RESUMO
Bone metastasis pain (BMP) is a severe chronic pain condition. Our previous studies on BMP revealed functional brain abnormalities. However, the potential effect of BMP on brain structure and function, especially gray matter volume (GMV) and related functional networks, have not yet been clearly illustrated. Voxel-based morphometry and functional connectivity (FC) analysis methods were used to investigate GMV and intrinsic FC differences in 45 right-handed lung cancer patients with BMP(+), 37 lung cancer patients without BMP(-), and 45 healthy controls (HCs). Correlation analysis was performed thereafter with all clinical variables by Pearson correlation. Compared to HCs, BMP(+) group exhibited decreased GMV in medial frontal gyrus (MFG) and right middle temporal gyrus (MTG). Compared with BMP(-) group, BMP(+) group exhibited reduced GMV in cerebelum_6_L and left lingual gyrus. However, no regions with significant GMV differences were found between BMP(-) and HCs groups. Receiver operating characteristic analysis indicated the potential classification power of these aberrant regions. Correlation analysis revealed that GMV in the right MTG was positively associated with anxiety in BMP(+) group. Further FC analysis demonstrated enhanced interactions between MFG/right MTG and cerebellum in BMP(+) patients compared with HCs. These results showed that BMP was closely associated with cerebral alterations, which may induce the impairment of pain moderation circuit, deficits in cognitive function, dysfunction of emotional control, and sensorimotor processing. These findings may provide a fresh perspective and further neuroimaging evidence for the possible mechanisms of BMP. Furthermore, the role of the cerebellum in pain processing needs to be further investigated.
Assuntos
Dor Crônica , Neoplasias Pulmonares , Humanos , Substância Cinzenta/diagnóstico por imagem , Neoplasias Pulmonares/complicações , Córtex Cerebral , Lobo TemporalRESUMO
BACKGROUND: Siamese network (SN) using longitudinal DCE-MRI for pathologic complete response (pCR) identification lack a unified approach to phases selection. PURPOSE: To identify pCR in early-stage NAC, using SN with longitudinal DCE-MRI and introducing IPS for phases selection. STUDY TYPE: Multicenter, longitudinal. POPULATION: Center A: 162 female patients (50.63 ± 8.41 years) divided 7:3 into training and internal validation cohorts. Center B: 61 female patients (50.08 ± 7.82 years) were used as an external validation cohort. FIELD STRENGTH/SEQUENCE: Center A: single vendor 3.0 T with a compressed-sensing volume interpolated breath-hold examination sequence. Center B: single vendor 1.5 T with volume interpolated breath-hold examination sequence. ASSESSMENT: Patients underwent DCE-MRI before and after two NAC cycles, with tumor regions of interest (ROI) manually delineated. Histopathology was the reference for pCR identification. Models developed included a clinical one, four SN models based on IPS-selected phases, and integrated models combining clinical and SN features. STATISTICAL TESTS: Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The DeLong test was used to compare AUCs. Net reclassification improvement and integrated discrimination improvement (IDI) tests were employed for performance comparison. P < 0.05 was considered significant. RESULTS: In internal and external validation cohorts, the clinical model showed AUCs of 0.760 and 0.718. SN and integrated models, with increasing phases via IPS, achieved AUCs ranging from 0.813 to 0.951 and 0.818 to 0.922. Notably, SN-3 and integrated-3 and integrated-4 outperformed the clinical model. However, input phases beyond 20% did not significantly enhance performance (IDI test: SN-4 vs. SN-3, P = 0.314 and 0.630; integrated-4 vs. integrated-3, P = 0.785 and 0.709). DATA CONCLUSION: The longitudinal multiphase DCE-MRI based on the SN demonstrates promise for identifying pCR in breast cancer. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 4.
RESUMO
There is growing evidence that chemotherapy may have a significant impact on the brains of breast cancer patients, causing changes in cortical morphology. However, early morphological alterations induced by chemotherapy in breast cancer patients are unclear. To investigate the patterns of those alterations, we compared female breast cancer patients (n = 45) longitudinally before (time point 0, TP0) and after (time point 1, TP1) the first cycle of neoadjuvant chemotherapy, using voxel-based morphometry (VBM) and surface-based morphometry (SBM). VBM and SBM alteration data underwent correlation analysis. We also compared cognition-related neuropsychological tests in the breast cancer patients between TP0 and TP1. Reductions in gray matter volume, cortical thickness, sulcal depth, and gyrification index were found in most brain areas, while increments were found to be mainly concentrated in and around the hippocampus. Reductions of fractal dimension mainly occurred in the limbic and occipital lobes, while increments mainly occurred in the anterior and posterior central gyrus. Significant correlations were found between altered VBM and altered SBM mainly in the bilateral superior frontal gyrus. We found no significant differences in the cognition-related neuropsychological tests before and after chemotherapy. The altered brain regions are in line with those associated with impaired cognitive domains in previous studies. We conclude that breast cancer patients showed widespread morphological alterations soon after neoadjuvant chemotherapy, despite an absence of cognitive impairments. The affected brain regions may indicate major targets of early brain damage after chemotherapy.
