Résumé
Radiomics-based early prediction and treatment efficacy evaluation is critical for personalized treatment strategies in patients with colorectal cancer liver metastases(CCLM).Owing to the high artificial intelligence(AI)participation,repeatability,and reliable perform-ance,deep learning(DL)based on convolutional neural networks enhances the predictive efficacy of the models,enabling its potential clinic-al application more promising.Subsequent to the gradual construction of a multimodal fusion model and multicenter large sample database,radiomics and DL will become increasingly essential in the management of CCLM.This review focuses on the main steps of radiomics and DL,and summarizes the value of its application in early state prediction and treatment efficacy evaluation of different treatment modalities in CCLM,we also look forward to the potential of its in-depth application in the clinical management of CCLM.
Résumé
Mammography has played an essential role in the screening and treatment of breast cancer. However, the application of X-rays will also increase the risks of breast cancer while improving its detection rate. Moreover, the risks will increase with an increase in the radiation dose. Since the glandular tissue in breasts is sensitive to radiation, the evaluation of the average glandular dose (AGD) in mammography has attracted considerable international attention. Compared to relatively mature dosimetric studies on traditional two-dimension mammography and digital breast tomosynthesis, the method for the dose evaluation of the new cone beam CT for breasts are still subjected to research. This paper reviews and explores the current status of studies on the assessment method and relevant influencing factors of AGD under different types of mammography equipment.
Résumé
Objective To establish a method to characterize the size and density of the female breast based on three-dimensional images of breast cone-beam computed tomography (CBCT), and describe the breast characteristics of women in a region of China, and to explore its value in dosimetric assessment for breast CBCT examinees. Methods We retrospectively surveyed the breast CBCT images of 203 women in a grade A tertiary hospital in a southwestern city of China from January 2021 to March 2023. The effective diameter of the breast at the chest-wall (Deff), chest wall-to-nipple length (CNL), the effective diameter of the breast at half of CNL (Dh/2), breast volume (BV), glandular volume (GV), and volumetric breast density (VBD) were measured using the specific tools of the Koning Imaging Viewer system. The differences between groups were assessed using the Kruskal-Wallis H test. The correlation between variables was assessed using the Spearman’s correlation coefficient. Results The median values of Deff, Dh/2, CNL, BV, GV, and VBD of the surveyed population were 11.9 cm, 8.3 cm, 6.5 cm, 327.7 cm3, 47.0 cm3, and 15.4%, respectively. GV and VBD had significant negative correlations with age. Deff, Dh/2, CNL, and BV were significantly negatively correlated with VBD. Conclusion We established a quantitative method to analyze female breast characteristics based on three-dimensional breast CBCT images, and preliminarily characterized the female breast in a region of China, which can provide methodological support for the investigation of female breast characteristics in various regions of China in the future.
Résumé
Objective:To investigate the computed tomography(CT)and magnetic resonance imaging(MRI)features of retroperitoneal ded-ifferentiated liposarcoma(DDL),and improve the understanding of DDL and the accuracy of preoperative diagnosis.Methods:Clinical and imaging features of 25 patients with retroperitoneal DDL from Tianjin Medical University Cancer Institute&Hospital,confirmed by patho-logy from January 2012 to June 2022,were retrospectively analyzed.Results:Among 25 cases of retroperitoneal DDL,19 and 6 had single and multiple tumors,respectively and 10 and 15 were oval and irregular shaped tumors,respectively.Most lesions had unclear boundaries,with 15 cases invading the surrounding tissues and organs.Small vessel shadows were visible in 15 cases,while calcifications or ossifications were observed in 7 cases,and cystic necrosis was observed in only 3 cases.Enhanced scanning exhibits a centripetal and progressive con-tinuous augmentation characteristic defined as"slow in and slow out."According to its manifestations in CT and MRI,it can be divided into two types:type I(soft tissue mass type),where the tumor has a soft tissue component with no fat content(14 cases);and type Ⅱ(fat con-taining),where the tumors exhibit both soft tissue and adipose components,most of which are clearly defined and rarely present in a mosa-ic shape.In abnormal fat areas,cord-like fibrous septa can be seen.Among them,the intratumoral fat composition<50%was Ⅱa type(10 cases).Intratumor fat composition≥50%was type Ⅱb(1 case).Conclusions:Combined with imaging classification,a comprehensive ana-lysis of the CT and MRI imaging characteristics of retroperitoneal DDL is of great value for its preoperative qualitative diagnosis.
