Observation of different tumor motion magnitude within liver and estimate of internal motion margins in postoperative patients with hepatocellular carcinoma

Long-term outcome of centrally located hepatocellular carcinoma treated by neoadjuvant radiotherapy and radical resection: a propensity score matched study

Objective

Centrally located hepatocellular carcinoma (HCC) typically presents challenges in surgical intervention and is associated with a bleak prognosis. In order to address this pressing issue, it is imperative to identify a comprehensive treatment approach, such as neoadjuvant radiotherapy (neoRT), that can enhance the prognosis of patients diagnosed with centrally located HCC.

Methods

Patients who had surgical resections for HCC between March 2015 and December 2020 were included in the study. Patients were assigned to either the neoRT combined with liver resection (neoRT+LR) group or the liver resection alone (LR) group. The study employed propensity-score analysis and Cox proportional-hazards regression models as research methodologies. Using the Kaplan-Meier method, overall survival (OS) and disease-free survival (DFS) were estimated in patients.

Results

During the study, 162 patients were enrolled, with 41 receiving neoRT+LR and 121 receiving LR. The duration of the median follow-up period was 45 months. The 1-year, 3-year, and 5-year OS rates were 95, 70, and 70% for patients in the neoRT+LR group, and 82, 64, and 54% for patients in the LR group, respectively. The 1-year, 3-year, 5-year DFS rates were 71, 53, and 37% for patients in the neoRT+LR group, and 52, 38, and 34% for patients in the LR group, respectively. A successful matching of 37 patients was achieved through propensity-score analysis. OS and DFS after matching analysis was statistically different between the two groups ( P=0.0099, P=0.034, respectively). neoRT was an independent prognostic factor for OS and DFS [hazard ratio (HR)=0.47, 95% CI: 0.24-0.93; HR=0.56, 95% CI: 0.34-0.92, respectively]. According to matching analysis, there were no statistically significant differences observed in terms of baseline characteristics, surgical safety, and complications between the groups.

Conclusion

Liver resection and neoRT can be advantageous for patients with centrally located HCC.

Online prediction for respiratory movement compensation: a patient-specific gating control for MRI-guided radiotherapy

BACKGROUND

This study aims to validate the effectiveness of linear regression for motion prediction of internal organs or tumors on 2D cine-MR and to present an online gating signal prediction scheme that can improve the accuracy of MR-guided radiotherapy for liver and lung cancer.

MATERIALS AND METHODS

We collected 2D cine-MR sequences of 21 liver cancer patients and 10 lung cancer patients to develop a binary gating signal prediction algorithm that forecasts the crossing-time of tumor motion traces relative to the target threshold. Both 0.4 s and 0.6 s prediction windows were tested using three linear predictors and three recurrent neural networks (RNNs), given the system delay of 0.5 s. Furthermore, an adaptive linear regression model was evaluated using only the first 30 s as the burn-in period, during which the model parameters were adapted during the online prediction process. The accuracy of the predicted traces was measured using amplitude metrics (MAE, RMSE, and R2), and in addition, we proposed three temporal metrics, namely crossing error, gating error, and gating accuracy, which are more relevant to the nature of the gating signals.

RESULTS

In both 0.6 s and 0.4 s prediction cases, linear regression outperformed other methods, demonstrating significantly smaller amplitude errors compared to the RNNs (P < 0.05). The proposed algorithm with adaptive linear regression had the best performance with an average gating accuracy of 98.3% and 98.0%, a gating error of 44 ms and 45 ms, for liver cancer and lung cancer patients, respectively.

CONCLUSION

A functional online gating control scheme was developed with an adaptive linear regression that is both more cost-efficient and accurate than sophisticated RNN based methods in all studied metrics.

Clinical Benefits of Neoadjuvant Radiotherapy on the Postoperative Recurrence of Centrally Located Hepatocellular Carcinoma: A Real-World Evidence Based on Phase II Clinical Trial

Objective

Although surgical resection is one of the most effective way to treat liver cancer, its efficacy and safety in treatment of centrally located hepatocellular carcinoma (HCC) remains elusive. Therefore, it is very important to find a comprehensive treatment mode, such as radical resection combined with neoadjuvant radiotherapy (neoRT).