Assuntos
Neoplasias da Mama , Encéfalo/patologia , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/patologia , Feminino , Substância Cinzenta/diagnóstico por imagem , Substância Cinzenta/patologia , Humanos , Imageamento por Ressonância Magnética , Terapia NeoadjuvanteRESUMO
OBJECTIVES: This study aimed to develop and validate a nomogram based on extracellular volume (ECV) derived from computed tomography (CT) for predicting post-hepatectomy liver failure (PHLF) in patients with resectable hepatocellular carcinoma (HCC). METHODS: A total of 202 patients with resectable HCC from two hospitals were enrolled and underwent multiphasic contrast-enhanced CT before surgery. One hundred twenty-one patients from our hospital and 81 patients from another hospital were assigned to the training cohort and the validation cohort, respectively. CT-derived ECV was measured using nonenhanced and equilibrium-phase-enhanced CT images. The nomogram was developed with independent predictors of PHLF. Predictive performance and calibration were assessed by receiver operator characteristic (ROC) analysis and Hosmer-Lemeshow test, respectively. The Delong test was used to compare the areas under the curve (AUCs). RESULTS: CT-derived ECV had a strong correlation with the postoperative pathological fibrosis stage of the background liver (p < 0.001, r = 0.591). The nomogram combining CT-derived ECV, serum albumin (Alb), and serum total bilirubin (Tbil) obtained higher AUCs than the albumin-bilirubin (ALBI) score for predicting PHLF in both the training cohort (0.828 vs. 0.708; p = 0.004) and the validation cohort (0.821 vs. 0.630; p < 0.001). The nomogram showed satisfactory goodness of fit for PHLF prediction in the training and validation cohorts (p = 0.621 and 0.697, respectively). CONCLUSIONS: The nomogram contributes to the preoperative prediction of PHLF in patients with resectable HCC. KEY POINTS: ⢠CT-derived ECV had a strong correlation with the postoperative pathological fibrosis stage of the background liver. ⢠CT-derived ECV was an independent predictor of PHLF in patients with resectable HCC. ⢠The nomogram based on CT-derived ECV showed a superior prediction efficacy than that of clinical models (including Child-Pugh stage, MELD score, and ALBI score).
Assuntos
Carcinoma Hepatocelular , Falência Hepática , Neoplasias Hepáticas , Humanos , Carcinoma Hepatocelular/cirurgia , Carcinoma Hepatocelular/patologia , Hepatectomia/métodos , Nomogramas , Neoplasias Hepáticas/cirurgia , Neoplasias Hepáticas/patologia , Falência Hepática/etiologia , Bilirrubina , Tomografia Computadorizada por Raios X , Fibrose , Estudos RetrospectivosRESUMO
OBJECTIVE: To objectively and subjectively assess the image characteristics of noise-optimized virtual monoenergetic images [MEI (+)] and polyenergetic images (PEIs) from dual-energy computed tomography angiography and then to explore the clinical value of the optimal MEI (+) in preoperative perforator planning of anterolateral thigh (ALT) flap transplantation. METHODS: Sixteen patients (32 thighs) who underwent lower extremity run-off dual-energy computed tomography angiography for planning ALT flap transplantation were enrolled. One standard PEI and 5 MEI (+) in 10-keV intervals (range, 40-80 keV) were reconstructed. First, we compared the image quality subjectively (branch order, image quality, and vascular network continuity) and objectively (vascular attenuation, image noise, signal-to-noise ratio, and the contrast-to-noise ratio). Then, we compared the clinical value (number, type, source artery, pedicle length, caliber, and location of all sizable perforators) between the optimal MEI (+) and PEI groups. RESULTS: The 40-keV MEI (+) was rated superior subjective and objective image quality metrics to PEI (all P < 0.001). Compared with PEI, 40 keV MEI (+) increased the number of visible perforators, the percentage of perforators with identifiable types, and the measurable length of perforator pedicle (all P < 0.001). CONCLUSIONS: We recommend 40 keV MEI (+) for the visualization of perforators and their contribution to the selection and location of suitable perforators in preoperative planning for ALT flaps.
