A fast deformable registration method for 4D lung CT in hybrid framework

Longitudinal registration of thoracic CT images with radiation-induced lung diseases: A divide-and-conquer approach based on component structure wise registration using coherent point drift

BACKGROUND AND OBJECTIVE

Registration of pulmonary computed tomography (CT) images with radiation-induced lung diseases (RILD) was essential to investigate the voxel-wise relationship between the formation of RILD and the radiation dose received by different tissues. Although various approaches had been developed for the registration of lung CTs, their performances remained clinically unsatisfactory for registration of lung CT images with RILD. The main difficulties arose from the longitudinal change in lung parenchyma, including RILD and volumetric change of lung cancers, after radiation therapy, leading to inaccurate registration and artifacts caused by erroneous matching of the RILD tissues.

METHODS

To overcome the influence of the parenchymal changes, a divide-and-conquer approach rooted in the coherent point drift (CPD) paradigm was proposed. The proposed method was based on two kernel ideas. One was the idea of component structure wise registration. Specifically, the proposed method relaxed the intrinsic assumption of equal isotropic covariances in CPD by decomposing a lung and its surrounding tissues into component structures and independently registering the component structures pairwise by CPD. The other was the idea of defining a vascular subtree centered at a matched branch point as a component structure. This idea could not only provide a sufficient number of matched feature points within a parenchyma, but avoid being corrupted by the false feature points resided in the RILD tissues due to globally and indiscriminately sampling using mathematical operators. The overall deformation model was built by using the Thin Plate Spline based on all matched points.

RESULTS

This study recruited 30 pairs of lung CT images with RILD, 15 of which were used for internal validation (leave-one-out cross-validation) and the other 15 for external validation. The experimental results showed that the proposed algorithm achieved a mean and a mean of maximum 1 % of average surface distances <2 and 8 mm, respectively, and a mean and a maximum target registration error <2 mm and 5 mm on both internal and external validation datasets. The paired two-sample t-tests corroborated that the proposed algorithm outperformed a recent method, the Stavropoulou's method, on the external validation dataset (p < 0.05).

CONCLUSIONS

The proposed algorithm effectively reduced the influence of parenchymal changes, resulting in a reasonably accurate and artifact-free registration.

Prognostic value of pretreatment MRI texture features in breast cancer brain metastasis treated with Gamma Knife radiosurgery

BACKGROUND

Gamma Knife radiosurgery (GKS) was recommended for treating patients with breast cancer brain metastasis (BCBM), but predictions of the existing prognostic models for therapeutic responsiveness vary substantially.

PURPOSE

To investigate the prognostic value of pretreatment clinical, MRI radiologic, and texture features in patients with BCBM undergoing GKS.

MATERIAL AND METHODS

The data of 81 BCBMs in 44 patients were retrospectively reviewed. Progressive disease was defined as an increase of at least 20% in the longest diameter of the target lesion or the presence of new intracranial lesions on contrast-enhanced T1-weighted (CE-T1W) imaging. Radiomic features were extracted from pretreatment CE-T1W images, T2-weighted (T2W) images, and ADC maps. Cox proportional hazard analyses were performed to identify independent predictors associated with BCBM-specific progression-free survival (PFS). A nomogram was constructed and its calibration ability was assessed.

RESULTS

The cumulative BCBM-specific PFS was 52.27% at six months and 11.36% at one year, respectively. Age (hazard ratio [HR] 1.04; 95% confidence interval [CI] 1.01-1.06; P = 0.004) and CE-T1W-based kurtosis (HR 0.72; 95% CI 0.57-0.92; P = 0.008) were the independent predictors. The combination of CE-T1W-based kurtosis and age displayed a higher C-index (C-index 0.70; 95% CI 0.63-0.77) than did CE-T1W-based kurtosis (C-index 0.65; 95% CI 0.57-0.73) or age (C-index 0.63; 95% CI 0.56-0.70) alone. The nomogram based on the combinative model provided a better performance over age (P < 0.05). The calibration curves elucidated good agreement between prediction and observation for the probability of 7- and 12-month BCBM-specific PFS.

CONCLUSION

Pretreatment CE-T1W-based kurtosis combined with age could improve prognostic ability in patients with BCBM undergoing GKS.

Diffeomorphic respiratory motion estimation of thoracoabdominal organs for image-guided interventions

PURPOSE

Percutaneous image-guided interventions are commonly used for the diagnosis and treatment of cancer. In practice, physiological breathing-induced motion increases the difficulty of accurately inserting needles into tumors without impairing the surrounding vital structures. In this work, we propose a data-driven patient-specific hierarchical respiratory motion estimation framework to accurately estimate the position of a tumor and surrounding vital tissues in real time.

METHODS

The motion of optical markers attached to the chest or abdomen skin is used as a surrogate signal to estimate tumor motion based on ɛ-support vector regression (ɛ-SVR). With the estimated tumor motion as the input, a novel respiratory motion model is developed to estimate the diffeomorphic deformation field of the whole organ (liver or lung) without intraoperative, iterative optimization computations. The respiratory motion model of the whole organ is established in Lie algebra space based on the kriging algorithm to ensure that the estimated deformation field is diffeomorphic, optimal, and unbiased. Preoperative prior knowledge for modeling the motion of whole organs is obtained by deformation registration between four-dimensional computed tomography (4D CT) images using a hybrid diffeomorphic registration method.

RESULTS AND CONCLUSIONS

Experimental results on an in vivo beagle dog show that the minimum value of the determinant of the Jacobian of the estimated deformation field is greater than zero, so the estimated deformation field of the whole liver with our method is diffeomorphic. The mean position error of the tumor is 1.2 mm corresponding to a mean accuracy improvement of 76.5%, and the mean position error of the whole liver is 2.1 mm, corresponding to a mean accuracy improvement of 37.9%. The experimental results based on public human subject data show that the mean position error of the tumor is 1.1 mm, corresponding to a mean accuracy improvement of 83.1%, and the mean position error of the whole lung is 2.1 mm, corresponding to a mean accuracy improvement of 41.4%. The positioning errors for the tumor and whole organ are hierarchical and consistent with clinical demand.

Multiparametric Magnetic Resonance Imaging-Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension With Prostate Cancer

BACKGROUND

Preoperative prediction of extracapsular extension (ECE) of prostate cancer (PCa) is important to guide clinical decision-making and improve patient prognosis.

PURPOSE

To investigate the value of multiparametric magnetic resonance imaging (mpMRI)-based peritumoral radiomics for preoperative prediction of the presence of ECE.

STUDY TYPE

Retrospective.

POPULATION

Two hundred eighty-four patients with PCa from two centers (center 1: 226 patients; center 2: 58 patients). Cases from center 1 were randomly divided into training (158 patients) and internal validation (68 patients) sets. Cases from center 2 were assigned to the external validation set.

FIELD STRENGTH/SEQUENCE

A 3.0 T MRI scanners (three vendors). Sequence: Pelvic T2-weighted turbo/fast spin echo sequence and diffusion weighted echo planar imaging sequence.

ASSESSMENT

The peritumoral region (PTR) was obtained by 3-12 mm (half of the tumor length) 3D dilatation of the intratumoral region (ITR). Single-MRI radiomics signatures, mpMRI radiomics signatures, and integrated models, which combined clinical characteristics with the radiomics signatures were built. The discrimination ability was assessed by area under the receiver operating characteristic curve (AUC) in the internal and external validation sets.

STATISTICAL TESTS

Fisher's exact test, Mann-Whitney U-test, DeLong test.

RESULTS

The PTR radiomics signatures demonstrated significantly better performance than the corresponding ITR radiomics signatures (AUC: 0.674 vs. 0.554, P < 0.05 on T2-weighted, 0.652 vs. 0.546, P < 0.05 on apparent diffusion coefficient, 0.682 vs. 0.556 on mpMRI in the external validation set). The integrated models combining the PTR radiomics signature with clinical characteristics performed better than corresponding radiomics signatures in the internal validation set (eg. AUC: 0.718 vs. 0.671, P < 0.05 on mpMRI) but performed similar in the external validation set (eg. AUC: 0.684, vs. 0.682, P = 0.45 on mpMRI).

DATA CONCLUSION

The peritumoral radiomics can better predict the presence of ECE preoperatively compared with the intratumoral radiomics and may have better generalization than clinical characteristics. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: 2.

Deep Learning Predicts Overall Survival of Patients With Unresectable Hepatocellular Carcinoma Treated by Transarterial Chemoembolization Plus Sorafenib

Objectives

To develop and validate a deep learning-based overall survival (OS) prediction model in patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) plus sorafenib.

Methods

This retrospective multicenter study consisted of 201 patients with treatment-naïve, unresectable HCC who were treated with TACE plus sorafenib. Data from 120 patients were used as the training set for model development. A deep learning signature was constructed using the deep image features from preoperative contrast-enhanced computed tomography images. An integrated nomogram was built using Cox regression by combining the deep learning signature and clinical features. The deep learning signature and nomograms were also externally validated in an independent validation set of 81 patients. C-index was used to evaluate the performance of OS prediction.

Results

The median OS of the entire set was 19.2 months and no significant difference was found between the training and validation cohort (18.6 months vs. 19.5 months, P = 0.45). The deep learning signature achieved good prediction performance with a C-index of 0.717 in the training set and 0.714 in the validation set. The integrated nomogram showed significantly better prediction performance than the clinical nomogram in the training set (0.739 vs. 0.664, P = 0.002) and validation set (0.730 vs. 0.679, P = 0.023).

Conclusion

The deep learning signature provided significant added value to clinical features in the development of an integrated nomogram which may act as a potential tool for individual prognosis prediction and identifying HCC patients who may benefit from the combination therapy of TACE plus sorafenib.

Predicting the Outcome of Transcatheter Arterial Embolization Therapy for Unresectable Hepatocellular Carcinoma Based on Radiomics of Preoperative Multiparameter MRI

BACKGROUND

In unresectable hepatocellular carcinoma (HCC), methods to predict patients at increased risk of progression are required.

PURPOSE

To investigate the feasibility of radiomics model in predicting early progression of unresectable HCC after transcatheter arterial chemoembolization (TACE) therapy using preoperative multiparametric magnetic resonance imaging (MP-MRI).

STUDY TYPE

Retrospective.

POPULATION

A total of 84 patients with BCLC B stage HCC from one medical center. According to the modified response evaluation criteria in solid tumors, patients who progressed at 6 months after TACE therapy were assigned as the progressive disease (PD) group (n = 32). Patients whose MRI was performed on four devices were divided into a training cohort (n = 67). Patients whose MRI was performed on other than the previous four devices were used as the testing set (n = 17).

FIELD STRENGTH/SEQUENCE

3.0T, 1.5T axial T₂ -weighted imaging (T₂ WI), diffusion-weighted imaging (DWI, b = 0, 500 s/mm² ), and apparent diffusion coefficient (ADC) ASSESSMENT: PD was confirmed via imaging studies with MRI. Risk factors, including age, alpha fetoprotein (AFP), size, and radiomic-related features of PD were assessed. In addition, the discrimination ability of each radiomics signature was tested on an independent testing set.

STATISTICAL TESTS

The area under the receiver-operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and testing sets. The results indicated that the MP-MRI model achieved the greatest benefit.

RESULTS

In the testing set, the model based on DWI features presented an AUC of (b = 0, 0.786; b = 500, 0.729), followed by T₂ WI features (0.729) and ADC (0.714). The AUC of the MP-MRI signature was increased to 0.800 compared to any single MRI signature. The multivariate logistic analysis identified the radiomics signature as independent parameters of PD, while clinical information such as age, AFP, size, etc., had no significance in the PD group.

DATA CONCLUSION

Preoperative MP-MRI has the potential to predict the outcome of TACE therapy for unresectable HCC. In addition, these image features may be complementary to the current staging systems of HCC patients.

LEVEL OF EVIDENCE

2.

TECHNICAL EFFICACY STAGE

3. J. Magn. Reson. Imaging 2020;52:1083-1090.

A novel method for estimating nail-tract bone density for intertrochanteric fractures

SUMMARY

A novel method based on voxel-based morphometry was proposed to investigate the average volumetric bone mineral density (vBMD) of femoral head nail tract in patients treated with intramedullary nails-proximal femoral nail antirotation (PFNA) and gamma nail (GN). The results showed that there was no significant difference in average vBMD between the two groups.

BACKGROUND

For unstable intertrochanteric fractures, poor bone quality might be one of the most important causes of cut-out complications in the femoral head during surgical treatment. Bone quality is generally regarded as an equivalent of BMD. Thus, we develop a novel voxel-based morphometry-based method to quantify vBMD of the femoral head nail tract.

METHODS

Automatic calculation of average vBMD of nail tracts requires three main steps. First, we built a standard nail tract in a proximal femur template. Then, we mapped the proximal femur structure of each patient to the template by B-spline and Demons registration so that the anatomical positions of the proximal femur of all patients spatially corresponded to the standard template. Finally, we calculated and visualized the average vBMD distribution of the nail tract of all patients. To verify the feasibility of the method, we enrolled 75 patients (52 women and 23 men) with hip fractures to our study to compare measurements. The root mean square of the standard deviation (RMSSD) was calculated, and the coefficient of variation (CV) of the RMSSD (CV-RMSSD) was used to evaluate the reproducibility of intraoperator and interscan measurements. The Mann-Whitney U test was used to compare the average vBMD of nail tracts for the PFNA and GN.

RESULTS

The CV-RMSSD of intraoperator measurements ranged from 1.0% to 2.0%, and the CV-RMSSD of interscan measurements ranged from 3.6% to 4.5%. There was no significant difference in the average vBMD between patients with PFNAs and those with GNs (p > 0.05).

CONCLUSIONS

The proposed method is reproducible for determining the average vBMD, which may provide a reference index for selection of appropriate intramedullary nails for individual patients. The current choice of intramedullary nail based on the experience of a surgeon may be biased.

THE TRANSLATIONAL POTENTIAL OF THIS ARTICLE

A novel method was proposed to measure the spatial average vBMD of nail tracts, which has good potential to provide a reference index for surgeons to choose appropriate implants.

Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer

OBJECTIVES

To develop and validate radiomic models in evaluating biological characteristics of rectal cancer based on multiparametric magnetic resonance imaging (MP-MRI).

METHODS

This study consisted of 345 patients with rectal cancer who underwent MP-MRI. We focused on evaluating five postoperative confirmed characteristics: lymph node (LN) metastasis, tumor differentiation, fraction of Ki-67-positive tumor cells, human epidermal growth factor receptor 2 (HER-2), and KRAS-2 gene mutation status. Data from 197 patients were used to develop the biological characteristics evaluation models. Radiomic features were extracted from MP-MRI and then refined for reproducibility and redundancy. The refined features were investigated for usefulness in building radiomic signatures by using two feature-ranking methods (MRMR and WLCX) and three classifiers (RF, SVM, and LASSO). Multivariable logistic regression was used to build an integrated evaluation model combining radiomic signatures and clinical characteristics. The performance was evaluated using an independent validation dataset comprising 148 patients.

RESULTS

The MRMR and LASSO regression produced the best-performing radiomic signatures for evaluating HER-2, LN metastasis, tumor differentiation, and KRAS-2 gene status, with AUC values of 0.696 (95% CI, 0.610-0.782), 0.677 (95% CI, 0.591-0.763), 0.720 (95% CI, 0.621-0.819), and 0.651 (95% CI, 0.539-0.763), respectively. The best-performing signatures for evaluating Ki-67 produced an AUC value of 0.699 (95% CI, 0.611-0.786), and it was developed by WLCX and RF algorithm. The integrated evaluation model incorporating radiomic signature and MRI-reported LN status had improved AUC of 0.697 (95% CI, 0.612-0.781).

CONCLUSION

Radiomic signatures based on MP-MRI have potential to noninvasively evaluate the biological characteristics of rectal cancer.

KEY POINTS

• Radiomic features were extracted from MP-MRI images of the rectal tumor. • The proposed radiomic signatures demonstrated discrimination ability in identifying the histopathological, immunohistochemical, and genetic characteristics of rectal cancer. • All MRI sequences were important and could provide complementary information in radiomic analysis.

Bone-Subtracted Spinal CT Angiography Using Nonrigid Registration for Better Visualization of Arterial Feeders in Spinal Arteriovenous Fistulas

BACKGROUND AND PURPOSE

Pretreatment diagnosis for the location of shunts and arterial feeders of spinal arteriovenous fistulas is crucial. This study aimed to evaluate the utility of subtracted CT angiography imaging by using nonrigid registration (R-CTA) in patients with spinal arteriovenous fistulas compared with conventional CTA imaging.

MATERIALS AND METHODS

The records of 15 consecutive subjects (mean age, 65 years; 2 women) who had undergone CTA and digital subtraction angiography for clinically suspected spinal arteriovenous fistula were reviewed. From CTA images obtained at the arterial and late arterial phases, warped images of the late arterial phase were obtained by using nonrigid registration that was adjusted to the arterial phase images. R-CTA images were then obtained by subtracting the warped images from the arterial phase images. The accuracies of using nonrigid registration and conventional spinal CTA and the time required for detecting arterial feeders in spinal arteriovenous fistulas were analyzed for each patient with DSA results as a standard reference. The difference between R-CTA and conventional spinal CTA was assessed by the Welch test and the McNemar χ(2) test.

RESULTS

R-CTA had a higher accuracy compared with conventional spinal CTA (80% versus 47%, P = .025). The time for interpretation was reduced in R-CTA compared with conventional spinal CTA (45.1 versus 97.1 seconds, P = .002).

CONCLUSIONS

Our subtracted CTA imaging by using nonrigid registration detects feeders of spinal arteriovenous fistulas more accurately and quickly than conventional CTA.