Résumé
Objective:To explore the predictive value of qualitative and quantitative parameters of dual-layer spectral detector CT plain scan on the invasiveness of pure ground-glass pulmonary nodules (pGGNs).Methods:Clinical and imaging data of 113 patients (119 pGGNs) with pathology-proven lung adenocarcinoma who underwent preoperative dual-layer spectral detector CT plain scan in Tianjin Medical University Cancer Institute and Hospital from November 2019 to December 2020 were retrospectively analyzed. According to invasiveness, pGGNs were divided into non-invasive adenocarcinoma (non-IA) group ( n=66) and IA group ( n=53). The non-IA group included atypical adenomatous hyperplasia ( n=10), adenocarcinoma in situ ( n=26) and minimally invasive adenocarcinoma ( n=30). The qualitative parameters were nodule shape, lung-tumor interface, lobulation, spiculation, pleural retraction, bubblelike lucency, air bronchogram and vascular abnormality. The quantitative parameters included nodule size, effective atomic number (Z eff), CT value on 120 kVp images (CT 120 kVp) and virtual monoenergetic images from 40 keV to 200 keV (CT 40 keV-CT 200 keV), and slope of energy spectrum curve (λHU). The χ 2 test, Mann-Whitney U test and independent sample t test were used to analyze the parameter differences between non-IA group and IA group. Multivariate logistic regression analysis was performed to screen out independent predictors. Receiver operating characteristic (ROC) curve was used to assess the diagnostic efficacy of single predictor and combined independent factors for the invasiveness of pGGN. Results:Significant differences were found in nodule shape, lobulation, air bronchogram, vascular abnormality, nodules size, Z eff, CT 120 kVp and CT 40 keV-CT 200 keV between non-IA and IA groups ( P<0.05). The maltivariate logistic regression analysis showed that nodule size [odds ratio 9.269, 95% confidence interval (CI) 1.640-52.395, P=0.012] CT 200 keV (odds ratio 1.012, 95%CI 1.006-1.019, P<0.001) as well as vascular abnormality sign (odds ratio 4.940, 95%CI 1.358-17.969, P=0.015) were independent predictors of pGGN invasiveness. ROC curve analysis of a single independent predictor and a combination of the three factors showed that the area under the curve (AUC) of the combination of three factors predicting the invasiveness of pGGN was significantly higher than the AUC of vascular abnormality sign ( Z=4.01, P<0.001) and CT 200 keV ( Z=3.25, P=0.001), while there was no significant difference in AUC between the combination of the three factors and nodule size ( Z=1.94, P=0.052). The AUC of the combination of the three independent predictors was 0.909, and the sensitivity and specificity for predicting pGGN invasion were 81.1% and 86.4%, respectively, using a threshold of 0.505. Conclusion:The combination of qualitative and quantitative parameters of dual-layer spectral detector CT plain scan shows a high predictive value for the invasiveness of pGGNs.
