A Super-Learner Model for Tumor Motion Prediction and Management in Radiation Therapy: Development and Feasibility Evaluation

Convolutional LSTM model for cine image prediction of abdominal motion

Objective.In this study, we tackle the challenge of latency in magnetic resonance linear accelerator (MR-Linac) systems, which compromises target coverage accuracy in gated real-time radiotherapy. Our focus is on enhancing motion prediction precision in abdominal organs to address this issue. We developed a convolutional long short-term memory (convLSTM) model, utilizing 2D cine magnetic resonance (cine-MR) imaging for this purpose.Approach.Our model, featuring a sequence-to-one architecture with six input frames and one output frame, employs structural similarity index measure (SSIM) as loss function. Data was gathered from 17 cine-MRI datasets using the Philips Ingenia MR-sim system and an Elekta Unity MR-Linac equivalent sequence, focusing on regions of interest (ROIs) like the stomach, liver, pancreas, and kidney. The datasets varied in duration from 1 to 10 min.Main results.The study comprised three main phases: hyperparameter optimization, individual training, and transfer learning with or without fine-tuning. Hyperparameters were initially optimized to construct the most effective model. Then, the model was individually applied to each dataset to predict images four frames ahead (1.24-3.28 s). We evaluated the model's performance using metrics such as SSIM, normalized mean square error, normalized correlation coefficient, and peak signal-to-noise ratio, specifically for ROIs with target motion. The average SSIM values achieved were 0.54, 0.64, 0.77, and 0.66 for the stomach, liver, kidney, and pancreas, respectively. In the transfer learning phase with fine-tuning, the model showed improved SSIM values of 0.69 for the liver and 0.78 for the kidney, compared to 0.64 and 0.37 without fine-tuning.Significance. The study's significant contribution is demonstrating the convLSTM model's ability to accurately predict motion for multiple abdominal organs using a Unity-equivalent MR sequence. This advancement is key in mitigating latency issues in MR-Linac radiotherapy, potentially improving the precision and effectiveness of real-time treatment for abdominal cancers.

Implications of rapid population growth on survey design and HIV estimates in the Rakai Community Cohort Study (RCCS), Uganda

OBJECTIVE

Since rapid population growth challenges longitudinal population-based HIV cohorts in Africa to maintain coverage of their target populations, this study evaluated whether the exclusion of some residents due to growing population size biases key HIV metrics like prevalence and population-level viremia.

DESIGN, SETTING AND PARTICIPANTS

Data were obtained from the Rakai Community Cohort Study (RCCS) in south central Uganda, an open population-based cohort which began excluding some residents of newly constructed household structures within its surveillance boundaries in 2008. The study includes adults aged 15-49 years who were censused from 2019 to 2020.

MEASURES

We fit ensemble machine learning models to RCCS census and survey data to predict HIV seroprevalence and viremia (prevalence of those with viral load >1000 copies/mL) in the excluded population and evaluated whether their inclusion would change overall estimates.

RESULTS

Of the 24 729 census-eligible residents, 2920 (12%) residents were excluded from the RCCS because they were living in new households. The predicted seroprevalence for these excluded residents was 10.8% (95% CI: 9.6% to 11.8%)-somewhat lower than 11.7% (95% CI: 11.2% to 12.3%) in the observed sample. Predicted seroprevalence for younger excluded residents aged 15-24 years was 4.9% (95% CI: 3.6% to 6.1%)-significantly higher than that in the observed sample for the same age group (2.6% (95% CI: 2.2% to 3.1%)), while predicted seroprevalence for older excluded residents aged 25-49 years was 15.0% (95% CI: 13.3% to 16.4%)-significantly lower than their counterparts in the observed sample (17.2% (95% CI: 16.4% to 18.1%)). Over all ages, the predicted prevalence of viremia in excluded residents (3.7% (95% CI: 3.0% to 4.5%)) was significantly higher than that in the observed sample (1.7% (95% CI: 1.5% to 1.9%)), resulting in a higher overall population-level viremia estimate of 2.1% (95% CI: 1.8% to 2.4%).

