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Introduction: Over the past few years, complete blood count, RBC histogram and peripheral blood smear have become the important diagnostic tools to diagnose various haematological conditions. Major public health burden worldwide is anemia with high prevalence in developing countries like India1. Red blood cell and histogram are indispensable for diagnosis and management of anemia2. The major diagnostic tool for work up of most commercial laboratories has been analysing the blood film routinely. The aim AIM: of the study is to compare between automated cell counter histogram and peripheral smear finding in diagnosis of anemia. Material and method: A prospective comparative study of RBC histogram and peripheral blood smear in diagnosis of anemia was done on 100 patients of HB<14gm%, over six month time span (June2022-Nov2022) in the central laboratory of Saraswathi Institute of Medical Sciences Hapur, (UP) India. This study included all the age groups. All cases of anemia that have undergone blood transfusion is excluded from this study. In Result: our study it was observed that on peripheral blood smear, the most common type of anemia was microcytic hypochromic anemia followed by normocytic normochromic anemia, when we compared with automated cell counter generated histogram most common type of anemia was normocytic normochromic followed by microcytic hypochromic anemia. In our study female population were more than males. The mean age was(32.4yr). Automated cell counters generated CBC histograms and peripheral blood smears Conclusion: plays a major role in diagnosis and management of red cell disorder. Our study observed that histogram patterns and their confirmation by peripheral blood smear along with clinical history gives accurate and confirmed diagnosis of various haematogical conditions. There is much improvement in accuracy and precision 3 reducing the subjective errors .
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ObjectiveTo investigate the feasibility of multiparametric MRI （mpMRI） combined with histogram analysis of apparent diffusion coefficient （ADC） in the assessment of patients with variant histology （VH） of urothelial carcinoma （UC）. MethodWe retrospectively analyzed the data of patients pathologically diagnosed with UC who underwent mpMRI in the First Affiliated Hospital of Sun Yat-sen University between March 2015 and March 2023. The patients were divided into VH group （urothelial carcinoma mixed with other histologies） and non-VH group （pure urothelial carcinoma） according to pathological results. We performed propensity score 1：1 nearest-neighbor matching on the two groups based on age and gender and 49 patients were included in each group. The regions of interest （ROIs） of the whole tumor were delineated manually by using ITK-SNAP software and Pyradiomics was applied to extract ADC histogram parameters. We compared the clinicopathological data， MRI morphological features and ADC histogram parameters between the groups. Multivariate logistic regression was used to identify independent risk factors and construct the prediction model. Receiver operating characteristic （ROC） curve analyses were performed to evaluate the diagnostic performance of these parameters for determining VH of UC. ResultsMRI morphological features including the lesion shape， vesical imaging-reporting and data system （Ⅵ-RADS）score， enhancement pattern and suspicious lymph node metastasis were markedly different between the two groups （all P < 0.05）. ADC mean， ADC median， ADC25th， ADC75th， ADC10th and ADC90th were significantly lower in patients with VH than those in non-VH group （all P<0.05）. Multivariate logistic regression analysis showed enhancement pattern， ADC25th， ADC75th and ADC mean were independent predictors （P < 0.05）. The combined model yielded the best predictive performance， with an area under the ROC curve （AUC） of 0.91 （95% CI： 0.83-0.96）. ConclusionsMpMRI combined with whole-tumor histogram analysis of ADC can serve as a reliable method for evaluating the presence of VH in UC， further to assist the clinical decision making.
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OBJECTIVE@#To propose an deep learning-based algorithm for automatic prediction of dose distribution in radiotherapy planning for head and neck cancer.@*METHODS@#We propose a novel beam dose decomposition learning (BDDL) method designed on a cascade network. The delivery matter of beam through the planning target volume (PTV) was fitted with the pre-defined beam angles, which served as an input to the convolution neural network (CNN). The output of the network was decomposed into multiple sub-fractions of dose distribution along the beam directions to carry out a complex task by performing multiple simpler sub-tasks, thus allowing the model more focused on extracting the local features. The subfractions of dose distribution map were merged into a distribution map using the proposed multi-voting mechanism. We also introduced dose distribution features of the regions-of-interest (ROIs) and boundary map as the loss function during the training phase to serve as constraining factors of the network when extracting features of the ROIs and areas of dose boundary. Public datasets of radiotherapy planning for head and neck cancer were used for obtaining the accuracy of dose distribution of the BDDL method and for implementing the ablation study of the proposed method.@*RESULTS@#The BDDL method achieved a Dose score of 2.166 and a DVH score of 1.178 (P < 0.05), demonstrating its superior prediction accuracy to that of current state-ofthe-art (SOTA) methods. Compared with the C3D method, which was in the first place in OpenKBP-2020 Challenge, the BDDL method improved the Dose score and DVH score by 26.3% and 30%, respectively. The results of the ablation study also demonstrated the effectiveness of each key component of the BDDL method.@*CONCLUSION@#The BDDL method utilizes the prior knowledge of the delivery matter of beam and dose distribution in the ROIs to establish a dose prediction model. Compared with the existing methods, the proposed method is interpretable and reliable and can be potentially applied in clinical radiotherapy.