Résumé
Objective:To establish a clinical diagnostic scoring model for preoperative predicting hepatocellular carcinoma (HCC) microvascular invasion (MVI) based on gadolinium-ethoxybenzyl-diethylenetriamine pentacetic acid (Gd-EOB-DTPA) enhanced MRI, and verify its effectiveness.Methods:From January 2014 to December 2020, a total of 251 cases with pathologically confirmed HCC from Tianjin First Central Hospital and Jilin University First Hospital were retrospectively collected to serve as the training set, while 57 HCC patients from Tianjin Medical University Cancer Hospital were recruited as an independent external validation set. The HCC patients were divided into MVI positive and MVI negative groups according to the pathological results. The tumor maximum diameters and apparent diffusion coefficient (ADC) values were measured. On the Gd-EOB-DTPA MRI images, tumor morphology, peritumoral enhancement, peritumoral low intensity (PTLI), capsule, intratumoral artery, intratumoral fat, intratumoral hemorrhage, and intratumoral necrosis were observed. Univariate analysis was performed using the χ 2 test or the independent sample t-test. The independent risk factors associated with MVI were obtained in the training set using a multivariate logistic analysis. Points were assigned to each factor according to the weight value to establish a preoperative score model for predicting MVI. The receiver operating characteristic (ROC) curve was used to determine the score threshold and to verify the efficacy of this scoring model in predicting MVI in the independent external validation set. Results:The training set obtained 98 patients in the MVI positive group and 153 patients in the MVI negative group, while the external validation set obtained 16 patients in the MVI positive group and 41 patients in the MVI negative group. According to logistic analysis, tumor maximum diameter>3.66 cm (OR 3.654, 95%CI 1.902-7.018), hepatobiliary PTLI (OR 9.235, 95%CI 4.833-16.896) and incomplete capsule (OR 6.266, 95%CI 1.993-9.345) were independent risk factors for MVI in HCC, which were assigned scores of 3, 4 and 2, respectively. The total score ranged from 0 to 9. In the external validation set, ROC curve analysis showed that the area under the curve of the scoring model was 0.918 (95%CI 0.815-0.974, P=0.001). When the score>4 was used as the threshold, the accuracy, sensitivity, and specificity of the model in predicting MVI were 84.2%, 81.3%, and 85.4%, respectively. Conclusions:A scoring model based on Gd-EOB-DTPA-enhanced MRI provided a convenient and reliable way to predict MVI preoperatively.
Résumé
Objective:To investigate the value of logistic regression model based on the features of cone-beam breast CT (CBBCT) for the identification of benign and malignant masses in dense breast.Methods:The data of 106 patients (130 masses) with dense breast who underwent contrast-enhanced CBBCT examination and obtained pathological results from May 2011 to August 2020 were retrospectively analyzed as the training set. From August 2020, the data of 49 patients (54 masses) who met the same criteria were prospectively and consecutively collected and used as the validation set. Taking pathological results as the gold standard, the training set was divided into benign and malignant groups. The t-test, χ 2 test and Fisher′s exact test were used to compare the differences in CBBCT image characteristics between the two groups in the training set. A binary logistic regression model was established by multivariate analysis. ROC curves were used to assess the diagnostic efficacy of the model as a whole in the training and validation sets and the diagnostic efficacy of each feature in the model, and the cut-off value of the intensity (ΔCT) value was determined. The H-L method was used to test the goodness of fit of the model. Decision curve analysis (DCA) was drawn to validate the clinical power of the model. Results:Univariate analysis showed that the breast parenchymal background enhancement (BPE), shape, margin, lobulation, spiculation, density, calcifications, ΔCT value, enhancement pattern, non-mass enhancement, ipsilateral increased vascularity (IIV), and peripheral vascular signs had statistical difference between benign group and malignant group ( P<0.05). BPE, margin, ΔCT value and IIV were included in the multivariate analysis, the equation was logit( P′)=-8.510+0.830×BPE+0.822×margin+1.919× ΔCT+1.896 × IIV. The are a under curve of the model in the training set was 0.879 ( P<0.001) and in the validation set was 0.851 ( P=0.001). The are a under curve of BPE, margin, ΔCT value, and IIV in the diagnosis of malignant mass were 0.645, 0.711, 0.712, 0.775 (all P<0.05); the best cut-off value of ΔCT was 50.38 HU. The fit of this model was good ( P = 0.776). The DCA curve showed that when the risk threshold was 0.05-0.97, the net benefit rate was>0, and this model had some clinical value. Conclusion:The logistic regression model based on the features of CBBCT is helpful to distinguish benign and malignant masses in dense breasts.