CONCLUSIONS

Exclusion of residents in new households may modestly bias HIV viremia estimates and some age-specific seroprevalence estimates in the RCCS. Overall, HIV seroprevalence estimates were not significantly affected.

[Prediction of respiratory motion based on sequential embedding combined with relational embedding]

OBJECTIVE

To propose a deep learning model for modeling and prediction of the integration of respiratory motion in all directions.

METHODS

The respiratory motion signals in different directions were input into the sequential embedding layer composed of LSTM to capture the sequential dependence of the historical motion state and obtain the sequential embedding representation, which enabled relational embedding in all directions through the self-attention mechanism to obtain the relational embedding representation. The sequential embedding representation and the relational embedding representation were concatenated and input into a prediction layer consisting of a fully connected neural network to generate nonlinear prediction components, which were added to the linear prediction components generated by the autoregressive module parallel to the above structure to generate the final prediction. The model was trained using a 'pre-training + fine-tuning' approach. In the validation experiments, 304 respiratory motion trajectories were used for model pre-training, and 7 evaluation samples were used for model testing.

RESULTS

The proposed prediction model achieved more accurate prediction results than other methods. For the 7 evaluation samples with different delay time, the proposed prediction model achieved a reduction of absolute deviations in the 3D directions by over 70%.

CONCLUSION

The proposed model is capable of accurate prediction of respiratory motion and can thus help to reduce system delay in precise radiotherapy.

Stereotactic Body Radiotherapy Boost with the CyberKnife for Locally Advanced Cervical Cancer: Dosimetric Analysis and Potential Clinical Benefits

(1) Aim: To compare the treatment plans of stereotactic body radiotherapy (SBRT) with CyberKnife (CK) and high-dose-rate (HDR) intracavitary/interstitial brachytherapy (IC/ISBT) and examine the feasibility of CK-SBRT as a viable alternative to BT in patients with locally advanced cervical cancer (LACC). (2) Methods: A BT plan of 28 Gy in four fractions delivered previously to 20 patients with LACC was compared with a CK plan based on the same CT images with structures delineation for BT. The SBRT treatment plan was further divided according to two different approaches, with the high-risk planning target volume (HR-PTV) defined by the high-risk clinical target volume (HR-CTV) without and with a 5 mm margin, which were named CK-CTV plan and CK-PTV plan, respectively. The dose distributions and dosimetric parameters of the target volumes and organs at risk (OARs) were recorded and compared for the three boost plans. Radiobiological metrics were calculated based on the EUD for the hybrid plans. Additionally, the relationship between tumor volume and tolerance doses for the OARs in the BT plan and CK-PTV plan was investigated. (3) Results: Target coverage was better with the CK plan than with the BT plan, as the D95%, D98%, HI and CI of the CK-CTV plan and CK-PTV plan were higher than those of the BT plan; an exception was the D50%. Similarly, the TCP of the target was also significantly in favor of the CK hybrid plans (p < 0.01). For the OARs, the CK-CTV plan was superior to the BT plan as regards the rectum D2cc, bladder D2cc and bladder Dmax. The CK-PTV plan could achieve dosimetric parameters comparable to those of the BT plan for OARs concerning the small residual tumor volume. The NTCP of the rectum for the WPI+CK-CTV plans was significantly lower than that of the WPI+BT plans (p < 0.01). (4) Conclusions: CK-based SBRT can achieve better target coverage, dose sparing for the OARs and radiobiological effects compared with the BT plan for tumors that are not excessively large. CK-based SBRT could be an alternative option to administer a radiation boost for patients with LACC.

Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network

Objectives

This paper aimed to identify the technology frontiers of artificial intelligence-assisted pathology based on patent citation network.