Methods

The centrally located HCC patients who underwent radical resection from July 2015 to April 2021 were enrolled. According to whether the neoRT was implemented or not, these patients were allocated into neoadjuvant radiotherapy combined with liver resection (neoRT+LR) and liver resection alone (LR) group. The research method used propensity-score analysis and Cox proportional-hazards regression models. We generated an E-value to assess the sensitivity to unmeasured confounding. This study is a real-world, retrospective study based on phase II clinical trial.

Results

A total of 168 patients were enrolled, including 38 patients treating with neoRT+LR and 130 patients with LR. The 1-, 3-, 5-year disease free survival (DFS) rates were 74%, 55% and 39% in the neoRT+LR group, and 44%, 28%, and 24% in the LR group, respectively. Neoadjuvant radiotherapy was an independent prognostic factor for postoperative recurrence ([HR]0.42, 95% CI [0.25, 0.69]). There was significant association between neoRT+LR and longer disease-free survival (Match, [HR] 0.43, 95% CI [0.24, 0.76]; GenMatch, [HR] 0.32, 95% CI [0.23, 0.43]; Adjusted for propensity score, [HR] 0.41, 95% CI [0.23, 0.73]; Inverse probability weighting, [HR] 0.38, 95% CI [0.22, 0.65], respectively). DFS before and after matching analysis was statistically different in two groups (p-value=0.005, p-value=0.0024, respectively). Neoadjuvant radiotherapy can significantly reduce the postoperative early recurrence (p-value <0.05). E-value analysis suggested robustness to unmeasured confounding.

Conclusion

Liver resection combined with neoadjuvant radiotherapy was effective and safe for treatment of centrally located HCC patients, which improved the prognosis of patients and reduced the incidence of early recurrence.

Real-time motion monitoring using orthogonal cine MRI during MR-guided adaptive radiation therapy for abdominal tumors on 1.5T MR-Linac

BACKGROUND

Real-time motion monitoring (RTMM) is necessary for accurate motion management of intrafraction motions during radiation therapy (RT).

PURPOSE

Building upon a previous study, this work develops and tests an improved RTMM technique based on real-time orthogonal cine magnetic resonance imaging (MRI) acquired during magnetic resonance-guided adaptive RT (MRgART) for abdominal tumors on MR-Linac.

METHODS

A motion monitoring research package (MMRP) was developed and tested for RTMM based on template rigid registration between beam-on real-time orthogonal cine MRI and pre-beam daily reference 3D-MRI (baseline). The MRI data acquired under free-breathing during the routine MRgART on a 1.5T MR-Linac for 18 patients with abdominal malignancies of 8 liver, 4 adrenal glands (renal fossa), and 6 pancreas cases were used to evaluate the MMRP package. For each patient, a 3D mid-position image derived from an in-house daily 4D-MRI was used to define a target mask or a surrogate sub-region encompassing the target. Additionally, an exploratory case reviewed for an MRI dataset of a healthy volunteer acquired under both free-breathing and deep inspiration breath-hold (DIBH) was used to test how effectively the RTMM using the MMRP can address through-plane motion (TPM). For all cases, the 2D T2/T1-weighted cine MRIs were captured with a temporal resolution of 200 ms interleaved between coronal and sagittal orientations. Manually delineated contours on the cine frames were used as the ground-truth motion. Common visible vessels and segments of target boundaries in proximity to the target were used as anatomical landmarks for reproducible delineations on both the 3D and the cine MRI images. Standard deviation of the error (SDE) between the ground-truth and the measured target motion from the MMRP package were analyzed to evaluate the RTMM accuracy. The maximum target motion (MTM) was measured on the 4D-MRI for all cases during free-breathing.

RESULTS

The mean (range) centroid motions for the 13 abdominal tumor cases were 7.69 (4.71-11.15), 1.73 (0.81-3.05), and 2.71 (1.45-3.93) mm with an overall accuracy of <2 mm in the superior-inferior (SI), the left-right (LR), and the anterior-posterior (AP) directions, respectively. The mean (range) of the MTM from the 4D-MRI was 7.38 (2-11) mm in the SI direction, smaller than the monitored motion of centroid, demonstrating the importance of the real-time motion capture. For the remaining patient cases, the ground-truth delineation was challenging under free-breathing due to the target deformation and the large TPM in the AP direction, the implant-induced image artifacts, and/or the suboptimal image plane selection. These cases were evaluated based on visual assessment. For the healthy volunteer, the TPM of the target was significant under free-breathing which degraded the RTMM accuracy. RTMM accuracy of <2 mm was achieved under DIBH, indicating DIBH is an effective method to address large TPM.

CONCLUSIONS

We have successfully developed and tested the use of a template-based registration method for an accurate RTMM of abdominal targets during MRgART on a 1.5T MR-Linac without using injected contrast agents or radio-opaque implants. DIBH may be used to effectively reduce or eliminate TPM of abdominal targets during RTMM.

Machine learning for predicting accuracy of lung and liver tumor motion tracking using radiomic features

Background

Internal tumor motion is commonly predicted using external respiratory signals. However, the internal/external correlation is complex and patient-specific. The purpose of this study was to develop various models based on the radiomic features of computed tomography (CT) images to predict the accuracy of tumor motion tracking using external surrogates and to find accurate and reliable tracking algorithms.

Methods

Images obtained from a total of 108 and 71 patients pathologically diagnosed with lung and liver cancers, respectively, were examined. Real-time position monitoring motion was fitted to tumor motion, and samples with fitting errors greater than 2 mm were considered positive. Radiomic features were extracted from internal target volumes of average intensity projections, and cross-validation least absolute shrinkage and selection operator (LassoCV) was used to conduct feature selection. Based on the radiomic features, a total of 26 separate models (13 for the lung and 13 for the liver) were trained and tested. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to assess performance. Relative standard deviation was used to assess stability.

Results

Thirty-three and 22 radiomic features were selected for the lung and liver, respectively. For the lung, the AUC varied from 0.848 (decision tree) to 0.941 [support vector classifier (SVC), logistic regression]; sensitivity varied from 0.723 (extreme gradient boosting) to 0.848 [linear support vector classifier (linearSVC)]; specificity varied from 0.834 (gaussian naive bayes) to 0.936 [multilayer perceptron (MLP), wide and deep (W&D)]; and MLP and W&D had better performance and stability than the median. For the liver, the AUC varied from 0.677 [light gradient boosting machine (Light)] to 0.892 (logistic regression); sensitivity varied from 0.717 (W&D) to 0.862 (MLP); specificity varied from 0.566 (Light) to 0.829 (linearSVC); and logistic regression, MLP, and SVC had better performance and stability than the median.

Conclusions

Respiratory-sensitive radiomic features extracted from CT images of lung and liver tumors were proved to contain sufficient information to establish an external/internal motion relationship. We developed a rapid and accurate method based on radiomics to classify the accuracy of monitoring a patient's external surface for lung and liver tumor tracking. Several machine learning algorithms-in particular, MLP-demonstrated excellent classification performance and stability.

Utilization of Diaphragm Motion to Predict the Displacement of Liver Tumors for Patients Treated with Carbon ion Radiotherapy

