Head and Neck Neoplasms , Image-Guided Biopsy , Tomography, X-Ray Computed , Humans , Male , Female , Middle Aged , Head and Neck Neoplasms/pathology , Head and Neck Neoplasms/diagnostic imaging , Tomography, X-Ray Computed/methods , Retrospective Studies , Image-Guided Biopsy/methods , Biopsy, Large-Core Needle/methods , Biopsy, Large-Core Needle/adverse effects , Radiography, Interventional/methods , Aged , Adult , Aged, 80 and overABSTRACT
OBJECTIVE: Our study objective was to explore the additional value of dual-energy CT (DECT) material decomposition for squamous cell carcinoma of the head and neck (SCCHN) survival prognostication. METHODS: A group of 50 SCCHN patients (male, 37; female, 13; mean age, 63.6 ± 10.82 years) with baseline head and neck DECT between September 2014 and August 2020 were retrospectively included. Primary tumors were segmented, radiomics features were extracted, and DECT material decomposition was performed. We used independent train and validation datasets with cross-validation and 100 independent iterations to identify prognostic signatures applying elastic net (EN) and random survival forest (RSF). Features were ranked and intercorrelated according to their prognostic importance. We benchmarked the models against clinical parameters. Intraclass correlation coefficients were used to analyze the interreader variation. RESULTS: The exclusively radiomics-trained models achieved similar ( P = 0.947) prognostic performance of area under the curve (AUC) = 0.784 (95% confidence interval [CI], 0.775-0.812) (EN) and AUC = 0.785 (95% CI, 0.759-0.812) (RSF). The additional application of DECT material decomposition did not improve the model's performance (EN, P = 0.594; RSF, P = 0.198). In the clinical benchmark, the top averaged AUC value of 0.643 (95% CI, 0.611-0.675) was inferior to the quantitative imaging-biomarker models ( P < 0.001). A combined imaging and clinical model did not improve the imaging-based models ( P > 0.101). Shape features revealed high prognostic importance. CONCLUSIONS: Radiomics AI applications may be used for SCCHN survival prognostication, but the spectral information of DECT material decomposition did not improve the model's performance in our preliminary investigation.
Subject(s)
Head and Neck Neoplasms , Radiomics , Humans , Male , Female , Middle Aged , Aged , Squamous Cell Carcinoma of Head and Neck/diagnostic imaging , Retrospective Studies , Tomography, X-Ray Computed/methods , Head and Neck Neoplasms/diagnostic imagingABSTRACT
BACKGROUND: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. METHODS: Retrospectively, 106 prostate tissue samples from 48 patients (mean age, [Formula: see text] years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open-source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. RESULTS: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02-0.06 for ERG and PIN-4. CONCLUSIONS: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.
Subject(s)
Prostate , Prostatic Neoplasms , Male , Humans , Retrospective Studies , Reproducibility of Results , Prostatic Neoplasms/pathology , AlgorithmsABSTRACT
Background In patients with distal radius fractures (DRFs), low bone mineral density (BMD) is associated with bone substitute use during surgery and bone nonunion, but BMD information is not regularly available. Purpose To evaluate the feasibility of dual-energy CT (DECT)-based BMD assessment from routine examinations in the distal radius and the relationship between the obtained BMD values, the occurrence of DRFs, bone nonunion, and use of surgical bone substitute. Materials and Methods Scans in patients who underwent routine dual-source DECT in the distal radius between January 2016 and December 2021 were retrospectively acquired. Phantomless BMD assessment was performed using the delineated trabecular bone of a nonfractured segment of the distal radius and both DECT image series. CT images and health records were examined to determine fracture severity, surgical management, and the occurrence of bone nonunion. Associations of BMD with the occurrence of DRFs, bone nonunion, and bone substitute use at surgical treatment were examined with generalized additive models and receiver operating characteristic analysis. Results This study included 263 patients (median age, 52 years; IQR, 36-64 years; 132 female patients), of whom 192 were diagnosed with fractures. Mean volumetric BMD was lower in patients who sustained a DRF (93.9 mg/cm3 vs 135.4 mg/cm3; P < .001), required bone substitutes (79.6 mg/cm3 vs 95.5 mg/cm3; P < .001), and developed bone nonunion (71.1 mg/cm3 vs 96.5 mg/cm3; P < .001). Receiver operating characteristic curve analysis identified these patients with an area under the curve of 0.71-0.91 (P < .001). Lower BMD increased the risk to sustain DRFs, develop bone nonunion, and receive bone substitutes at surgery (P < .001). Conclusion DECT-based BMD assessment at routine examinations is feasible and could help predict surgical bone substitute use and the occurrence of bone nonunion in patients with DRFs. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Carrino in this issue.