Methods

Patents related to artificial intelligence-assisted pathology were searched and collected from the Derwent Innovation Index (DII), which were imported into Derwent Data Analyzer (DDA, Clarivate Derwent, New York, NY, USA) for authority control, and imported into the freely available computer program Ucinet 6 for drawing the patent citation network. The patent citation network according to the citation relationship could describe the technology development context in the field of artificial intelligence-assisted pathology. The patent citations were extracted from the collected patent data, selected highly cited patents to form a co-occurrence matrix, and built a patent citation network based on the co-occurrence matrix in each period. Text clustering is an unsupervised learning method, an important method in text mining, where similar documents are grouped into clusters. The similarity between documents are determined by calculating the distance between them, and the two documents with the closest distance are combined. The method of text clustering was used to identify the technology frontiers based on the patent citation network, which was according to co-word analysis of the title and abstract of the patents in this field.

Results

1704 patents were obtained in the field of artificial intelligence-assisted pathology, which had been currently undergoing three stages, namely the budding period (1992–2000), the development period (2001–2015), and the rapid growth period (2016–2021). There were two technology frontiers in the budding period (1992–2000), namely systems and methods for image data processing in computerized tomography (CT), and immunohistochemistry (IHC), five technology frontiers in the development period (2001–2015), namely spectral analysis methods of biomacromolecules, pathological information system, diagnostic biomarkers, molecular pathology diagnosis, and pathological diagnosis antibody, and six technology frontiers in the rapid growth period (2016–2021), namely digital pathology (DP), deep learning (DL) algorithms—convolutional neural networks (CNN), disease prediction models, computational pathology, pathological image analysis method, and intelligent pathological system.

Conclusions

Artificial intelligence-assisted pathology was currently in a rapid development period, and computational pathology, DL and other technologies in this period all involved the study of algorithms. Future research hotspots in this field would focus on algorithm improvement and intelligent diagnosis in order to realize the precise diagnosis. The results of this study presented an overview of the characteristics of research status and development trends in the field of artificial intelligence-assisted pathology, which could help readers broaden innovative ideas and discover new technological opportunities, and also served as important indicators for government policymaking.

Artificial intelligence in radiotherapy

Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.

Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency

The interest in machine learning (ML) has grown tremendously in recent years, partly due to the performance leap that occurred with new techniques of deep learning, convolutional neural networks for images, increased computational power, and wider availability of large datasets. Most fields of medicine follow that popular trend and, notably, radiation oncology is one of those that are at the forefront, with already a long tradition in using digital images and fully computerized workflows. ML models are driven by data, and in contrast with many statistical or physical models, they can be very large and complex, with countless generic parameters. This inevitably raises two questions, namely, the tight dependence between the models and the datasets that feed them, and the interpretability of the models, which scales with its complexity. Any problems in the data used to train the model will be later reflected in their performance. This, together with the low interpretability of ML models, makes their implementation into the clinical workflow particularly difficult. Building tools for risk assessment and quality assurance of ML models must involve then two main points: interpretability and data-model dependency. After a joint introduction of both radiation oncology and ML, this paper reviews the main risks and current solutions when applying the latter to workflows in the former. Risks associated with data and models, as well as their interaction, are detailed. Next, the core concepts of interpretability, explainability, and data-model dependency are formally defined and illustrated with examples. Afterwards, a broad discussion goes through key applications of ML in workflows of radiation oncology as well as vendors' perspectives for the clinical implementation of ML.

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility

This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumor displacement. Specifically, the proposed scheme is driven by two time-series deep-learning algorithmic models: the respiratory-phase classification model and the regression-based prediction model. To assess the performance of the proposed scheme, the classification and prediction models were tested with four categories of datasets: patient-based datasets with regular and irregular breathing patterns; and pseudopatient-based datasets with regular and irregular breathing patterns. In this study, 'pseudopatients' refer to a dynamic thorax phantom with a lung tumor programmed with varying breathing patterns and breaths per minute. The total accuracy of the respiratory-phase classification model was 100%, 100%, 100%, and 92.44% for the four dataset categories, with a corresponding mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R²) of 1.2-1.6%, 0.65-0.8%, and 0.97-0.98, respectively. The results demonstrate that the time-series deep-learning classification and regression-based prediction models can classify the respiratory phases and predict the lung tumor displacement with high accuracy. Essentially, the novelty of this research lies in the use of a low-cost 3D Kinect camera with time-series deep-learning algorithms in the medical field to efficiently classify the respiratory phase and predict the lung tumor displacement.