Assuntos
Angiografia por Tomografia Computadorizada , Imagem Radiográfica a Partir de Emissão de Duplo Fóton , Coxa da Perna , Angiografia por Tomografia Computadorizada/métodos , Humanos , Cuidados Pré-Operatórios , Imagem Radiográfica a Partir de Emissão de Duplo Fóton/métodos , Razão Sinal-Ruído , Transplante de Pele , Retalhos Cirúrgicos , Coxa da Perna/diagnóstico por imagem , Coxa da Perna/cirurgiaRESUMO
When compared with the traditional manual design method, the convolutional neural network has the advantages of strong expressive ability and it is insensitive to scale, light, and deformation, so it has become the mainstream method in the object detection field. In order to further improve the accuracy of existing object detection methods based on convolutional neural networks, this paper draws on the characteristics of the attention mechanism to model color priors. Firstly, it proposes a cognitive-driven color prior model to obtain the color prior features for the known types of target samples and the overall scene, respectively. Subsequently, the acquired color prior features and test image color features are adaptively weighted and competed to obtain prior-based saliency images. Finally, the obtained saliency images are treated as features maps and they are further fused with those extracted by the convolutional neural network to complete the subsequent object detection task. The proposed algorithm does not need training parameters, has strong generalization ability, and it is directly fused with convolutional neural network features at the feature extraction stage, thus has strong versatility. Experiments on the VOC2007 and VOC2012 benchmark data sets show that the utilization of cognitive-drive color priors can further improve the performance of existing object detection algorithms.
RESUMO
Visual tracking is a basic task in many applications. However, the heavy computation and low speed of many recent trackers limit their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter-based trackers restricts their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter-based trackers and propose an efficient and adaptive training sample update scheme. The training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with a difference hashing algorithm or discarded according to tracking result reliability. In addition, we expand our new tracker to long-term tracking. On the basis of the proposed model updating mechanism, we propose a new tracking state discrimination mechanism to accurately judge tracking failure, and resume tracking after the target is recovered. Experiments on OTB-2015, Temple Color 128 and UAV123 (including UAV20L) demonstrate that our tracker performs favorably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2 GHz).
RESUMO
PURPOSE: To investigate the feasibility of dual-energy CT (DECT)-derived extracellular volume (ECV) fraction for characterization of breast tumors, compared to apparent diffusion coefficient (ADC) and validated against histopathological findings. MATERIAL AND METHODS: The ECV fraction and ADC were prospectively assessed in patients with breast tumors using chest DECT and breast MRI. The diagnostic performance of ECV fraction and ADC was accessed in predicting breast histopathological subtypes and pathological complete response (pCR) status. Histopathological sections were analyzed by digital image analysis. Pearson's correlation analysis was used to correlate between DECT and histopathological ECV fractions. RESULTS: This study included 271 patients, with 314 breast lesions (61 benign and 253 malignant). The ECV fraction and ADC showed comparable area under the curve (AUC) for distinguishing benign from malignant lesions (p = 0.123) and invasive carcinoma from ductal carcinoma in situ (p = 0.115). There were significant differences in ECV fraction between different hormone receptors and Ki67 states (p = 0.001 â¼ 0.014), while ADC values only differed among various Ki67 states (p < 0.001). The ECV fraction was lower (p = 0.007), ADC was higher (p = 0.013) in pCR than in non-pCR group, with an AUC of 0.748 and 0.730 (p = 0.887), respectively. There was a positive correlation between DECT and histopathological ECV fractions (r = 0.615, p < 0.01). CONCLUSIONS: Routine chest DECT-derived ECV fraction is a viable quantitative imaging biomarker for predicting histopathological subtypes and pCR in patient with breast tumors, and correlated well with histopathology finding.
Assuntos
Neoplasias da Mama , Carcinoma Intraductal não Infiltrante , Humanos , Feminino , Antígeno Ki-67 , Imagem de Difusão por Ressonância Magnética/métodos , Mama/diagnóstico por imagem , Neoplasias da Mama/diagnóstico por imagemRESUMO
Pain is a pervasive symptom in lung cancer patients during the onset of the disease. This study aims to investigate the connectivity disruption patterns of the whole-brain functional network in lung cancer patients with cancer pain (CP+). We constructed individual whole-brain, region of interest (ROI)-level functional connectivity (FC) networks for 50 CP+ patients, 34 lung cancer patients without pain-related complaints (CP-), and 31 matched healthy controls (HC). Then, a ROI-based FC analysis was used to determine the disruptions of FC among the three groups. The relationships between aberrant FCs and clinical parameters were also characterized. The ROI-based FC analysis demonstrated that hypo-connectivity was present both in CP+ and CP- patients compared to HC, which were particularly clustered in the somatomotor and ventral attention, frontoparietal control, and default mode modules. Notably, compared to CP- patients, CP+ patients had hyper-connectivity in several brain regions mainly distributed in the somatomotor and visual modules, suggesting these abnormal FC patterns may be significant for cancer pain. Moreover, CP+ patients also showed increased intramodular and intermodular connectivity strength of the functional network, which could be replicated in cancer stage IV and lung adenocarcinoma. Finally, abnormal FCs within the prefrontal cortex and somatomotor cortex were positively correlated with pain intensity and pain duration, respectively. These findings suggested that lung cancer patients with cancer pain had disrupted connectivity in the intrinsic brain functional network, which may be the underlying neuroimaging mechanisms.