Résumé
Objective:To analyze and explore the common radiomics features of radiation pneumonitis (RP) in patients with lung cancer and esophageal cancer, and then establish a prediction model that can predict the occurrence of RP in two types of cancer after radiotherapy.Methods:Clinical data of 100 patients with stage Ⅲ lung cancer and 100 patients with stage Ⅲ esophageal cancer who received radical radiotherapy were retrospectively analyzed. The RP was graded by imaging data and clinical information during follow-up, and the planning CT images were collected. The whole lung was used as the volume of interest to extract radiomics features. The radiomics features, clinical and dosimetric parameters related to RP were analyzed, and the model was constructed by machine learning.Results:A total of 1691 radiomics features were extracted from CT images. After ANOVA and LASSO dimensionality reduction in lung cancer and esophageal cancer patients, 8 and 6 radiomics features associated with RP were identified, and 5 of them were the same. Using the random forest to construct the prediction model, lung cancer and esophageal cancer were alternately used as the training and validation sets. The AUC values of esophageal cancer and lung cancer as the independent validation set were 0.662 and 0.645.Conclusions:It is feasible to construct a common prediction model of RP in patients with lung cancer and esophageal cancer. Nevertheless, it is necessary to further expand the sample size and include clinical and dosimetric parameters to increase its accuracy, stability and generalization ability.
Résumé
Objective:To study the predictive value of combining alpha-feto protein (AFP) with contrast-enhanced MRI imaging features in predicting incidence of microvascular invasion (MVI) in patients with hepatocellular carcinoma.Methods:The data of 206 patients with hepatocellular carcinoma treated at Tianjin Medical University Cancer Institute and Hospital from January 2017 to April 2019 were retrospectively analyzed. There were 179 males and 27 females, with an average age of 58.7 years. The roles of preoperative MRI imaging features and clinical data on predicting the incidence of MVI in patients with hepatocellular carcinoma were evaluated by univariate and multivariate logistic regression analyses. Multivariable regression analysis was then used to plot a nomogram.Results:There were 86 patients (41.7%) with MVI positivity and 120 patients (58.3%) with MVI negativity. Multivariate logistic regression analysis showed that AFP >400 μg/L ( OR=3.318, 95% CI: 1.243-8.855, P=0.017), two-trait predictor of venous invasion (TTPVI) ( OR=13.111, 95% CI: 6.797-28.119, P<0.001), diffusion weighted imaging/T 2 weighted imaging (DWI/T 2WI) mismatch ( OR=17.233, 95% CI: 4.731-44.490, P<0.001), and rim enhancement( OR=5.665, 95% CI: 2.579-18.152, P=0.013) predicted increased risks of MVI in patients with hepatocellular carcinoma. The constructed nomogram directly predicted the risk of MVI in these patients. Conclusions:AFP>400 μg/L, TTPVI, DWI/T 2WI mismatch and rim enhancement were independent risk factors in predicting MVI in patients with hepatocellular carcinoma. This predictive model of MVI which was based on multivariate logistic regression analysis was helpful to clinicians in making individualized treatment plans for patients with hepatocellular carcinoma.