Subject(s)
Bone Substitutes , Fractures, Bone , Wrist Fractures , Humans , Female , Middle Aged , Bone Density , Radius/diagnostic imaging , Retrospective Studies , Tomography, X-Ray Computed/methods , Absorptiometry, PhotonABSTRACT
Whole-body magnetic resonance imaging (MRI) has become increasingly popular in oncology. However, the long acquisition time might hamper its widespread application. We sought to assess and compare free-breathing sequences with conventional breath-hold examinations in whole-body MRI using an automated workflow process. This prospective study consisted of 20 volunteers and six patients with a variety of pathologies who had undergone whole-body 1.5-T MRI that included T1-weighted radial and Dixon volumetric interpolated breath-hold examination sequences. Free-breathing sequences were operated by using an automated user interface. Image quality, diagnostic confidence, and image noise were evaluated by two experienced radiologists. Additionally, signal-to-noise ratio was measured. Diagnostic performance for the overall detection of pathologies was assessed using the area under the receiver operating characteristics curve (AUC). Study participants were asked to rate their examination experiences in a satisfaction survey. MR free-breathing scans were rated as at least equivalent to conventional MR scans in more than 92% of cases, showing high overall diagnostic accuracy (95% [95% CI 92-100]) and performance (AUC 0.971, 95% CI 0.942-0.988; p < 0.0001) for the assessment of pathologies at simultaneously reduced examination times (25 ± 2 vs. 32 ± 3 min; p < 0.0001). Interrater agreement was excellent for both free-breathing (Ï° = 0.96 [95% CI 0.88-1.00]) and conventional scans (Ï° = 0.93 [95% CI 0.84-1.00]). Qualitative and quantitative assessment for image quality, image noise, and diagnostic confidence did not differ between the two types of MR image acquisition (all p > 0.05). Scores for patient satisfaction were significantly better for free-breathing compared with breath-hold examinations (p = 0.0145), including significant correlations for the grade of noise (r = 0.79, p < 0.0001), tightness (r = 0.71, p < 0.0001), and physical fatigue (r = 0.52, p = 0.0065). In summary, free-breathing whole-body MRI in tandem with an automated user interface yielded similar diagnostic performance at equivalent image quality and shorter acquisition times compared to conventional breath-hold sequences.
Subject(s)
Image Interpretation, Computer-Assisted , Magnetic Resonance Imaging , Humans , Image Enhancement/methods , Image Interpretation, Computer-Assisted/methods , Magnetic Resonance Imaging/methods , Prospective Studies , Whole Body Imaging , WorkflowABSTRACT
BACKGROUND: D-dimer testing is known to have a high sensitivity at simultaneously low specificity, resulting in nonspecific elevations in a variety of conditions. METHODS: This retrospective study sought to assess diagnostic and prognostic features of D-dimers in cancer patients referred to the emergency department for suspected pulmonary embolism (PE) and deep vein thrombosis (DVT). In total, 526 patients with a final adjudicated diagnosis of PE (n = 83) and DVT (n = 69) were enrolled, whereas 374 patients served as the comparative group, in which venous thromboembolism (VTE) has been excluded. RESULTS: For the identification of VTE, D-dimers yielded the highest positive predictive value of 96% (95% confidence interval (CI), 85-99) at concentrations of 9.9 mg/L and a negative predictive value of 100% at .6 mg/L (95% CI, 97-100). At the established rule-out cut-off level of .5 mg/L, D-dimers were found to be very sensitive (100%) at a moderate specificity of nearly 65%. Using an optimised cut-off value of 4.9 mg/L increased the specificity to 95% for the detection of life-threatening VTE at the cost of moderate sensitivities (64%). During a median follow-up of 30 months, D-dimers positively correlated with the reoccurrence of VTE (p = .0299) and mortality in both cancer patients with VTE (p < .0001) and without VTE (p = .0008). CONCLUSIONS: Although D-dimer testing in cancer patients is discouraged by current guidelines, very high concentrations above the 10-fold upper reference limit contain diagnostic and prognostic information and might be helpful in risk assessment, while low concentrations remain useful for ruling out VTE.