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Humans , Deep Learning , Head and Neck Neoplasms/radiotherapy , Algorithms , Neural Networks, ComputerABSTRACT
Objective:To compare the predictive value of parameters extracted from circular region-of-interest (ROI) with whole-liver histogram on gadoxetic acid disodium (Gd-EOB-DTPA)-enhanced T 1 map for postoperative liver failure in patients with liver focal lesions. Methods:The data of patients who underwent Gd-EOB-DTPA-enhanced MRI for focal liver lesions in Zhongshan Hospital, Fudan University from March 2016 to March 2018 were analyzed retrospectively. Forty patients were enrolled, including 30 males and 10 females, aged (56.6±12.1) years. According to the occurrence of postoperative liver failure, forty patients were divided into liver failure group ( n=14) and control group ( n=26). The parameters extracted from circular ROIs and whole liver histogram on T 1 map before Gd-EOB-DTPA enhancement and in hepatobiliary phase (HBP) were compared between the two groups. The receiver operating characteristic (ROC) curve was used to evaluate the value of these parameters in predicting postoperative liver failure. Results:The mean, standard deviation, median and 95% quantile of T 1 HBP in histogram parameters of liver failure group were significantly higher than those of control group (all P<0.05). The three parameters extracted from circular ROIs were not effective in predicting liver failure after hepatectomy (all P>0.05). Among all the liver histogram parameters, the area under the ROC curve of the 95% quantile before T 1 enhancement for predicting postoperative liver failure was 0.702 (95% CI: 0.523-0.881), the standard deviation of T 1 HBP was 0.739 (95% CI: 0.568-0.910), and the 95% quantile of T 1 HBP was 0.721 (95% CI: 0.540-0.903). The predictive efficacy were good (all P<0.05). Among them, the predictive performance of T 1 HBP standard deviation was the best, the area under the ROC curve was 0.739, the sensitivity was 85.7%, the specificity was 57.7%, and the best threshold was 54.8 ms. Conclusions:When Gd-EOB-DTPA enhanced T 1 mapping is used to predict postoperative liver failure in patients with focal liver lesions, the whole-liver histogram analysis is superior to the conventional circular ROI-based statistical method.
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Objective:To analyze the prognostic value of nomogram model for cervical cancer based on the imaging features of diffusion kurtosis imaging (DKI) histogram.Methods:The DKI and clinical data of 272 patients with cervical cancer who were admitted to Affiliated Hospital of Guangdong Medical University from March 2015 to February 2022 were collected and retrospectively analyzed. All patients were randomly divided into the training group ( n=190) and validation group ( n=82) at a ratio of 7 vs. 3. The parameters of DKI histogram were obtained by GE AW 4.2 MRI software. The best prognostic imaging features were screened by LASSO regression. The DKI radiomics score was calculated by linear combination. The independent risk factors of prognosis were identified by univariate and multivariate regression analyses, and a nomogram model was constructed. The model discrimination was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). The internal consistency of the model was evaluated by the calibration map. Results:Adenocarcinoma ( HR=2.496, 95% CI=1.312-4.749, P=0.005), DKI score ( HR=24.087, 95% CI=6.062-95.711, P<0.001), depth of invasion ≥ 1/2 muscular layer ( HR=2.277, 95% CI=1.156-4.487, P=0.017) and neutrophil to lymphocyte ratio (NLR) ( HR=1.800, 95% CI=1.313-2.468, P<0.001) were the independent risk factors for prognosis of cervical cancer. The AUC of the nomogram model in the training and validation groups were 0.860 and 0.757, respectively. The calibration curve was well fitted with the 45° diagonal. The prediction results of long-term prognosis of this model were in good agreement with the actual situation. Conclusions:Adenocarcinoma, NLR, DKI score and depth of invasion ≥ 1/2 muscular layer are the independent risk factors for the prognosis of patients with cervical cancer. The constructed nomogram model could reliably predict the 3-year survival rate of patients with cervical cancer.