Résumé
Objective:To investigate the value of radiomics in image quality control with low-dose CT examination of solid pulmonary nodules.Methods:Images were acquired on CT750 HD scanner, and chest pulmonary nodules phantom were scanned at different tube voltage and tube current. The radiation dose CTDI vol under different scanning conditions were recorded, as well as CNR and SNR of each scanning sequence. The variation of radiation dose, noise, tube voltage and tube current were analyzed. All data were analyzed by radiomics analysis software. R language statistics software was adopted to analyze the extracted features by principal component analysis (PCA), and the characteristic parameters with the largest contribution rate to image quality were selected for analysis. One-way ANOVA was used to analyze all the important characteristic parameters to reveal the difference of characteristic parameters under different tube voltages. Finally, the post-test method was used to find out the differences among different tube voltage groups. Results:Radiation dose rised linearly with the increase of tube current and tube voltage. Although the overall change trend of SNR and CNR in pulmonary nodules was linearly related to the change of tube voltage and tube current, there was no clear change trend threshold at low dose, which could not accurately evaluate the image quality under low radiation. Both CNR and SNR cannot evaluate the image quality effectively, and have no practiced value for optimizing the low dose scanning parameters. There main components including Uniformity, Voxel Value Sum, and Haralick Correlation extracted by radiomics analysis software were proved to play a critical role in image quality control. The cumulative contribution rate of variance was 89.20% and the eigen values were greater than 1. Uniformity curve of characteristic parameter showed that the trend of change was correlated with the change of tube voltage and tube current, and the stability and consistency were good. Uniformity one-way ANOVA analysis showed that when the tube voltage reduced from 140 to 120 kVp, there was no difference ( P=0.117) in the uniformity, while from 120 to 80 kVp, significant differences revealed ( P<0.001). Considering tube current, no significant variation was observed in uniformity when current was greater than 90 mA, which indicated that tube current of 90 mA could lead to better image quality. Conclusion:Radiomics analysis can effectively evaluate and control the CT image quality of low dose solid pulmonary nodules.
Résumé
Objective@#To investigate the value of radiomics in image quality control with low-dose CT examination of solid pulmonary nodules.@*Methods@#Images were acquired on CT750 HD scanner, and chest pulmonary nodules phantom were scanned at different tube voltage and tube current. The radiation dose CTDIvol under different scanning conditions were recorded, as well as CNR and SNR of each scanning sequence. The variation of radiation dose, noise, tube voltage and tube current were analyzed. All data were analyzed by radiomics analysis software. R language statistics software was adopted to analyze the extracted features by principal component analysis (PCA), and the characteristic parameters with the largest contribution rate to image quality were selected for analysis. One-way ANOVA was used to analyze all the important characteristic parameters to reveal the difference of characteristic parameters under different tube voltages. Finally, the post-test method was used to find out the differences among different tube voltage groups.@*Results@#Radiation dose rised linearly with the increase of tube current and tube voltage. Although the overall change trend of SNR and CNR in pulmonary nodules was linearly related to the change of tube voltage and tube current, there was no clear change trend threshold at low dose, which could not accurately evaluate the image quality under low radiation. Both CNR and SNR cannot evaluate the image quality effectively, and have no practiced value for optimizing the low dose scanning parameters. There main components including Uniformity, Voxel Value Sum, and Haralick Correlation extracted by radiomics analysis software were proved to play a critical role in image quality control. The cumulative contribution rate of variance was 89.20% and the eigen values were greater than 1. Uniformity curve of characteristic parameter showed that the trend of change was correlated with the change of tube voltage and tube current, and the stability and consistency were good. Uniformity one-way ANOVA analysis showed that when the tube voltage reduced from 140 to 120 kVp, there was no difference (P=0.117) in the uniformity, while from 120 to 80 kVp, significant differences revealed (P<0.001). Considering tube current, no significant variation was observed in uniformity when current was greater than 90 mA, which indicated that tube current of 90 mA could lead to better image quality.@*Conclusion@#Radiomics analysis can effectively evaluate and control the CT image quality of low dose solid pulmonary nodules.
Résumé
Tumor neovascularization plays an important role in the occurrence, development and metastasis of cancer. Non-invasive quantification and detection of tumor neovascularization is crucial for early diagnosis and prognosis assessment of cancer. Targeted molecular imaging has arisen in vascular targeting imaging and precise treatment based on the molecular characteristics of neovascularization. Aminopeptidase N (APN, or CD13) is a multifunctional membrane-bound exopeptidase that is overexpressed in neovascular endothelial cells and some tumor cells but rarely expressed in normal blood vessels, which makes it a potential target for tumor neovascularization imaging and anti-angiogenic therapy. This review summarizes the application progress and the future development trend of target molecular imaging and precise treatment based on CD13.