Assuntos
Encéfalo , Dor do Câncer , Neoplasias Pulmonares , Imageamento por Ressonância Magnética , Vias Neurais , Humanos , Neoplasias Pulmonares/fisiopatologia , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/complicações , Masculino , Feminino , Pessoa de Meia-Idade , Imageamento por Ressonância Magnética/métodos , Encéfalo/fisiopatologia , Encéfalo/diagnóstico por imagem , Vias Neurais/fisiopatologia , Vias Neurais/diagnóstico por imagem , Dor do Câncer/fisiopatologia , Mapeamento Encefálico/métodos , Rede Nervosa/fisiopatologia , Rede Nervosa/diagnóstico por imagem , Idoso , Conectoma/métodos , AdultoRESUMO
BACKGROUND: The association of coronary computed tomography angiography (CTA) and left ventricular (LV) myocardium measurements with cancer therapy-related cardiac dysfunction (CTRCD) is limited. OBJECTIVES: In this study, the authors sought to evaluate the changes in coronary arteries and LV myocardium in patients with left breast cancer (BC) receiving anthracycline with or without radiotherapy, with the use of coronary CTA. METHODS: Participants with left BC receiving anthracycline with or without radiotherapy were prospectively included. All participants underwent coronary CTA before and after treatment, including nonenhanced calcium-scoring scan, computed tomography angiography, and dual-energy late enhancement scan. Computed tomographic fractional flow reserve (CT-FFR), pericoronary adipose tissue (PCAT) CT attenuation, and LV segments' extracellular volume (ECV) before and after treatment were compared. Logistic regression analysis was used to assess the association between baseline coronary CTA parameters and CTRCD. RESULTS: Eighty participants receiving anthracycline and 59 participants receiving anthracycline with radiotherapy were included. CT-FFR decreased and PCAT CT attenuation and LV global and segments' ECV increased after treatment (all P < 0.05). After chemoradiotherapy, CT-FFR was lower and PCAT CT attenuation and LV myocardial ECV were higher than after chemotherapy. Twenty-four participants developed CTRCD. After adjustment by Heart Failure Association-International Cardio-Oncology Society risk in multivariable logistic regression analysis, baseline stenosis of the left anterior descending artery (LAD) (OR: 1.987 [95% CI: 1.322-2.768]; P = 0.021), left circumflex artery (LCX) (OR: 1.895 [95% CI: 1.281-2.802]; P = 0.031), and right coronary artery (RCA) (OR: 1.920 [95% CI: 1.405-2.811]; P = 0.028), and baseline CT-FFR of the LAD (OR: 3.425 [95% CI: 1.621-9.434]; P < 0.001), LCX (OR: 2.058 [95% CI: 1.030-5.076]; P = 0.006), and RCA (OR: 2.469 [95% CI: 1.232-6.944]; P = 0.004) were associated with CTRCD. CONCLUSIONS: Multiparameter coronary CTA contributes to comprehensive assessment of the coronary arteries and myocardium in patients with left BC receiving anthracycline with or without radiotherapy. Baseline coronary artery stenosis and CT-FFR might be imaging markers for predicting CTRCD in these patients.
RESUMO
OBJECTIVES: To explore the potential and performance of quantitative and semi-quantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on compressed sensing volumetric interpolated breath-hold (CS-VIBE) examination in the differential diagnosis of thyroid nodules. MATERIALS AND METHODS: A total of 208 patients with 259 thyroid nodules scheduled for surgery operation were prospectively recruited. All participants underwent routine and DCE-MRI. DCE-MRI quantitative parameters [Ktrans, Kep, Ve], semi-quantitative parameters [wash-in, wash-out, time to peak (TTP), arrival time (AT), peak enhancement intensity (PEI), and initial area under curve in 60 s (iAUC)] and time-intensity curve (TIC) types were analyzed. Differential diagnostic performances were assessed using area under the receiver operating characteristic curve (AUC) and compared with the Delong test. RESULTS: Ktrans, Kep, Ve, wash-in, wash-out, PEI and iAUC were statistically significantly different between malignant and benign nodules (P < 0.001). Among these parameters, ROC analysis revealed that Ktrans showed the highest diagnostic performance in the differentiation of benign and malignant nodules, followed by wash-in. ROC analysis also revealed that Ktrans achieved the best diagnostic performance for distinguishing papillary thyroid carcinoma (PTC) from non-PTC, follicular adenoma (FA) from non-FA, nodular goiter (NG) from non-NG, with AUC values of 0.854, 0.895 and 0.609, respectively. Type III curve is frequently observed in benign thyroid nodules, accounting for 77.4% (82/106). While malignant nodules are more common in type II, accounting for 57.5% (88/153). CONCLUSION: Thyroid examination using CS-VIBE based DCE-MRI is a feasible, non-invasive method to identify benign and malignant thyroid nodules and pathological types.
Assuntos
Suspensão da Respiração , Meios de Contraste , Estudos de Viabilidade , Imageamento por Ressonância Magnética , Nódulo da Glândula Tireoide , Humanos , Masculino , Feminino , Nódulo da Glândula Tireoide/diagnóstico por imagem , Pessoa de Meia-Idade , Adulto , Imageamento por Ressonância Magnética/métodos , Diagnóstico Diferencial , Idoso , Estudos Prospectivos , Curva ROC , Glândula Tireoide/diagnóstico por imagem , Glândula Tireoide/patologia , Interpretação de Imagem Assistida por Computador/métodos , Adulto Jovem , Reprodutibilidade dos Testes , Aumento da Imagem/métodos , Neoplasias da Glândula Tireoide/diagnóstico por imagem , Neoplasias da Glândula Tireoide/patologia , Sensibilidade e EspecificidadeRESUMO
PURPOSE: The purpose of this study was to assess the predictive performance of multiparametric magnetic resonance imaging (MRI) for molecular subtypes and interpret features using SHapley Additive exPlanations (SHAP) analysis. MATERIAL AND METHODS: Patients with breast cancer who underwent pre-treatment MRI (including ultrafast dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, diffusion kurtosis imaging and intravoxel incoherent motion) were recruited between February 2019 and January 2022. Thirteen semantic and thirteen multiparametric features were collected and the key features were selected to develop machine-learning models for predicting molecular subtypes of breast cancers (luminal A, luminal B, triple-negative and HER2-enriched) by using stepwise logistic regression. Semantic model and multiparametric model were built and compared based on five machine-learning classifiers. Model decision-making was interpreted using SHAP analysis. RESULTS: A total of 188 women (mean age, 53 ± 11 [standard deviation] years; age range: 25-75 years) were enrolled and further divided into training cohort (131 women) and validation cohort (57 women). XGBoost demonstrated good predictive performance among five machine-learning classifiers. Within the validation cohort, the areas under the receiver operating characteristic curves (AUCs) for the semantic models ranged from 0.693 (95% confidence interval [CI]: 0.478-0.839) for HER2-enriched subtype to 0.764 (95% CI: 0.681-0.908) for luminal A subtype, inferior to multiparametric models that yielded AUCs ranging from 0.771 (95% CI: 0.630-0.888) for HER2-enriched subtype to 0.857 (95% CI: 0.717-0.957) for triple-negative subtype. The AUCs between the semantic and the multiparametric models did not show significant differences (P range: 0.217-0.640). SHAP analysis revealed that lower iAUC, higher kurtosis, lower D*, and lower kurtosis were distinctive features for luminal A, luminal B, triple-negative breast cancer, and HER2-enriched subtypes, respectively. CONCLUSION: Multiparametric MRI is superior to semantic models to effectively predict the molecular subtypes of breast cancer.
Assuntos
Neoplasias da Mama , Aprendizado de Máquina , Imageamento por Ressonância Magnética Multiparamétrica , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Pessoa de Meia-Idade , Imageamento por Ressonância Magnética Multiparamétrica/métodos , Adulto , Idoso , Valor Preditivo dos TestesRESUMO
OBJECTIVES: To prospectively evaluate the value of tomoelastography in determining the underlying origins of uterine adenocarcinoma. METHODS: This prospective work was approved by our institutional review board, and all patients provided informed consent. 64 patients with histopathologically confirmed adenocarcinomas originated either from the cervix (CAC: cervical adenocarcinoma) or endometrium (EAC: endometrial adenocarcinoma) underwent MRI and tomoelastography examination on a 3.0 T MR scanner. To biomechanically characterize the adenocarcinoma, two MRE-derived parameters maps were provided in the tomoelastography, namely shear wave speed (c, m/s) and loss angle (φ, radian), which represented the stiffness and fluidity, respectively. The MRE-derived parameters were compared by using a two-tailed independent-sample t-test or Mann-Whitney U test. Five morphologic features were also analyzed by using the χ2 test. Logistic regression analysis was used to develop diagnosis models. Delong test was used to compare the receiver operating characteristic curves whith different diagnostic models and evaluate the diagnostic efficiency. RESULTS: CAC were significantly stiffer and behaved more fluid like than EAC (c: 2.58 ± 0.62 m/s vs.2.17 ± 0.72 m/s, p = 0.029, φ, 0.97 ± 0.19 rad vs.0.73 ± 0.26 rad, p < 0.0001). The diagnostic performance for distinguishing CAC from EAC was similar for c (AUC = 0.71) and for φ (AUC = 0.75). For distinguishing CAC from EAC, the AUC of tumor location was the higher than c and φ (AUC = 0.80). A cmobined model consisting of tumor location, c, and φ achieved the best diagnostic performance, with an AUC of 0.88 (77.27% sensitivity and 85.71% specificity). CONCLUSIONS: CAC and EAC displayed their unique biomechanical features. 3D multifrequency MRE provided added value to the conventional morphologic features in distinguishing the two types of diseases.
Assuntos
Adenocarcinoma , Técnicas de Imagem por Elasticidade , Neoplasias do Colo do Útero , Neoplasias Uterinas , Feminino , Humanos , Estudos Prospectivos , Imageamento por Ressonância Magnética , Neoplasias do Colo do Útero/diagnóstico por imagem , Adenocarcinoma/diagnóstico por imagem , Endométrio/diagnóstico por imagemRESUMO
OBJECTIVES: Posthepatectomy liver failure (PHLF) is a severe complication of liver resection. We aimed to develop and validate a model based on extracellular volume (ECV) and liver volumetry derived from computed tomography (CT) for preoperative predicting PHLF in resectable hepatocellular carcinoma (HCC) patients. METHODS: A total of 393 resectable HCC patients from two hospitals were enrolled and underwent multiphasic contrast-enhanced CT before surgery. A total of 281 patients from our hospital were randomly divided into a training cohort (n = 181) and an internal validation cohort (n = 100), and 112 patients from another hospital formed the external validation cohort. CT-derived ECV was measured on nonenhanced and equilibrium phase images, and liver volumetry was measured on portal phase images. The model is composed of independent predictors of PHLF. The under the receiver operator characteristic curve (AUC) and calibration curve were used to reflect the predictive performance and calibration of the model. Comparison of AUCs used the DeLong test. RESULTS: CT-derived ECV, measured future liver remnant (mFLR) ratio, and serum albumin were independent predictors for PHLF in resectable HCC patients. The AUC of the model was significantly higher than that of the ALBI score in the training cohort, internal validation cohort, and external validation cohort (all p < 0.001). The calibration curve of the model showed good consistency in the training cohort and the internal and external validation cohorts. CONCLUSIONS: The novel model contributes to the preoperative prediction of PHLF in resectable HCC patients. CRITICAL RELEVANCE STATEMENT: The novel model combined CT-derived extracellular volume, measured future liver remnant ratio, and serum albumin outperforms the albumin-bilirubin score for predicting posthepatectomy liver failure in patients with resectable hepatocellular carcinoma. KEY POINTS: ⢠CT-derived ECV correlated well with the fibrosis stage of the background liver. ⢠CT-derived ECV and mFLR ratio were independent predictors for PHLF in HCC. ⢠The AUC of the model was higher than the CT-derived ECV and mFLR ratio. ⢠The model showed a superior predictive performance than that of the ALBI score.
RESUMO
BACKGROUND: This study aimed to investigate the ability of quantitative parameter-derived dual-source dual-energy computed tomography (DS-DECT) combined with machine learning to distinguish between benign and malignant thyroid nodules. METHODS: Patients with thyroid nodules and pathological surgical results who underwent preoperative DS-DECT were selected. Quantitative parameter-derived DS-DECT was applied to classify benign and malignant nodules. Then, machine learning and binary logistic regression analysis models were constructed using the DS-DECT quantitative parameters to distinguish between benign and malignant nodules. The receiver operating characteristic curve was used to assess the diagnostic performance. The DeLong test was used to compare the diagnostic efficacy. RESULTS: One hundred and thirty patients with 139 confirmed thyroid nodules were involved in the study. The malignant group had a significantly higher iodine concentrationnodule (arterial phase) (P=0.001), normalized iodine concentration (arterial phase) (P=0.002), iodine concentration difference (P<0.001), spectral curve slope (nonenhancement) (P=0.007), spectral curve slope (arterial phase) (P=0.001), effective atomic number (nonenhancement) (P<0.001), and effective atomic number (arterial phase) (P=0.039) than the benign group. The binary logistic regression analysis model had an AUC (area under the curve) of 0.76, a sensitivity of 0.821, and a specificity of 0.667. The machine learning model had an AUC of 0.86, a sensitivity of 0.822, specificity of 0.791 in the training cohort, an AUC of 0.84, a sensitivity of 0.727, and specificity of 0.750 in the testing cohort. CONCLUSIONS: Multiple quantitative parameters of DS-DECT combined with machine learning could differentiate between benign and malignant thyroid nodules.
RESUMO
Objective: This study aimed to compare the diagnostic capacity between IVIM and DKI in differentiating malignant from benign thyroid nodules. Material and Methods: This study is based on magnetic resonance imaging data of the thyroid with histopathology as the reference standard. Spearman analysis was used to assess the relationship of IVIM-derived parameters D, f, D* and the DKI-derived parameters Dapp and Kapp. The parameters of IVIM and DKI were compared between the malignant and benign groups. Binary logistic regression analysis was performed to establish the diagnostic model, and receiver operating characteristic (ROC) curve analysis was subsequently performed. The DeLong test was used to compare the diagnostic effectiveness of different prediction models. Spearman analysis was used to assess the relationship of Ki-67 expression and parameters of IVIM and DKI. Results: Among the 93 nodules, 46 nodules were malignant, and 47 nodules were benign. The Dapp of DKI-derived parameter was related to the D (P < 0.001, r = 0.863) of IVIM-derived parameter. The Kapp of DKI-derived parameter was related to the D (P < 0.001, r = -0.831) of IVIM-derived parameters. The malignant group had a significantly lower D value (P < 0.001) and f value (P = 0.013) than the benign group. The malignant group had significantly higher Kapp and lower Dapp values (all P < 0.001). The D+f had an area under the curve (AUC) of 0.951. The Dapp+Kapp had an AUC of 0.943. The D+f+Dapp+Kapp had an AUC of 0.954. The DeLong test showed no statistical significance among there prediction models. The D (P = 0.007) of IVIM-derived parameters and Dapp (P = 0.045) of DKI-derived parameter were correlated to the Ki-67 expression. Conclusions: IVIM and DKI were alternative for each other in in differentiating malignant from benign thyroid nodules.
RESUMO
PURPOSE: The purpose of this study was to compare the efficacy of five non-invasive models, including three-dimensional (3D) convolutional neural network (CNN) model, to predict the spread through air spaces (STAS) status of non-small cell lung cancer (NSCLC), and to obtain the best prediction model to provide a basis for clinical surgery planning. MATERIALS AND METHODS: A total of 203 patients (112 men, 91 women; mean age, 60 years; age range 22-80 years) with NSCLC were retrospectively included. Of these, 153 were used for training cohort and 50 for validation cohort. According to the image biomarker standardization initiative reference manual, the image processing and feature extraction were standardized using PyRadiomics. The logistic regression classifier was used to build the model. Five models (clinicopathological/CT model, conventional radiomics model, computer vision (CV) model, 3D CNN model and combined model) were constructed to predict STAS by NSCLC. Area under the receiver operating characteristic curves (AUC) were used to validate the capability of the five models to predict STAS. RESULTS: For predicting STAS, the 3D CNN model was superior to the clinicopathological/CT model, conventional radiomics model, CV model and combined model and achieved satisfactory discrimination performance, with an AUC of 0.93 (95% CI: 0.70-0.82) in the training cohort and 0.80 (95% CI: 0.65-0.86) in the validation cohort. Decision curve analysis indicated that, when the probability of the threshold was over 10%, the 3D CNN model was beneficial for predicting STAS status compared to either treating all or treating none of the patients within certain ranges of risk threshold CONCLUSION: The 3D CNN model can be used for the preoperative prediction of STAS in patients with NSCLC, and was superior to the other four models in predicting patients' risk of developing STAS.
Assuntos
Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Masculino , Humanos , Feminino , Adulto Jovem , Adulto , Pessoa de Meia-Idade , Idoso , Idoso de 80 Anos ou mais , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Neoplasias Pulmonares/diagnóstico por imagem , Estudos Retrospectivos , Tomografia Computadorizada por Raios X/métodos , Redes Neurais de ComputaçãoRESUMO
RATIONALE AND OBJECTIVES: To develop and validate a radiomics model, a clinical-semantic model and a combined model by using standard methods for the pretreatment prediction of distant metastasis (DM) in patients with non-small-cell lung cancer (NSCLC) and to explore whether the combined model provides added value compared to the individual models. MATERIALS AND METHODS: This retrospective study involved 356 patients with NSCLC. According to the image biomarker standardization initiative reference manual, we standardized the image processing and feature extraction using in-house software. Finally, 6692 radiomics features were extracted from each lesion based on contrast-enhanced chest CT images. The least absolute shrinkage selection operator and the recursive feature elimination algorithm were used to select features. The logistic regression classifier was used to build the model. Three models (radiomics model, clinical-semantic model and combined model) were constructed to predict DM in NSCLC. Area under the receiver operating characteristic curves were used to validate the ability of the three models to predict DM. A visual nomogram based on the combined model was developed for DM risk assessment in each patient. RESULTS: The receiver operating characteristic curve showed predictive performance for DM of the radiomics model (area under the curve [AUC] values for training and validation were 0.76 [95% CI, 0.704 - 0.820] and 0.76 [95% CI, 0.653 - 0.858], respectively). The combined model had AUCs of 0.78 (95% CI, 0.723 - 0.835) and 0.77 (95% CI, 0.673 - 0.870) in the training and validation cohorts, respectively. Both the radiomics model and combined model performed better than the clinical-semantic model (0.70 [95% CI, 0.634 - 0.760] and 0.67 [95% CI, 0.554 - 0.787] in the training and validation cohorts, respectively). CONCLUSION: The radiomics model and combined model may be useful for the prediction of DM in patients with NSCLC.