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Objective:To evaluate the practicability of dose volume histogram (DVH) prediction model for organ at risk (OAR) of radiotherapy plan by minimizing the cost function based on equivalent uniform dose (EUD).Methods:A total of 66 nasopharyngeal carcinoma (NPC) patients received volume rotational intensity modulated arc therapy (VMAT) at Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences from 2020 to 2021 were retrospectively selected for this study. Among them, 50 patients were used to train the recurrent neutral network (RNN) model and the remaining 16 cases were used to test the model. DVH prediction model was constructed based on RNN. A three-dimensional equal-weighted 9-field conformal plan was designed for each patient. For each OAR, the DVHs of individual fields were acquired as the model input, and the DVH of VMAT plan was regarded as the expected output. The prediction error obtained by minimizing EUD-based cost function was employed to train the model. The prediction accuracy was characterized by the mean and standard deviation between predicted and true values. The plan was re-optimized for the test cases based on the DVH prediction results, and the consistency and variability of the EUD and DVH parameters of interest (e.g., maximum dose for serial organs such as the spinal cord) were compared between the re-optimized plan and the original plan of OAR by the Wilcoxon paired test and box line plots.Results:The neural network obtained by training the cost function based on EUD was able to obtain better DVH prediction results. The new plan guided by the predicted DVH was in good agreement with the original plan: in most cases, the D 98% in the planning target volume (PTV) was greater than 95% of the prescribed dose for both plans, and there was no significant difference in the maximum dose and EUD in the brainstem, spinal cord and lens (all P>0.05). Compared with the original plan, the average reduction of optic chiasm, optic nerves and eyes in the new plans reached more than 1.56 Gy for the maximum doses and more than 1.22 Gy for EUD, and the average increment of temporal lobes reached 0.60 Gy for the maximum dose and 0.30 Gy for EUD. Conclusion:The EUD-based loss function improves the accuracy of DVH prediction, ensuring appropriate dose targets for treatment plan optimization and better consistency in the plan quality.
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Objective The navigation system of robot-assisted knee arthroplasty uses a laser scanner to acquire intraoperative cartilage point clouds and align them with the preoperative model for automatic non-contact space registration. The intraoperative patient knee lesion point cloud contains a large number of irrelevant background point clouds of muscles, tendons, ligaments and surgical instruments. Manual removal of irrelevant point clouds takes up surgery time due to human-computer interaction, so in this study we proposed a novel method for automatic extraction of point clouds from the knee cartilage surface for fast and accurate intraoperative registration. Methods Due to the lack of adequate description of cartilage surface and geometric local information, PointNet cannot extract cartilage point clouds with high precision. In this paper, a fast point feature histogram(FPFH)-PointNet method combined with fast point feature histogram was proposed, which effectively described the appearance of cartilage point cloud and achieved the automatic and efficient segmentation of cartilage point cloud. Results The point clouds of distal femoral cartilage of 10 cadaveric knee specimens and 1 human leg model were scanned from different directions as data sets. The accuracy of cartilage point cloud segmentation by PointNet and FPFH-PointNet were 0.94 ±0.003 and 0.98 ±0, and mean intersection over union(mIOU) were 0.83 ±0.015 and 0.93 ±0.005, respectively. Compared with PointNet, FPFH-PointNet improved accuracy and mIOU by 4% and 10% respectively, while the elapsed time was only about 1.37 s. Conclusion FPFH-PointNet can accurately and automatically extract the knee cartilage point cloud, which meets the performance requirement for intraoperative navigation.
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Objective: To investigate the feasibility and value of histogram analysis based on two-dimensional gray-scale ultrasonography in the differential diagnosis of medullary thyroid carcinoma (MTC) and thyroid adenoma (TA). Methods: The preoperative ultrasound images of 86 newly diagnosed MTC patients and 100 TA patients treated in the Cancer Hospital of Chinese Academy of Medical Sciences from January 2015 to October 2021 were collected. Histograms were performed based on the regions of interest (ROIs) delineated manually by two radiologists, thereafter, mean, variance, skewness, kurtosis, percentiles (1st, 10th, 50th, 90th, 99th) were generated. The histogram parameters between the MTC group and the TA group were compared, and the independent predictors were screened by multivariate logistic regression analysis. Receiver operating characteristic (ROC) analysis was used to compare the individual diagnostic efficacy and joint diagnostic efficacy of independent predictors. Results: Multivariate regression analysis showed that mean, skewness, kurtosis and 50th percentile were independent factors. The skewness and kurtosis in the MTC group were significantly higher than those in the TA group, and the mean and 50th percentile were significantly lower than those in the TA group. The area under the individual ROC curve of mean, skewness, kurtosis and 50th percentile is 0.654-0.778. The area under the combined ROC curve is 0.826. Conclusion: Histogram analysis based on two-dimensional gray-scale ultrasonography is a promising tool to distinguish MTC from TA, in which the joint diagnosis value of mean, skewness, kurtosis and 50th percentile is the highest.