Résumé
Objective: To investigate the prognostic value of chest computed tomography (CT) characteristics in crizotinib-treated pa-tients with advanced non-small cell lung cancer (NSCLC). Methods: Forty-seven patients with advanced ALK-rearranged NSCLC who re-ceived crizotinib treatment from January 2014 to March 2017 were enrolled in this retrospective study. Pre-treatment CT characteris-tics were evaluated. Patients were followed up after crizotinib treatment, and the best overall response and progression-free survival (PFS) were assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST version 1.1). Results: The median PFS of all patients was 10 months. There was no association between CT characteristics and response. In univariate analysis, large tumor size (P=0.009), central type (P=0.002), consolidation of surrounding lung tissue (P=0.002), pleural effusion (P=0.001), and lymphangitic carcino-matosis (P=0.019) suggested a poor prognosis. Multivariate Cox regression analysis showed that location (hazard ratio, 3.219; 95% con-fidence interval: 1.517-6.833; P=0.002) was an independent prognostic predictor. Conclusions: Pre-treatment CT characteristics are useful in predicting the PFS of crizotinib-treated patients with advanced NSCLC harboring ALK rearrangement.
Résumé
To investigate the correlation between magnetic resonance imaging (MRI) features and tumor risk grade of gas-trointestinal stromal tumors (GISTs). Methods: Between September 2007 to December 2017, 54 patients who underwent MRI and were pathologically diagnosed in Tianjin Medical University Cancer Institute and Hospital were retrospectively reviewed. We analyzed MRI features including the size, location, shape, boundary, and growth pattern of the tumor; cystic necrosis; metastasis; T1WI and T2WI signal intensities; enhancement signal intensity-time (SIT) curve pattern; and average apparent diffusion coefficient (ADC) val-ues. The MRI features were compared with the tumor risk grade. Results: Of the 54 cases, 16 were of low-risk grade, 13 were of inter-mediate-risk grade, and 25 were of high-risk grade. Statistical analysis showed that tumor size, location, shape, boundary, cystic necro-sis, signal intensity, and average ADC values were correlated with tumor risk grade (P<0.05). However, tumor growth pattern, metasta-sis, and enhancement SIT curve pattern were not correlated with tumor risk grade (P>0.05). GISTs with higher aggressive features were more likely to have larger size, irregular shape, unclear boundary, cystic necrosis, heterogeneous signal intensity, and lower ADC values on MRI. Conclusions: MRI has the potential to predict the risk grade of GISTs before surgery, thereby guiding clinical manage-ment, and evaluating prognosis.
Résumé
Objective To evaluate the accuracy of cone?beam breast CT (CBBCT) on tumor sizing in patients with invasive breast carcinoma and analyze the influence factors. Methods The preoperative CBBCT images of 82 female patients (85 breast lesions) with invasive breast carcinoma confirmed by pathology from November, 2011 to December, 2017 in Tianjin Medical University Cancer Hospital were included in this retrospective study. All the patients underwent the pathology and immunohistochemical test after operation. Tumor size estimation were performed on preoperative CBBCT images. Referring to tumor size measurement on pathology, all the lesions were divided into two groups. Concordance was defined as the discrepancy in diameter less than 0.5 cm, and the discordance was more than 0.5 cm. Pathology examination was performed after resection, and estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2(HER?2) and Ki?67 result were recorded. All the lesions were classified into molecular subtype, including 14 Luminal A, 50 Luminal B, 11 HER?2?enriched and 10 triple?negative. Intraclass correlation coefficient (ICC) and Pearson correlation coefficient were used to analyze the reliability of CBBCT on tumor sizing. CBBCT?pathology discordance was analyzed based on the clinical, histopathology and CBBCT features by using t test, Chi?square and Fisher exact test. ROC curve was used to analyze the cut?off value between tumor size and CBBCT?pathology discordance. Results The agreement between CBBCT (2.155 ± 0.799) cm and pathology (1.986 ± 0.933) cm measurement was on moderate degree based on the ICC value (ICC=0.781, P<0.01) and had positive correlation (r=0.803, P<0.01). CBBCT?pathology concordance was found in 71 lesions, and discordance in 14 lesions. The factors of family history, symptom, pathology type, molecular subtypes, histological grade, surrounding fat invasion, lymphatic invasion, axillary lymph node metastasis, HER?2 positive and Ki?67 high expression had no significant difference between two groups. ER or PR positive had significant difference, suggesting that the accuracy of evaluation on ER or PR negative lesions could be reduced. The cut?off value of ROC curve between tumor size and CBBCT?pathology discordance was 2.08 cm, and the area under curve was 0.70. In 85 lesions. 66 of 75 mass lesions and 5 of 10 non?mass lesions were consistent. The lesion type had significant difference between two groups (χ2=6.705, P=0.010), which suggested the CBBCT evaluation on non?mass could have discrepancy with pathology. Conclusion CBBCT has high accuracy on tumor size evaluation on invasive carcinoma. ER or PR negative, large lesions or non?tumor lesions should be alert to the impact of CBBCT?pathology discordance before surgery which may cause the alteration of surgical protocols.