Subject(s)
Neoplasms , Venous Thromboembolism , Humans , Venous Thromboembolism/diagnosis , Prognosis , Retrospective Studies , Fibrin Fibrinogen Degradation Products , Predictive Value of TestsABSTRACT
BACKGROUND: To investigate the potential of radiomic features and dual-source dual-energy CT (DECT) parameters in differentiating between benign and malignant mediastinal masses and predicting patient outcomes. METHODS: In this retrospective study, we analysed data from 90 patients (38 females, mean age 51 ± 25 years) with confirmed mediastinal masses who underwent contrast-enhanced DECT. Attenuation, radiomic features and DECT-derived imaging parameters were evaluated by two experienced readers. We performed analysis of variance (ANOVA) and Chi-square statistic tests for data comparison. Receiver operating characteristic curve analysis and Cox regression tests were used to differentiate between mediastinal masses. RESULTS: Of the 90 mediastinal masses, 49 (54%) were benign, including cases of thymic hyperplasia/thymic rebound (n = 10), mediastinitis (n = 16) and thymoma (n = 23). The remaining 41 (46%) lesions were classified as malignant, consisting of lymphoma (n = 28), mediastinal tumour (n = 4) and thymic carcinoma (n = 9). Significant differences were observed between benign and malignant mediastinal masses in all DECT-derived parameters (p ≤ .001) and 38 radiomic features (p ≤ .044) obtained from contrast-enhanced DECT. The combination of these methods achieved an area under the curve of .98 (95% CI, .893-1.000; p < .001) to differentiate between benign and malignant masses, with 100% sensitivity and 91% specificity. Throughout a follow-up of 1800 days, a multiparametric model incorporating radiomic features, DECT parameters and gender showed promising prognostic power in predicting all-cause mortality (c-index = .8 [95% CI, .702-.890], p < .001). CONCLUSIONS: A multiparametric approach combining radiomic features and DECT-derived imaging biomarkers allows for accurate and noninvasive differentiation between benign and malignant masses in the anterior mediastinum.
Subject(s)
Lymphoma , Mediastinal Neoplasms , Thymus Neoplasms , Female , Humans , Adult , Middle Aged , Aged , Tomography, X-Ray Computed/methods , Retrospective Studies , Thymus Neoplasms/diagnostic imaging , Thymus Neoplasms/pathology , Lymphoma/diagnostic imaging , Mediastinal Neoplasms/diagnostic imagingABSTRACT
BACKGROUND: Cancer is a well-known risk factor for venous thromboembolism (VTE). A combined strategy of D-dimer testing and clinical pre-test probability is usually used to exclude VTE. However, its effectiveness is diminished in cancer patients due to reduced specificity, ultimately leading to a decreased clinical utility. This review article seeks to provide a comprehensive summary of how to interpret D-dimer testing in cancer patients. METHODS: In accordance with PRISMA standards, literature pertaining to the diagnostic and prognostic significance of D-dimer testing in cancer patients was carefully chosen from reputable sources such as PubMed and the Cochrane databases. RESULTS: D-dimers have not only a diagnostic value in ruling out VTE but can also serve as an aid for rule-in if their values exceed 10-times the upper limit of normal. This threshold allows a diagnosis of VTE in cancer patients with a positive predictive value of more than 80%. Moreover, elevated D-dimers carry important prognostic information and are associated with VTE reoccurrence. A gradual increase in risk for all-cause death suggests that VTE is also an indicator of biologically more aggressive cancer types and advanced cancer stages. Considering the lack of standardization for D-dimer assays, it is essential for clinicians to carefully consider the variations in assay performance and the specific test characteristics of their institution. CONCLUSIONS: Standardizing D-dimer assays and developing modified pretest probability models specifically for cancer patients, along with adjusted cut-off values for D-dimer testing, could significantly enhance the accuracy and effectiveness of VTE diagnosis in this population.