Assuntos
Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Carcinoma Pulmonar de Células não Pequenas/patologia , Humanos , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/patologia , Nomogramas , Estudos Retrospectivos , Tomografia Computadorizada por Raios X/métodosRESUMO
PURPOSE: The purpose of this study was to investigate whether amide proton transfer (APT) imaging and intravoxel incoherent motion (IVIM) imaging can predict tumor response to concurrent chemoradiotherapy (CCRT) in patients with squamous cell carcinoma of the cervix (SCCC). MATERIAL AND METHODS: Fifty-nine women (mean age, 54 years ± 10 [standard deviation] years; age range: 32-81 years) with pathologically confirmed SCCC underwent magnetic resonance imaging examination of the pelvis including APT and IVIM before concurrent chemoradiotherapy. They were divided into complete remission (CR) and non-CR groups according to therapeutic effect. APT values and IVIM-derived parameters were measured. Intra- and interobserver agreement for IVIM and APT parameters was assessed using intraclass correlation coefficient (ICC) The independent samples t-test was performed to compare the evaluated parameters between the two groups. Predictive performance for treatment response was evaluated by receiver operator characteristic (ROC) curve analysis. RESULTS: There were 38 and 21 patients in the non-CR and CR groups, respectively. Excellent interobserver and intraobserver agreement were obtained for all IVIM and APT parameters, with ICCs ranging from 0.844 to 0.962. Perfusion fraction (f) and APT values were lower in the CR group compared with the non-CR group (both P < 0.05). The combination of f and APT values showed good diagnostic performances in predicting response to concurrent chemoradiotherapy, with an area under the ROC curve of 0.852 (95% CI: 0.744-0.961), 79% sensitivity (95% CI: 63-90%), 90% specificity (95% CI: 70-99%) and 83% accuracy (95% CI: 71-92%). CONCLUSION: APT and IVIM imaging may serve as noninvasive tools for predicting response to concurrent chemoradiotherapy in patients with SCCC.
Assuntos
Carcinoma de Células Escamosas , Neoplasias do Colo do Útero , Humanos , Feminino , Adulto , Pessoa de Meia-Idade , Idoso , Idoso de 80 Anos ou mais , Prótons , Colo do Útero/patologia , Amidas , Quimiorradioterapia/métodos , Carcinoma de Células Escamosas/diagnóstico por imagem , Carcinoma de Células Escamosas/terapia , Neoplasias do Colo do Útero/diagnóstico por imagem , Neoplasias do Colo do Útero/terapia , Neoplasias do Colo do Útero/patologia , Imagem de Difusão por Ressonância Magnética/métodosRESUMO
BACKGROUND: Multiparametric dual-energy computed tomography (mpDECT) is widely used to differentiate various kinds of tumors; however, the data regarding its diagnostic performance with machine learning to diagnose breast tumors is limited. We evaluated univariate analysis and machine learning performance with mpDECT to distinguish between benign and malignant breast lesions. METHODS: In total, 172 patients with 214 breast lesions (55 benign and 159 malignant) who underwent preoperative dual-phase contrast-enhanced DECT were included in this retrospective study. Twelve quantitative features were extracted for each lesion, including CT attenuation (precontrast, arterial, and venous phases), the arterial-venous phase difference in normalized effective atomic number (nZeff), normalized iodine concentration (NIC), and slope of the spectral Hounsfield unit (HU) curve (λHu). Predictive models were developed using univariate analysis and eight machine learning methods [logistic regression, extreme gradient boosting (XGBoost), stochastic gradient descent (SGD), linear discriminant analysis (LDA), adaptive boosting (AdaBoost), random forest (RF), decision tree, and linear support vector machine (SVM)]. Classification performances were assessed based on the area under the receiver operating characteristic curve (AUROC). The best performances of the conventional univariate analysis and machine learning methods were compared using the Delong test. RESULTS: The univariate analysis showed that the venous phase λHu had the highest AUROC (0.88). Machine learning with mpDECT achieved an excellent and stable diagnostic performance, as shown by the mean classification performances in the training dataset (AUROC, 0.88-0.99) and testing (AUROC, 0.83-0.96) datasets. The performance of the AdaBoost model based on mpDECT was more stable than the other machine learning models and superior to the univariate analysis (AUROC, 0.96 vs. 0.88; P<0.001). CONCLUSIONS: The performance of the AdaBoost classifier based on mpDECT data achieved the highest mean accuracy compared to the other machine learning models and univariate analysis in differentiating between benign and malignant breast lesions.