Résumé
Kidney tumor is one of the diseases threatening human health. Ultrasound is widely applied in kidney tumor diagnosis due to its high popularization, low price and no radiation. Accurate segmentation of kidney tumor is the basis of precise treatment. Kidney tumors often grow in the middle of cortex, so that segmentation is easy disturbed by nearby organs. Besides, ultrasound images own low contrast and large speckle, leading to difficult segmentation. This paper proposed a novel kidney tumor segmentation method in ultrasound images using adaptive sub-regional evolution level set models (ASLSM). Regions of interest are firstly divided into subareas. Secondly, object function is designed by integrating inside and outside energy and gradient, in which the ratio of these two parts are adjusted adaptively. Thirdly, ASLSM adapts convolution radius and curvature according to centroid principle and similarity inside and outside zero level set. Hausdorff distance (HD) of (8.75 ± 4.21) mm, mean absolute distance (MAD) of (3.26 ± 1.69) mm, dice-coefficient (DICE) of 0.93 ± 0.03 were obtained in the experiment. Compared with traditional ultrasound segmentation method, ASLSM is more accurate in kidney tumor segmentation. ASLSM may offer convenience for doctor to locate and diagnose kidney tumor in the future.
Sujets)
Humains , Algorithmes , Retard de croissance intra-utérin , Traitement d'image par ordinateur , Tumeurs du rein , Ostéochondrodysplasies , ÉchographieRésumé
Objective@#To compare the diagnostic efficiency of lesion in dense breast between cone beam breast computer tomography (CBBCT) and mammography.@*Methods@#From May 2012 to August 2014, 160 patients with 165 breasts who underwent mammography and CBBCT examinations were included in this study. The diagnostic results of CBBCT and mammography were reviewed and compared with pathological results.@*Results@#In the 165 breast, 24 were dense breasts and 141 were dense breasts. The diagnostic results were similar in 109 lesions, but different in 56 lesions. According to the analysis of the 165 breasts using receiver operation characteristics (ROC) curve, the area under the ROC curves (AUC) of CBBCT and mammography were 0.923 (95%CI: 0.878-0.967, P<0.05) and 0.959 (95%CI: 0.926-0.992, P<0.05), respectively. With Breast Imaging-Reporting and Data System (BI-RADS) 4b as the critical value, the sensitivity and specificity were 70.0% and 98.7% using mammography, and 83.3% and 97.3% using CBBCT, respectively. The AUC of CBBCT and mammography of the 141 dense breasts was 0.919(95%CI: 0.868-0.969, P<0.05) and 0.973(95%CI: 0.947-0.999, P<0.05), respectively. With BI-RADS 4b as the critical value, the sensitivity and specificity were 69.0% and 98.6% by mammography, and 83.1% and 98.6% by CBBCT, respectively.@*Conclusions@#CBBCT showed higher sensitivity and specificity in the diagnosis of breast malignant tumors compared to mammography. It is expected to be applied to breast cancer detection in the future, especially in dense breast.