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Humans , ROC Curve , Diagnosis, Differential , Retrospective Studies , Thyroid Neoplasms/diagnostic imaging , Ultrasonography , Diffusion Magnetic Resonance Imaging/methodsABSTRACT
Objective:To investigate the correlation between three-dimensional histogram analysis of dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and Gleason score(GS)in prostate cancer(Pca)from two hospital, and its diagnostic efficacy for discriminating low-grade from high-grade Pca.Methods:A total of 102 pathologically confirmed Pca patients in the First Affiliated Hospital of Zhejiang Chinese Medical University and Hangzhou Traditional Chinese Medical Hospital(TCM Hospital)Affiliated to Zhejiang Chinese Medical University from January 2017 to October 2020 were retrospectively analyzed.The quantitative parameters of Pca, including transport constant(K trans), rate constant(K ep), percent volume of the extravascular extracellular space(V e)and fraction of the Intraplasmic contrast volume(V p), were obtained by manually layer by layer delineating of interested regions of all lesions on the original DCE-MRI imaging.Then the three-dimensional histogram analysis of the above parameters were performed to obtain the minimum, maximum, median, mean, area, 10 thpercentile, 25 thpercentile, 75 thpercentile and 90 thpercentile.The correlations between quantitative parameters and GS, and diagnostic efficiencies were analyzed. Results:102 Pca patients were divided into low-grade prostate cancer group(GS≤3+ 4)(n=44)and high-grade Pca group(GS≥4+ 3)(n=58). There were no statistically significant differences in age and location of lesions between the two groups( P>0.05), but there were statistically significant differences in Gleason score, PSA level and lesion diameter between the two groups( U=0.000, 730.000, 711.000, all P<0.05). The median, mean, 10 thpercentile, 25 thpercentile, 75 thpercentile, 90 thpercentile derived from K trans, and K ep(median, mean, 10%, 25%, 75%, 90%)together with maximum of K transand mean for V e were positively correlated with GS( r=0.405 to 0.583, P<0.05), in which mean of K transhad the highest positive correlation( r=0.583, P=0.000). The histogram parameters derived from V pwere negatively correlated with GS( r=-0.301 to 0.341, P<0.05). The area under ROC of 75th percentile derived from K transwas the highest(0.832). When the cut-off value of 75 thpercentile derived from K transwas ≥0.680/min, its Youden index, sensitivity, and specificity were 0.594, 0.776, 0.818, respectively. Conclusions:The three-dimensional histogram of DCE-MRI quantitative parameters has correlation with GS in Pca patients, can be used to discriminate low-grade from high-grade Pca.
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Objective:To evaluate the volumetric modulated arc therapy (VMAT) dose verification of cervical cancer based on γ rule and dose volume histogram (DVH) and to perform correlation analysis between the evaluation results and the dose differences.Methods:Twenty cervical cancer VMAT plans were selected and performed on TrueBeam Linac. The delivered point and surface dose was measured by FC-65 G and ArcCheck and the results were compared to those calculated by Eclipse. The dose of patients was reconstructed by 3DVH. Then, differences between the reconstructed and plan value of D mean, D 95%, D 98% and D 2% of PTV, V 20Gy of left and right femoral head, V 40Gy of rectum, D 1cm 3 of cord, D 98%, D 2% and D 50% of the 50% prescription iso-dose volume (IDV), were evaluated and 3-dimensional (3D) γ was assessed for each organ. Lastly, Pearson’s correlation coefficient was used to analyze the relationship between point dose difference, 2D γ pass-rate (γ%), γ mean and 3D γ% of each organ and the dose difference. Results:Small differences were found between the point dose measured, reconstructed and the plan value. Differences between D mean of PTV, all dose parameters of IDV and plan values were all within 3% and V 40Gy of rectum showed the largest difference. As for the 3D γ%, the maximum pass rate was found for the left and right femoral head and the maximum variance for cord D 1cm 3. There was a moderate correlation between measured and reconstructed point dose deviation and dose difference of each organ, while no significant correlation was found for 2D γ%. Strong correlation was found between 3D γ% of target and D 50% of PTV/IDV and no correlation was found for other organs. Conclusion:The performance of both γ-and DVH-based evaluation can reveal dose error for dose verification, but both of them have some limitations and should be combined in clinical practice.