Subject(s)
Fibrin Fibrinogen Degradation Products , Neoplasms , Humans , Neoplasms/blood , Neoplasms/complications , Neoplasms/diagnosis , Predictive Value of Tests , Risk Factors , Venous Thromboembolism/blood , Venous Thromboembolism/diagnosis , Venous Thromboembolism/prevention & control , Biological Assay/standards , Sensitivity and SpecificityABSTRACT
OBJECTIVES: The purpose of this study was to evaluate the diagnostic accuracy of third-generation dual-source dual-energy CT (DECT) color-coded collagen reconstructions for the assessment of the cruciate ligaments compared to standard grayscale image reconstruction. METHODS: Patients who underwent third-generation dual-source DECT followed by either 3-T MRI or arthroscopy of the knee joint within 14 days between January 2016 and December 2021 were included in this retrospective study. Five radiologists independently evaluated conventional grayscale DECT for the presence of injury to the cruciate ligaments; after 4 weeks, readers re-evaluated the examinations using grayscale images and color-coded collagen reconstructions. A reference standard for MRI was provided by a consensus reading of two experienced readers and arthroscopy. Sensitivity and specificity were the primary metrics of diagnostic performance. RESULTS: Eighty-five patients (mean age, 44 years ± 16; 50 male) with injury to the ACL or PCL (n = 31) were ultimately included. Color-coded collagen reconstructions significantly increased overall sensitivity (94/105 [90%] vs. 67/105 [64%]), specificity (248/320 [78%] vs. 215/320 [67%]), PPV (94/166 [57%] vs. 67/162 [39%]), NPV (248/259 [96%] vs. 215/253 [85%]), and accuracy (342/425 [81%] vs. 282/425 [66%]) for the detection of injury to the anterior cruciate ligament (all parameters, p < .001). For injury to the posterior cruciate ligament, diagnostic accuracy increased for complete tears (p < .001). Color-coded collagen reconstructions achieved superior diagnostic confidence, image quality, and noise scores compared to grayscale CT (all parameters, p < .001) and showed good agreement with MRI examinations. CONCLUSIONS: DECT-derived color-coded collagen reconstructions yield substantially higher diagnostic accuracy and confidence for assessing the integrity of the cruciate ligaments compared to standard grayscale CT in patients with acute trauma. KEY POINTS: ⢠Color-coded collagen reconstructions derived from dual-energy CT yield substantially higher diagnostic accuracy and confidence for the assessment of the cruciate ligaments compared to standard grayscale CT in patients with acute trauma. ⢠Color-coded collagen reconstructions demonstrate good agreement with MRI for the assessment cruciate ligament injury. ⢠Dual-energy CT may serve as a readily available screening approach for patients with acute trauma to the knee when injury to the cruciate ligaments is suspected.
Subject(s)
Anterior Cruciate Ligament Injuries , Tomography, X-Ray Computed , Humans , Male , Adult , Retrospective Studies , Tomography, X-Ray Computed/methods , Knee Joint , Anterior Cruciate Ligament , Collagen , Anterior Cruciate Ligament Injuries/diagnostic imaging , Sensitivity and Specificity , Magnetic Resonance Imaging/methodsABSTRACT
PURPOSE: Non-invasive prediction of the tumour of origin giving rise to brain metastases (BMs) using MRI measurements obtained in radiological routine and elucidating the biological basis by matched histopathological analysis. METHODS: Preoperative MRI and histological parameters of 95 BM patients (female, 50; mean age 59.6 ± 11.5 years) suffering from different primary tumours were retrospectively analysed. MR features were assessed by region of interest (ROI) measurements of signal intensities on unenhanced T1-, T2-, diffusion-weighted imaging and apparent diffusion coefficient (ADC) normalised to an internal reference ROI. Furthermore, we assessed BM size and oedema as well as cell density, proliferation rate, microvessel density and vessel area as histopathological parameters. RESULTS: Applying recursive partitioning conditional inference trees, only histopathological parameters could stratify the primary tumour entities. We identified two distinct BM growth patterns depending on their proliferative status: Ki67high BMs were larger (p = 0.02), showed less peritumoural oedema (p = 0.02) and showed a trend towards higher cell density (p = 0.05). Furthermore, Ki67high BMs were associated with higher DWI signals (p = 0.03) and reduced ADC values (p = 0.004). Vessel density was strongly reduced in Ki67high BM (p < 0.001). These features differentiated between lung cancer BM entities (p ≤ 0.03 for all features) with SCLCs representing predominantly the Ki67high group, while NSCLCs rather matching with Ki67low features. CONCLUSION: Interpretable and easy to obtain MRI features may not be sufficient to predict directly the primary tumour entity of BM but seem to have the potential to aid differentiating high- and low-proliferative BMs, such as SCLC and NSCLC.