Predictors of Covid-19 level of concern among older adults from the health and retirement study

The purpose of this longitudinal study is to construct a prediction model for Covid-19 level of concern using established Covid-19 socio-demographic, lifestyle and health risk characteristics and to examine specific contributions of obesity-related cardiometabolic health characteristics as predictors of Covid-19 level of concern among a representative sample of U.S. older adults. We performed secondary analyses of existing data on 2872 2006–2020 Health and Retirement Study participants and examined 19 characteristics in relation to the outcome of interest using logistic regression and machine learning algorithms. In mixed-effects ordinal logistic regression models, a history of diabetes, stroke as well as 1–2 cardiometabolic risk factors and/or chronic conditions were associated with greater Covid-19 level of concern, after controlling for confounders. Female sex, birth cohort, minority race, Hispanic ethnicity and total wealth as well as depressive symptoms were associated with higher level of Covid-19 concern, and education was associated with lower level of Covid-19 concern in fully adjusted mixed-effects ordinal logistic regression models. The selected socio-demographic, lifestyle and health characteristics accounted for < 70% of the variability in Covid-19 level of concern based on machine learning algorithms. Independent risk factors for Covid-19 level of concern among U.S. older adults include socio-demographic characteristics and depressive symptoms. Advanced research is needed to identify relevant predictors and elucidate underlying mechanisms of observed relationships.

Management of Motion and Anatomical Variations in Charged Particle Therapy: Past, Present, and Into the Future

The major aim of radiation therapy is to provide curative or palliative treatment to cancerous malignancies while minimizing damage to healthy tissues. Charged particle radiotherapy utilizing carbon ions or protons is uniquely suited for this task due to its ability to achieve highly conformal dose distributions around the tumor volume. For these treatment modalities, uncertainties in the localization of patient anatomy due to inter- and intra-fractional motion present a heightened risk of undesired dose delivery. A diverse range of mitigation strategies have been developed and clinically implemented in various disease sites to monitor and correct for patient motion, but much work remains. This review provides an overview of current clinical practices for inter and intra-fractional motion management in charged particle therapy, including motion control, current imaging and motion tracking modalities, as well as treatment planning and delivery techniques. We also cover progress to date on emerging technologies including particle-based radiography imaging, novel treatment delivery methods such as tumor tracking and FLASH, and artificial intelligence and discuss their potential impact towards improving or increasing the challenge of motion mitigation in charged particle therapy.

Correlation of Optical Surface Respiratory Motion Signal and Internal Lung and Liver Tumor Motion: A Retrospective Single-Center Observational Study

Purpose: Surface-guided radiation therapy (SGRT) application has limitations. This study aimed to explore the relationship between patient characteristics and their external/internal correlation to qualitatively assess the external/internal correlation in a particular patient. Methods: Liver and lung cancer patients treated with radiotherapy in our institution were retrospectively analyzed. The external/internal correlation were calculated with Spearman correlation coefficient (SCC) and SCC after support vector regression (SVR) fitting (SCC_svr). The relationship between the external/internal correlation and magnitudes of motion of the tumor and external marker (A_i, A_e), tumor volume V_t, patient age, gender, and tumor location were explored. Results: The external/internal motions of liver and lung cancer patients were strongly correlated in the S-I direction, with mean SCC_svr values of 0.913 and 0.813. The correlation coefficients between the external/internal correlations and the patients’ characteristics (A_i, A_e, V_t, and age) were all smaller than 0.5; A_i, A_e and liver tumor volumes were positively correlated with the strength of the external/internal correlation, while lung tumor volumes and patient age were negative. The external/internal correlations in males and females were roughly equal, and the external/internal correlations in patients with peripheral lung cancers were stronger than those in patients with central lung cancers. Conclusion: The external/internal correlation shows great individual differences. The effects of A_i, A_e, V_t, and age are weakly to moderately correlated. Our results suggest the necessity of individualized assessment of patient's external/internal motion correlation prior to the application of SGRT technique for breath motion monitoring.