Résumé
Radiomics enables extraction of innumerable quantitative features from medical images with high-throughput computing for diagnosis and prediction. The practice of radiomics involves image acquisition, identifying and segmenting the volumes of interest, extracting and analyzing of quantitative features, and classification or prediction model development. Compared with traditional visual interpretation of medical images, the deep mining of medical images by computer technology from radiomics makes feature uptake more efficient, relatively objective and rich in feature types. Whereas, radiomic analysis requires high image quality and consistent scan parameters. The features extracted are confined to the segmented area. Radiomics is promising in tumor screening, early diagnosis, accurate grading and staging, treatment and prognosis, molecular characteristics and so on. Combined with traditional visual interpretation of medical images, radiomics is helpful in tumor diagnosis and prediction.
Résumé
Objective:To investigate the accuracy of a threshold-based segmentation method based on cone beam breast CT(CBBCT) images in breast density measurement,and its value for breast-type classification and breast cancer screening.Methods:A retrospec-tive analysis of 195 patients who had undergone CBBCT examination at Tianjin Medical University Cancer Institute and Hospital be-tween May 2012 and August 2014 was performed.A total of 64 breasts were analyzed.On the basis of the classification criteria for breast density in BI-RADS,they were classified into four types and the majority report was reported.Breast density was measured by the threshold-based segmentation method based on CBBCT images and corrected manually to obtain the corrected breast density.A month later,the procedure was repeated.Intra-class correlation coefficients(ICCs)were used to compare the intra-observer and inter-observer consistencies of threshold-based segmentation and manually corrected breast density measurement results for non-dense and dense breasts.Results:For threshold-based segmentation measurements the intra-observer and inter-observer ICC values were 0.0.9624(95% CI:0.9388~0.9770)and 0.9666(95% CI:0.9500~0.9785).For manually corrected measurements,the intra-observer and inter-observer ICC values were 0.9750 (95% CI: 0.9592~0.9847) and 0.9775 (95% CI: 0.9661~0.9855). The ICC between the threshold-based segmentation method and manual correction was 0.9962 (95% CI: 0.9983~0.9977). The ICC values of threshold-based and manually corrected measurement in non-dense and dense breasts were 0.9497(95% CI:0.7072-0.9914)and 0.9983(95% CI:0.9971-0.9990),respectively.Conclusions:The threshold-based segmentation method based on CBBCT is a reliable and accurate com-puter-aided method of measuring breast density.It is expected to be applied in large-scale screening of breast cancer and to provide more information for predicting the risk of breast cancer.
Résumé
Objective:To discuss the value of CT quantitative parameters in prediction of the invasiveness of pulmonary mixed ground glass nodules(mGGNs).Methods:CT images of 164 patients with pulmonary ground-glass nodules(mGGNs),enrolled in Tianjin Medi-cal University Cancer Institute and Hospital from January 2013 to September 2016,confirmed by surgical pathology were analyzed ret-rospectively.CT quantitative parameters including maximum diameter,largest diameter perpendicular to the maximum diameter,vol-ume on both pulmonary window and mediastinal window,mean CT value and TDR(tumor shadow disappear rate)were evaluated be-tween minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) with ROC and Logistic regression analysis. Re-sults:Logistic regression analysis showed that the maximum diameter of the pulmonary window(OR=3.080,95% CI:1.135-8.355,P=0.027)and the maximum diameter of the mediastinal window(OR=5.881,95%CI:1.634-21.166,P=0.007)independently predicted the invasiveness of mGGNs;the corresponding values of area under the curve of the combination of two parameters were 0.855,with sen-sitivity of 77.61% and specificity of 86.67 %.The cut-off point to the corresponding value of the maximum diameter on pulmonary win-dow and mediastinal window was 1.902cm and 1.273cm,respectively.Conclusions:CT quantitative parameters are helpful in predict-ing the invasiveness of mGGNs.The maximum diameters on both pulmonary window and mediastinal window are independent predic-tors.