Subject(s)
Brain Neoplasms , Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Humans , Middle Aged , Aged , Retrospective Studies , Ki-67 Antigen , Magnetic Resonance Imaging , Brain Neoplasms/pathology , Diffusion Magnetic Resonance Imaging/methods , Lung Neoplasms/diagnostic imaging , Cell ProliferationABSTRACT
BACKGROUND: Treatment plans for squamous cell carcinoma of the head and neck (SCCHN) are individually decided in tumor board meetings but some treatment decision-steps lack objective prognostic estimates. Our purpose was to explore the potential of radiomics for SCCHN therapy-specific survival prognostication and to increase the models' interpretability by ranking the features based on their predictive importance. METHODS: We included 157 SCCHN patients (male, 119; female, 38; mean age, 64.39 ± 10.71 years) with baseline head and neck CT between 09/2014 and 08/2020 in this retrospective study. Patients were stratified according to their treatment. Using independent training and test datasets with cross-validation and 100 iterations, we identified, ranked and inter-correlated prognostic signatures using elastic net (EN) and random survival forest (RSF). We benchmarked the models against clinical parameters. Inter-reader variation was analyzed using intraclass-correlation coefficients (ICC). RESULTS: EN and RSF achieved top prognostication performances of AUC = 0.795 (95% CI 0.767-0.822) and AUC = 0.811 (95% CI 0.782-0.839). RSF prognostication slightly outperformed the EN for the complete (ΔAUC 0.035, p = 0.002) and radiochemotherapy (ΔAUC 0.092, p < 0.001) cohort. RSF was superior to most clinical benchmarking (p ≤ 0.006). The inter-reader correlation was moderate or high for all features classes (ICC ≥ 0.77 (± 0.19)). Shape features had the highest prognostic importance, followed by texture features. CONCLUSIONS: EN and RSF built on radiomics features may be used for survival prognostication. The prognostically leading features may vary between treatment subgroups. This warrants further validation to potentially aid clinical treatment decision making in the future.
Subject(s)
Head and Neck Neoplasms , Tomography, X-Ray Computed , Humans , Male , Female , Middle Aged , Aged , Squamous Cell Carcinoma of Head and Neck/diagnostic imaging , Squamous Cell Carcinoma of Head and Neck/therapy , Retrospective Studies , Prognosis , Head and Neck Neoplasms/diagnostic imaging , Head and Neck Neoplasms/therapyABSTRACT
This prospective study sought to evaluate potential savings of radiation dose to medical staff using real-time dosimetry coupled with visual radiation dose feedback during angiographic interventions. For this purpose, we analyzed a total of 214 angiographic examinations that consisted of chemoembolizations and several other types of therapeutic interventions. The Unfors RaySafe i2 dosimeter was worn by the interventionalist at chest height over the lead protection. A total of 110 interventions were performed with real-time radiation dosimetry allowing the interventionalist to react upon higher x-ray exposure and 104 examinations served as the comparative group without real-time radiation monitoring. By using the real-time display during interventions, the overall mean operator radiation dose decreased from 3.67 (IQR, 0.95-23.01) to 2.36 µSv (IQR, 0.52-12.66) (-36%; p = 0.032) at simultaneously reduced operator exposure time by 4.5 min (p = 0.071). Dividing interventions into chemoembolizations and other types of therapeutic interventions, radiation dose decreased from 1.31 (IQR, 0.46-3.62) to 0.95 µSv (IQR, 0.53-3.11) and from 24.39 (IQR, 12.14-63.0) to 10.37 µSv (IQR, 0.85-36.84), respectively, using live-screen dosimetry (p ≤ 0.005). Radiation dose reductions were also observed for the participating assistants, indicating that they could also benefit from real-time visual feedback dosimetry during interventions (-30%; p = 0.039). Integration of real-time dosimetry into clinical processes might be useful in reducing occupational radiation exposure time during angiographic interventions. The real-time visual feedback raised the awareness of interventionalists and their assistants to the potential danger of prolonged radiation exposure leading to the adoption of radiation-sparing practices. Therefore, it might create a safer environment for the medical staff by keeping the applied radiation exposure as low as possible.