Objectives: To establish and validate a linear model utilizing diaphragm motion (DM) to predict the displacement of liver tumors (DLTs) for patients who underwent carbon ion radiotherapy (CIRT). A total of 60 pairs of planning and reviewing four-dimensional computed tomography (4DCT) sets over 23 patients were used. Method: We constructed an averaged computed tomography (CT) set for each either planning or reviewing 4DCT within respiratory phases between 20% of exhale and inhale. A rigid image registration to align bony structures was performed between planning and reviewing 4DCT. The position changes on top of diaphragm in superior-inferior (SI) direction between 2 CTs to present DM were obtained. The translational vectors in SI from matching to present DLT were obtained. The linear model was built by training data for 23 imaging pairs. A distance model utilized the cumulative probability distribution (CPD) of DM or DLT and was compared with the linear model. We conducted the statistical regression analysis with receiver operating characteristic (ROC) testing data of 37 imaging pairs to validate the performance of our linear model. Results: The DM within 0.5 mm was true positive (TP) with an area under the ROC curve (AUC) of 0.983 to predict DLT. The error of predicted DLT within half of its mean value indicated the reliability of prediction method. The 23 pairs of data showed (4.5 ± 3.3) mm for trend of DM and (2.2 ± 1.6) mm for DLT. A linear model of DLT = 0.46*DM + 0.12 was established. The predicted DLT was (2.2 ± 1.5) mm with a prediction error of (0.3 ± 0.3) mm. The accumulated probability of observed and predicted DLT with < 5.0 mm magnitude was 93.2% and 94.5%, respectively. Conclusion: We utilized the linear model to set the proper beam gating for predicting DLT within 5.0 mm to treat patients. We will investigate a proper process on x-ray fluoroscopy images to establish a reliable model predicting DLT for DM observed in x-ray fluoroscopy in the following two years.

Interfractional diaphragmatic position variation according to stomach volume change during respiratory-gated radiotherapy for hepatocellular carcinoma

PURPOSE

We evaluated the correlation between stomach volume change and interfractional baseline shifts of the diaphragm in image-guided radiotherapy (IGRT) for hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Twenty-four patients with HCC underwent ten fractions of IGRT, and a total of 240 cone beam computed tomography (CBCT) and on-board imager (OBI) kV image sets were acquired. These image sets were retrospectively analyzed. Baseline shifts of the diaphragm relative to bone and stomach volume change ratios were evaluated using four-dimensional simulation CT, kV image, and CBCT images. Associations between baseline shifts and patient physiologic factors were investigated.

RESULTS

The average baseline shift of the diaphragm in the superior-inferior (SI) direction was 1.5 mm (standard deviation 4.6 mm), which was higher than the shift in other directions (0.7, 2.0 mm and 0.9, 2.6 mm in right-left (RL) and anterior-posterior (AP) directions, respectively). Interfractional baseline shifts of the diaphragm in the SI and AP directions were positively correlated with the stomach volume change ratio (Pearson's r: 0.416 and 0.302, p-value: <0.001 and <0.001, respectively).

CONCLUSIONS

The interfractional baseline shifts of the diaphragm in the SI and AP directions correlated well with stomach volume changes. Efforts to maintain a constant stomach volume before the simulation and each treatment, such as fasting, may reduce interfractional baseline shifts of liver tumors.

Safety and Efficacy of Transcatheter Arterial Chemoembolization Plus Radiotherapy Combined With Sorafenib in Hepatocellular Carcinoma Showing Macrovascular Invasion

The safety and efficacy of transcatheter arterial chemoembolization (TACE) plus intensity-modulated radiotherapy (IMRT) combined with sorafenib in hepatocellular carcinoma (HCC) showing macrovascular invasion (MVI) remain controversial. The records of 63 patients with HCC showing MVI, who underwent IMRT plus TACE combined with (28 participants; Group A) or without (35 participants; Group B) sorafenib from 2015 to 2018, were retrospectively reviewed to assess the progression-free survival (PFS), overall survival (OS), and treatment-associated toxicity. The median PFS was longer in Group A (13.6 months) than in Group B (9.2 months), and still significant after propensity score matching (PSM). However, the median OS was similar in the two groups (19.0 vs. 15.2 months, P = 0.094 before PSM; P = 0.204 after PSM). The grade 3 hematologic and hepatic toxicity was present in 10 (15.9%) and 7 (11.1%) patients, respectively. The incidence of skin reaction, hand-foot syndrome, and diarrhea, all grade 1-2 adverse events, was significantly higher in Group A than in Group B. No patient experienced grade 4 or 5 toxicity, and radiation-induced liver disease was also not observed. TACE plus IMRT combined with sorafenib showed a good safety profile and clinical benefit in patients with HCC having MVI.