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BACKGROUND@#The incidence of symptomatic radiation pneumonitis (RP) and its relationship with dose-volume histogram (DVH) parameters in non-small cell lung cancer (NSCLC) patients receiving epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) and concurrent once-daily thoracic radiotherapy (TRT) remain unclear. We aim to analyze the values of clinical factors and dose-volume histogram (DVH) parameters to predict the risk for symptomatic RP in these patients.@*METHODS@#Between 2011 and 2019, we retrospectively analyzed and identified 85 patients who had received EGFR-TKIs and once-daily TRT simultaneously (EGFR-TKIs group) and 129 patients who had received concurrent chemoradiotherapy (CCRT group). The symptomatic RP was recorded according to the Common Terminology Criteria for Adverse Event (CTCAE) criteria (grade 2 or above). Statistical analyses were performed using SPSS 26.0.@*RESULTS@#In total, the incidences of symptomatic (grade≥2) and severe RP (grade≥3) were 43.5% (37/85) and 16.5% (14/85) in EGFR-TKIs group vs 27.1% (35/129) and 10.1% (13/129) in CCRT group respectively. After 1:1 ratio between EGFR-TKIs group and CCRT group was matched by propensity score matching, chi-square test suggested that the incidence of symptomatic RP in the MATCHED EGFR-TKIs group was higher than that in the matched CCRT group (χ2=4.469, P=0.035). In EGFR-TKIs group, univariate and multivariate analyses indicated that the percentage of ipsilateral lung volume receiving ≥30 Gy (ilV30) [odds ratio (OR): 1.163, 95%CI: 1.036-1.306, P=0.011] and the percentage of total lung volume receiving ≥20 Gy (tlV20) (OR: 1.171, 95%CI: 1.031-1.330, P=0.015), with chronic obstructive pulmonary disease (COPD) or not (OR: 0.158, 95%CI: 0.041-0.600, P=0.007), were independent predictors of symptomatic RP. Compared to patients with lower ilV30/tlV20 values (ilV30 and tlV20<cut-off point values) and without COPD, patients with higher ilV30/tlV20 values (ilV30 and tlV20>cut-off point values) and COPD had a significantly higher risk for developing symptomatic RP, with a hazard ratio (HR) of 1.350 (95%CI: 1.190-1.531, P<0.001).@*CONCLUSIONS@#Patients receiving both EGFR-TKIs and once-daily TRT were more likely to develop symptomatic RP than patients receiving concurrent chemoradiotherapy. The ilV30, tlV20, and comorbidity of COPD may predict the risk of symptomatic RP among NSCLC patients receiving EGFR-TKIs and conventionally fractionated TRT concurrently.
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Humans , Carcinoma, Non-Small-Cell Lung/radiotherapy , ErbB Receptors/genetics , Lung Neoplasms/radiotherapy , Protein Kinase Inhibitors/adverse effects , Pulmonary Disease, Chronic Obstructive/complications , Radiation Pneumonitis/etiology , Radiotherapy Dosage , Retrospective StudiesABSTRACT
ABSTRACT The aim of this paper is to show a technique to speed up the interpretation of bone scans in order to determine the presence of early bone metastasis. This is done using the gray levels histogram of the region of interest. The technique is intended to assist in the bone scans interpretation in order to provide a successful diagnosis. During the analysis, three types of histograms were observed on the regions of interest. If the histogram is narrow and shifted toward the origin, the bone scan is free of metastasis. If it is shifted to the right and slightly broadened, indicates the presence of a bone anomaly different from a metastasis. On the other hand, if the histogram is more broadened and shifted to the right, is suggests the presence of metastasis. This histogram is characterized by displaying small curls on the right side providing information about the metastatic disease stage, which could be low-amplitude peaks and have a short length, if the metastasis is in early stage, or high-amplitude peaks and a long length, if is advanced. Finally, the analyzed region is displayed in false color considering the minimum gray levels observed in the histogram.