Subject(s)
Occupational Exposure , Radiation Exposure , Radiation Injuries , Humans , Radiation Dosage , Prospective Studies , Feedback, Sensory , Radiometry , Radiation Injuries/prevention & control , Radiation Exposure/prevention & control , Occupational Exposure/prevention & control , Radiography, Interventional , FluoroscopyABSTRACT
AIMS: Changes in metabolism are known to contribute to tumour phenotypes. If and how metabolic alterations in brain tumours contribute to patient outcome is still poorly understood. Epigenetics impact metabolism and mitochondrial function. The aim of this study is a characterisation of metabolic features in molecular subgroups of isocitrate dehydrogenase mutant (IDHmut) and isocitrate dehydrogenase wildtype (IDHwt) gliomas. METHODS: We employed DNA methylation pattern analyses with a special focus on metabolic genes, large-scale metabolism panel immunohistochemistry (IHC), qPCR-based determination of mitochondrial DNA copy number and immune cell content using IHC and deconvolution of DNA methylation data. We analysed molecularly characterised gliomas (n = 57) for in depth DNA methylation, a cohort of primary and recurrent gliomas (n = 22) for mitochondrial copy number and validated these results in a large glioma cohort (n = 293). Finally, we investigated the potential of metabolic markers in Bevacizumab (Bev)-treated gliomas (n = 29). RESULTS: DNA methylation patterns of metabolic genes successfully distinguished the molecular subtypes of IDHmut and IDHwt gliomas. Promoter methylation of lactate dehydrogenase A negatively correlated with protein expression and was associated with IDHmut gliomas. Mitochondrial DNA copy number was increased in IDHmut tumours and did not change in recurrent tumours. Hierarchical clustering based on metabolism panel IHC revealed distinct subclasses of IDHmut and IDHwt gliomas with an impact on patient outcome. Further quantification of these markers allowed for the prediction of survival under anti-angiogenic therapy. CONCLUSION: A mitochondrial signature was associated with increased survival in all analyses, which could indicate tumour subgroups with specific metabolic vulnerabilities.
Subject(s)
Brain Neoplasms/genetics , Brain Neoplasms/metabolism , DNA Methylation/physiology , Glioma/genetics , Glioma/metabolism , Isocitrate Dehydrogenase/genetics , DNA, Mitochondrial/genetics , DNA, Mitochondrial/metabolism , Humans , Phenotype , TranscriptomeABSTRACT
BACKGROUND: To assess the potential of radiomic features to quantify components of blood in intraaortic vessels to non-invasively predict moderate-to-severe anemia in non-contrast enhanced CT scans. METHODS: One hundred patients (median age, 69 years; range, 19-94 years) who received CT scans of the thoracolumbar spine and blood-testing for hemoglobin and hematocrit levels ± 24 h between 08/2018 and 11/2019 were retrospectively included. Intraaortic blood was segmented using a spherical volume of interest of 1 cm diameter with consecutive radiomic analysis applying PyRadiomics software. Feature selection was performed applying analysis of correlation and collinearity. The final feature set was obtained to differentiate moderate-to-severe anemia. Random forest machine learning was applied and predictive performance was assessed. A decision-tree was obtained to propose a cut-off value of CT Hounsfield units (HU). RESULTS: High correlation with hemoglobin and hematocrit levels was shown for first-order radiomic features (p < 0.001 to p = 0.032). The top 3 features showed high correlation to hemoglobin values (p) and minimal collinearity (r) to the top ranked feature Median (p < 0.001), Energy (p = 0.002, r = 0.387), Minimum (p = 0.032, r = 0.437). Median (p < 0.001) and Minimum (p = 0.003) differed in moderate-to-severe anemia compared to non-anemic state. Median yielded superiority to the combination of Median and Minimum (p(AUC) = 0.015, p(precision) = 0.017, p(accuracy) = 0.612) in the predictive performance employing random forest analysis. A Median HU value ≤ 36.5 indicated moderate-to-severe anemia (accuracy = 0.90, precision = 0.80). CONCLUSIONS: First-order radiomic features correlate with hemoglobin levels and may be feasible for the prediction of moderate-to-severe anemia. High dimensional radiomic features did not aid augmenting the data in our exemplary use case of intraluminal blood component assessment. Trial registration Retrospectively registered.