Toward MR-integrated proton therapy: modeling the potential benefits for liver tumors

Magnetic resonance imaging (MRI)-integrated proton therapy (MRiPT) is envisioned to improve treatment quality for many cancer patients. However, given the availability of alternative image-guided strategies, its clinical need is yet to be justified. This study aims to compare the expected clinical outcomes of MRiPT with standard of practice cone-beam CT (CBCT)-guided PT, and other MR-guided methods, i.e. offline MR-guided PT and MR-linac, for treatment of liver tumors. Clinical outcomes were assessed by quantifying the dosimetric and biological impact of target margin reduction enabled by each image-guided approach. Planning target volume (PTV) margins were calculated using random and systematic setup, delineation and motion uncertainties, which were quantified by analyzing longitudinal MRI data for 10 patients with liver tumors. Proton treatment plans were created using appropriate PTV margins for each image-guided PT method. Photon plans with margins equivalent to MRiPT were generated to represent MR-linac. Normal tissue complication probabilities (NTCP) of the uninvolved liver were compared. We found that PTV margin can be reduced by 20% and 40% for offline MR-guided PT and MRiPT, respectively, compared with CBCT-guided PT. Furthermore, clinical target volume expansion could be largely alleviated when delineating on MRI rather than CT. Dosimetric implications included decreased equivalent mean dose of the uninvolved liver, i.e. up to 24.4 Gy and 27.3 Gy for offline MR-guided PT and MRiPT compared to CBCT-guided PT, respectively. Considering Child-Pugh score increase as endpoint, NTCP of the uninvolved liver was significantly decreased for MRiPT compared to CBCT-guided PT (up to 48.4%,p < 0.01), offline MR-guided PT (up to 12.9%,p < 0.01) and MR-linac (up to 30.8%,p < 0.05). Target underdose was possible in the absence of MRI-guidance (D90 reduction up to 4.2 Gy in 20% of cases). In conclusion, MRiPT has the potential to significantly reduce healthy liver toxicities in patients with liver tumors. It is superior to other image-guided techniques currently available.

Image-guided Radiotherapy to Manage Respiratory Motion: Lung and Liver

Organ motion as a result of respiratory and cardiac motion poses significant challenges for the accurate delivery of radiotherapy to both the thorax and the upper abdomen. Modern imaging techniques during radiotherapy simulation and delivery now permit better quantification of organ motion, which in turn reduces tumour and organ at risk position uncertainty. These imaging advances, coupled with respiratory correlated radiotherapy delivery techniques, have led to the development of a range of approaches to manage respiratory motion. This review summarises the key strategies of image-guided respiratory motion management with a focus on lung and liver radiotherapy.

Artificial intelligence in radiation oncology

Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is practised. AI platforms excel in recognizing complex patterns in medical data and provide a quantitative, rather than purely qualitative, assessment of clinical conditions. Accordingly, AI could have particularly transformative applications in radiation oncology given the multifaceted and highly technical nature of this field of medicine with a heavy reliance on digital data processing and computer software. Indeed, AI has the potential to improve the accuracy, precision, efficiency and overall quality of radiation therapy for patients with cancer. In this Perspective, we first provide a general description of AI methods, followed by a high-level overview of the radiation therapy workflow with discussion of the implications that AI is likely to have on each step of this process. Finally, we describe the challenges associated with the clinical development and implementation of AI platforms in radiation oncology and provide our perspective on how these platforms might change the roles of radiotherapy medical professionals.