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Objective:To establish a local effect model (LEM)-based rectal dose volume histogram (DVH) prediction model in prostate cancer patients treated with carbon ion therapy based on Japanese experience, aiming to provide reference for clinically reducing the incidence of rectal adverse reactions.Methods:The planning CT data of 76 patients with prostate cancer were collected. The microdosimetric kinetic model (MKM) was used for initial planning, and the LEM was selected to recalculate the biological dose based on the same fields to MKM. Then, the geometric features and DVH of the rectum were extracted from the LEM plans. The planning data of 61 cases were used to establish the prediction model with linear regression and the other 15 cases were used for validation.Results:The ratio of the overlapped volume between the rectum and the region of interest (ROI) expended from planning target volume by 1 cm along the left and right directions of the rectum could be proved to be the characteristic parameters for linear regression. The mean goodness-of-fit R2 of predicted and LEM plan-based DVH of 15 cases was 0.964. The results of predicted rectal adverse reactions based on predicted DVH were consistent with those of LEM plan-based DVH. Conclusions:The linear regression method used in this study can establish an accurate prediction model of rectal DVH, which may provide certain reference for reducing the incidence of rectal adverse reactions. Nevertheless, the findings remain to be further verified by clinical trials with larger sample size.
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Objective:To establish an automatic planning method using volumetric-modulated arc therapy (VMAT) for primary liver cancer (PLC) radiotherapy based on predicting the feasibility dose-volume histogram (DVH) and evaluate its performance.Methods:Ten patients with PLC were randomly chosen in this retrospective study. Pinnacle Auto-Planning was used to design the VMAT automatic plan, and the feasibility DVH curve was obtained through the PlanIQ dose prediction, and the initial optimization objectives of the automatic plan were set according to the displayed feasible objectives interval. The plans were accessed according to dosimetric parameters of the planning target volume and organs at risk as well as the monitor units. All patients′ automatic plans were compared with clinically accepted manual plans by using the paired t-test. Results:There was no significant difference of the planning target volume D 2%, D 98%, D mean or homogeneity index between the automatic and manual plans ((58.55±2.81) Gy vs.(57.98±4.17) Gy, (47.15±1.58) Gy vs.(47.82±1.38) Gy, (53.14±0.95) Gy vs.(53.44±1.67) Gy and 1.15±0.05 vs. 1.14±0.07, all P>0.05). The planning target volume conformity index of the manual plan was slightly higher than that of the automatic plan (0.77±0.08 vs. 0.69±0.06, P<0.05). The mean doses of normal liver, V 30Gy, V 20Gy, V 10Gy, V 5Gy and V< 5Gy of the automatic plan were significantly better than those of the manual plan ((26.68±11.13)% vs.(28.00±10.95)%, (29.96±11.50)% vs.(31.89±11.51)%, (34.88±11.51)% vs.(38.66±11.67)%, (45.38±12.40)% vs.(50.74±13.56)%, and (628.52±191.80) cm 3vs.(563.15±188.39) cm 3, all P<0.05). The mean doses of the small intestine, the duodenum, and the heart, as well as lung V 10 of the automatic plan were significantly less than those of the manual plan ((1.83±2.17) Gy vs.(2.37±2.81) Gy, (9.15±9.36) Gy vs.(11.18±10.49) Gy, and (5.44±3.10) Gy vs.(6.25±3.26) Gy, as well as (12.70±7.08)% vs.(14.47±8.11)%, all P<0.05). Monitor units did not significantly differ between two plans ((710.67±163.72) MU vs.(707.53±155.89) MU, P>0.05). Conclusions:The automatic planning method using VMAT for PLC radiotherapy based on predicting the feasibility DVH enhances the quality for PLC plans, especially in terms of normal liver sparing. Besides, it also has advantages for the protection of the intestine, whole lung and heart.
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Objective:To evaluate the application value of the histogram features of quantitative parameters from synthetic MRI in predicting the expression of human epidermal growth factor receptor 2 (HER2) in breast invasive ductal carcinoma (IDC) and to compare the prediction efficiency with that of ADC histogram parameters.Methods:A total of 195 patients with breast lesions were prospectively enrolled in the Fudan University Cancer Hospital, from January 2020 to September 2020. All patients underwent preoperative synthetic MRI, DWI and dynamic contrast-enhanced MRI (DCE-MRI). All surgical specimens were confirmed by pathology. The histogram features of the quantitative parameters [T 1, T 2, and proton density (PD)] and ADC values were extracted by PyRadiomics software. Student t test or Mann-Whitney U test were used to compare the histogram characteristics of quantitative parameters (T 1, T 2, and PD) and ADC values between HER2-positive and HER2-negative breast cancers. The diagnostic efficacy of the variables in predicting HER2 expression state was evaluated using the area under curve (AUC) value of ROC. Results:A total of 122 patients with breast IDC were included into analysis, with 31 of HER2-positive and 91 of HER2-negative. There was no significant difference in the clinicopathological characteristics between HER2-positive and HER2-negative breast IDC patients. Univariate analysis showed that there was statistically significant difference in PD-median [79.80 (75.90, 83.90)ms vs. 76.56 (72.59, 79.09) ms, Z=-3.46, P<0.01], PD-mean [78.89 (74.80, 84.01) ms vs. 75.99 (71.70, 78.63) ms, Z=-2.61, P=0.01], PD-Kurtosis [6.45(3.45, 7.54) vs. 5.04 (3.55, 5.58), Z=-2.21, P=0.03], T 1-10 th percentile [731.52 (668.50, 975.39) ms vs. 726.51 (588.38, 852.19) ms, Z=-2.54, P=0.01], T 1-mean [1 161.97 (1 063.56, 1 253.78) ms vs. 1 072.75 (989.39, 1 154.04)ms, Z=-2.21, P=0.03] and ADC-Kurtosis [4.75 (2.72, 5.91) vs. 3.82 (2.69, 4.39), Z=-2.43, P=0.02] between HER2 positive and negative breast IDC patients. Multivariate analysis showed that PD-median ( P=0.004) and T 1-mean ( P=0.004) were independent risk factors for HER2 expression. The ROC curve of HER2 expression predicted by this model showed an AUC was 0.853(95%CI 0.779-0.926), with a sensitivity of 71% and a specificity of 81%. The ROC curve of ADC-Kurtosis for predicting the expression of HER2 showed that the AUC was 0.714 (95%CI 0.611-0.817), with the sensitivity of 45%, and the specificity of 85%. DeLong test showed that the diagnostic efficacy of quantitative parameters from synthetic MRI in predicting the status of HER2 was higher than that of ADC histogram parameters ( Z=2.18, P=0.04). Conclusion:Histogram features of synthetic MRI quantitative parameters contribute to the prediction of HER2 expression status in IDC and may therefore contribute to the determination of individualized anti-HER2 targeted therapy strategies.
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Objective To develop and evaluate an automatic intensity-modulated radiation therapy (IMRT) program for cervical cancer based on a database of overlap volume histogram (OVH) and high-quality cervical IMRT plans for previously-treated patients.Methods A database consisting of high-quality IMRT plans and OVHs from 200 cervical cancer patients was established.OVHs of another 26 cervical cancer patients were converted into gray level images to calculate the image similarity compared with those from the database.The planning optimization function of the patients from the database with the highest image similarity was selected and inherent Pinnacle3 scripts were utilized to automatically generate IMRT plan.Finally,the dosimetric parameters,plan quality and design time were statistically compared between the automatic and manual plans.Results The target coverage,conformity index and homogeneity index did not significantly differ between two plans (all P＞0.05).The V40,V45 and mean dose for the rectum in the automatic plans were significantly decreased by 6.1％,1.3％ and 50.7 cGy than those in the manual plans (all P＜0.05).Compared with the manual plans,the mean dose for the intestine and femur in the automatic plans were significantly reduced by 31.7 cGy and 188.9 cGy (both P＜0.05),whereas the mean dose for the ilium was slightly decreased by 92.3 cGy in the automatic plans (P＞ 0.05).The plan design time was shortened by 71％ in the automatic plans.Conclusions The automatic IMRT plans based on a database of OVH and high-quality IMRT plans can not only significantly shorten the plan design time,but also reduce the irradiated dose of normal tissues without compromising the target coverage and conformity index.
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Objective To study a lung dose prediction method for the early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy based on machine learning algorithm,and to evaluate the feasibility of application in planning quality assurance.Methods A machine learning algorithm was utilized to achieve DVH prediction.First,an expert plan dataset with 125 cases was built,and the geometric features of ROI,beam angle and dose-volume histogram(DVH) parameters in the dataset were extracted.Following a correlation model was established between the features and DVHs.Second,the geometric and beam features from 10 cases outside the training pool were extracted,and the model was adopted to predict the achievable DVHs values of the lung.The predicted DVHs values were compared with the actual planned results.Results The mean squared errors of external validation for the 10 cases in mean lung dose (MLD) MLD and V20 of the lung were 91.95 cGy and 3.12％,respectively.Two cases whose lung doses were higher than the predicted values were re-planned,and the results showed that the the lung doses were reduced.Conclusion It is feasible to utilize the anatomy and beam angle features to predict the lung DVH parameters for plan evaluation and quality assurance in early stage NSCLC patients treated with stereotactic body radiotherapy
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Objective To evaluate the value of MRI-T2WI texture analysis in the differentiation of atypical medulloblastoma and ependymoma of the fourth ventricle.Methods Preoperative MRI data of 36 cases of fourth ventricle tumor (19 cases of medulloblastoma and 17 cases of ependymoma) confirmed by the Central Theater General Hospital of the Chinese People's Liberation Army were retrospectively analyzed.Manually sketch areas of interest (ROI) were made using texture analysis software to get histogram parameters,including mean,median,standard deviation,skewness,kurtosis,maximum,minimum,heterogeneity,entropy,the 5th percentile (T2WI5th),the 10th percentile (T2WI10th),the 25th percentile (T2WI25th),the 50th percentile (T2WI50th),the 75th percentile(T2WI75th),the 95th percentile (T2WI95th).Independent sample t test or mann-whitney U test was used for statistical analysis of each parameter value between the two groups.Logistic regression analysis was used to perform multiparameter joint analysis on meaningful parameters.The area under the curve (AUC) was obtained by using the subject operating characteristic (ROC) curve to determine the threshold and diagnostic efficacy for distinguishing the two group.Results The mean,median,T2WI5th,T2W10th,T2WI25th,T2WI50th,T2WI75th values of the medulloblastoma group were smaller than those of the ependymoma group,and difference was statistically significant (P ＜ 0.05).The T2WI5th had the best diagnostic performance.When the critical value was 1 343,the diagnostic specificity was 75.0％ and the sensitivity was 92.9％.Logistic regression was used to predict the joint analysis of probability parameters.T2WI15th+T2WI10th had the best performance,and AUC was 0.928.When the critical value was 0.75,the diagnostic specificity was 75.0％ and the sensitivity was 100.0％.Conclusions MRI-T2WI texture analysis can provide quantitative information for preoperative diagnosis of atypical medulloblastoma and ependymoma and improve the accuracy of preoperative diagnosis,which has obvious clinical value.
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Objective: To explore the value of histogram features based on plain and enhanced CT for differential diagnosis of benign and malignant thyroid nodules. Methods: A total of 132 patients with 140 thyroid nodules confirmed by postoperative pathology were retrospectively analyzed. The level with the largest axial focus was selected, and then Mazda software was used to sketch ROI along the edge of focus and perform histogram analysis to obtain 9 parameters. The parameters were compared between benign and malignant nodules, and then statistically significant gray-scale histogram parameters were used to analyze their value of differentiating benign and malignant thyroid nodules. Results: On plain CT, the mean value, the 10th, 50th and 90th percentiles of malignant nodules were higher than those of benign ones (all P0.05). Among the parameters of CT plain and enhanced histogram, the 10th percentile AUC was the highest of 0.68, and the sensitivity of identifying benign and malignant thyroid nodules was 74.32%,and specificity was 62.12%. Conclusion: CT histogram analysis can be used as an important auxiliary means to identify benign and malignant thyroid nodules.
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Objective: To observe the value of whole tumor volume histogram analysis based enhanced CT in differential diagnosis of ≤5 cm gastrointestinal stromal tumor (GIST) and gastric schwannoma (GS). Methods: Totally 51 patients with pathologically proven ≤5 cm GIST and 16 with GS underwent abdominal enhanced CT, and the images were retrospectively analyzed. ROIs were chosen at dual-phase CT images of total tumor, and histogram analysis was performed using MaZda software to obtain the mean, variation, skewness, kurtosis and percentiles (Perc.1%, Perc.10%, Perc.50%, Perc.90%, Perc.99%). The differences of the above parameters were compared between 2 groups, and their diagnostic efficacies were analyzed. Results: Among histogram parameters of arterial phase images, the mean, variation and percentiles (Perc.1%, Perc.10%, Perc.50%, Perc.90%) values of GIST group were 125.71±13.57, 145.31 (110.44,191.84), 97.63±16.40, 110.45±14.69, 125.67±13.46 and 141.06±13.48, respectively, while of GS group were 139.97±19.08, 99.21 (80.97, 135.21), 113.38±19.86, 127.44±18.90, 140.25±19.20 and 152.75±20.00, respectively. There were statistical differences between 2 groups (all P<0.05), and the AUC of ROC values were 0.76, 0.72, 0.75, 0.78, 0.76 and 0.73,respectively. Among histogram parameters of venous phase images, the variation value of GIST group was 144.08 (106.58, 221.25), and of GS was 94.38 (80.97,122.90, P<0.05), and the AUC was 0.78. Conclusion: Histogram analysis based enhanced CT is valuable for differentiating ≤5 cm GIST and GS, while Perc.10% (on arterial phase) and variation (on venous phase) have relative high diagnostic efficacy.