Subject(s)
Anemia/diagnosis , Aorta/diagnostic imaging , Hematocrit , Hemoglobins/analysis , Image Interpretation, Computer-Assisted/methods , Machine Learning , Tomography, X-Ray Computed , Adult , Aged , Aged, 80 and over , Decision Trees , Female , Humans , Male , Middle Aged , Young AdultABSTRACT
OBJECTIVES: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). METHODS: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. RESULTS: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. CONCLUSIONS: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance. KEY POINTS: ⢠Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. ⢠Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. ⢠Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone.
Subject(s)
Diffusion Magnetic Resonance Imaging , Machine Learning , Multiparametric Magnetic Resonance Imaging , Prostatic Neoplasms/diagnostic imaging , Prostatic Neoplasms/surgery , Adult , Aged , Aged, 80 and over , Algorithms , Area Under Curve , Biopsy , Cluster Analysis , Humans , Male , Middle Aged , Principal Component Analysis , Prostate/diagnostic imaging , Prostatectomy , Reproducibility of Results , Retrospective Studies , Support Vector Machine , Treatment OutcomeABSTRACT
Despite its well-characterized side effects, dexamethasone is widely used in the pre-, peri- and postoperative neurosurgical setting due to its effective relief of tumor-induced symptoms through the reduction of tumor-associated edema. However, some patients show laboratory-defined dexamethasone induced elevation of white blood cell count, and its impact on glioblastoma progression is unknown. We retrospectively analyzed 113 patients with newly diagnosed glioblastoma to describe the incidence, risk factors and clinical features of dexamethasone-induced leukocytosis in primary glioblastoma patients. We further conducted an immunohistochemical analysis of the granulocyte and lymphocyte tumor-infiltration in the available corresponding histological sections. Patient age was identified to be a risk factor for the development of dexamethasone-induced leukocytosis (p < 0.05). The presence of dexamethasone-induced leukocytosis decreased overall survival (HR 2.25 95% CI [1.15-4.38]; p < 0.001) and progression-free survival (HR 2.23 95% CI [1.09-4.59]; p < 0.01). Furthermore, patients with dexamethasone-induced leukocytosis had significantly reduced CD15 + granulocytic- (p < 0.05) and CD3 + lymphocytic tumour infiltration (p < 0.05). We identified a subgroup of glioblastoma patients that are at particularly high risk for poor outcome upon dexamethasone treatment. Therefore, restrictive dosage or other edema reducing substances should be considered in patients with dexamethasone-induced leukocytosis.
Subject(s)
Antineoplastic Agents, Hormonal/adverse effects , Brain Neoplasms/drug therapy , Dexamethasone/adverse effects , Glioblastoma/drug therapy , Leukocytosis/etiology , Antineoplastic Agents, Hormonal/therapeutic use , Brain/drug effects , Brain/pathology , Brain/surgery , Brain Neoplasms/mortality , Brain Neoplasms/pathology , Brain Neoplasms/surgery , Dexamethasone/therapeutic use , Female , Glioblastoma/mortality , Glioblastoma/pathology , Glioblastoma/surgery , Humans , Incidence , Leukocytosis/diagnosis , Leukocytosis/mortality , Leukocytosis/pathology , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Factors , Survival AnalysisABSTRACT
PURPOSE: To assess the prognostic value of Parenchymal Blood Volume (PBV) in predicting survival, tumor response, and PBV response after transarterial chemoembolization (TACE). METHODS: A total of 137 patients with malignant liver tumors who were treated with TACE between 07/2016 and 07/2018 were evaluated. Computed tomography illustrations were reworked at a dedicated workstation to create a PBV map which was overlapped with the associated magnetic resonance image to determine tumor diameter and PBV. Patients were divided into two groups according to their initial PBV value: PBV < 50 or ≥ 50 ml/l. RESULTS: Retrospectively, for patients with at least 2 TACE and initial PBV < 50 ml/l (n = 27), the tumor volume, regardless of the primary tumor type, decreased by 13.26%, and PBV showed a decrease of 23.11%. For 84 patients with PBV ≥ 50 ml/l, the tumor volume decreased by 24.01%, and PBV showed a more substantial decrease of 44.69% (both p < 0.001). In the overall study population (n = 137), patients with an initial PBV ≥ 50 ml/l (n = 101) survived for an average of 15.05 months, while patients with an initial PBV < 50 ml/l (n = 36) survived for 10.01 months (p < 0.002). Subgroup analysis indicated that median survival in the HCC group was longer at PBV ≥ 50 ml/l. For CRC and other primary tumors, the survival time for high and low initial PBV was almost identical. CONCLUSION: Our study reveals a noteworthy correlation between high initial PBV values and a significant reduction in both relative and absolute tumor volume. This association suggests a potential prognostic indicator, indicating that elevated PBV may signify a more favorable response to transarterial chemoembolization (TACE). Additionally, patients with high initial PBV values experienced an extended overall survival time. Notably, the subgroup analysis highlighted a prolonged survival time in the HCC group with elevated initial PBV values. These findings underscore the potential significance of assessing PBV as a predictive factor in the context of TACE, especially in specific tumor entities such as HCC. Further investigations are essential to validate and extrapolate these observations to optimize patient outcomes.
Subject(s)
Blood Volume , Chemoembolization, Therapeutic , Liver Neoplasms , Tomography, X-Ray Computed , Humans , Liver Neoplasms/therapy , Liver Neoplasms/diagnostic imaging , Chemoembolization, Therapeutic/methods , Male , Female , Retrospective Studies , Prognosis , Middle Aged , Aged , Tomography, X-Ray Computed/methods , Magnetic Resonance Imaging/methods , Tumor Burden , Adult , Aged, 80 and over , Carcinoma, Hepatocellular/therapy , Carcinoma, Hepatocellular/diagnostic imagingABSTRACT
BACKGROUND: Dual-energy CT (DECT)-derived bone mineral density (BMD) of the distal radius and other CT-derived metrics related to bone health have been suggested for opportunistic osteoporosis screening and risk evaluation for sustaining distal radius fractures (DRFs). METHODS: The distal radius of patients who underwent DECT between 01/2016 and 08/2021 was retrospectively analyzed. Cortical Hounsfield Unit (HU), trabecular HU, cortical thickness, and DECT-based BMD were acquired from a non-fractured, metaphyseal area in all examinations. Receiver-operating characteristic (ROC) analysis was conducted to determine the area under the curve (AUC) values for predicting DRFs based on DECT-derived BMD, HU values, and cortical thickness. Logistic regression models were then employed to assess the associations of these parameters with the occurrence of DRFs. RESULTS: In this study, 263 patients (median age: 52 years; interquartile range: 36-64; 132 women; 192 fractures) were included. ROC curve analysis revealed a higher area under the curve (AUC) value for DECT-derived BMD compared to cortical HU, trabecular HU, and cortical thickness (0.91 vs. 0.61, 0.64, and 0.69, respectively; p <.001). Logistic regression models confirmed the association between lower DECT-derived BMD and the occurrence of DRFs (Odds Ratio, 0.83; p <.001); however, no influence was observed for cortical HU, trabecular HU, or cortical thickness. CONCLUSIONS: DECT can be used to assess the BMD of the distal radius without dedicated equipment such as calibration phantoms to increase the detection rates of osteoporosis and stratify the individual risk to sustain DRFs. In contrast, assessing HU-based values and cortical thickness does not provide clinical benefit.
Subject(s)
Fractures, Bone , Osteoporosis , Humans , Female , Middle Aged , Radius , Absorptiometry, Photon , Retrospective Studies , Osteoporosis/diagnostic imaging , Bone Density , Tomography, X-Ray Computed , Risk AssessmentABSTRACT
Foundation models in deep learning are characterized by a single large-scale model trained on vast amounts of data serving as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labelled datasets are often scarce. Here, we developed a foundation model for cancer imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of cancer imaging-based biomarkers. We found that it facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed conventional supervised and other state-of-the-art pretrained implementations on downstream tasks, especially when training dataset sizes were very limited. Furthermore, the foundation model was more stable to input variations and showed strong associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering new imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings.