Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Recently, artificial intelligence (AI) has demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review serves to present the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provide a literature summary on the topic. We will also discuss the limitations of these algorithms and propose potential improvements.Comment: 36 pages, 5 Figures, 4 Table

Personalized Pancreatic Tumor Growth Prediction via Group Learning

Tumor growth prediction, a highly challenging task, has long been viewed as a mathematical modeling problem, where the tumor growth pattern is personalized based on imaging and clinical data of a target patient. Though mathematical models yield promising results, their prediction accuracy may be limited by the absence of population trend data and personalized clinical characteristics. In this paper, we propose a statistical group learning approach to predict the tumor growth pattern that incorporates both the population trend and personalized data, in order to discover high-level features from multimodal imaging data. A deep convolutional neural network approach is developed to model the voxel-wise spatio-temporal tumor progression. The deep features are combined with the time intervals and the clinical factors to feed a process of feature selection. Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor. Multimodal imaging data at multiple time points are used in the learning, personalization and inference stages. Our method achieves a Dice coefficient of 86.8% +- 3.6% and RVD of 7.9% +- 5.4% on a pancreatic tumor data set, outperforming the DSC of 84.4% +- 4.0% and RVD 13.9% +- 9.8% obtained by a previous state-of-the-art model-based method

AI-basierte volumetrische Analyse der Lebermetastasenlast bei Patienten mit neuroendokrinen Neoplasmen (NEN)

Background: Quantification of liver tumor load in patients with liver metastases from neuroendocrine neoplasms is essential for therapeutic management. However, accurate measurement of three-dimensional (3D) volumes is time-consuming and difficult to achieve. Even though the common criteria for assessing treatment response have simplified the measurement of liver metastases, the workload of following up patients with neuroendocrine liver metastases (NELMs) remains heavy for radiologists due to their increased morbidity and prolonged survival. Among the many imaging methods, gadoxetic acid (Gd-EOB)-enhanced magnetic resonance imaging (MRI) has shown the highest accuracy. Methods: 3D-volumetric segmentation of NELM and livers were manually performed in 278 Gd-EOB MRI scans from 118 patients. Eighty percent (222 scans) of them were randomly divided into training datasets and the other 20% (56 scans) were internal validation datasets. An additional 33 patients from a different time period, who underwent Gd-EOB MRI at both baseline and 12-month follow-up examinations, were collected for external and clinical validation (n = 66). Model measurement results (NELM volume; hepatic tumor load (HTL)) and the respective absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) for baseline and follow-up-imaging were used and correlated with multidisciplinary cancer conferences (MCC) decisions (treatment success/failure). Three readers manually segmented MRI images of each slice, blinded to clinical data and independently. All images were reviewed by another senior radiologist. Results: The model’s performance showed high accuracy between NELM and liver in both internal and external validation (Matthew’s correlation coefficient (ϕ): 0.76/0.95, 0.80/0.96, respectively). And in internal validation dataset, the group with higher NELM volume (> 16.17 cm3) showed higher ϕ than the group with lower NELM volume (ϕ = 0.80 vs. 0.71; p = 0.0025). In the external validation dataset, all response variables (∆absNELM; ∆absHTL; ∆relNELM; ∆relHTL) reflected significant differences across MCC decision groups (all p < 0.001). The AI model correctly detected the response trend based on ∆relNELM and ∆relHTL in all the 33 MCC patients and showed the optimal discrimination between treatment success and failure at +56.88% and +57.73%, respectively (AUC: 1.000; P < 0.001). Conclusions: The created AI-based segmentation model performed well in the three-dimensional quantification of NELMs and HTL in Gd-EOB-MRI. Moreover, the model showed good agreement with the evaluation of treatment response of the MCC’s decision.Hintergrund: Die Quantifizierung der Lebertumorlast bei Patienten mit Lebermetastasen von neuroendokrinen Neoplasien ist für die Behandlung unerlässlich. Eine genaue Messung des dreidimensionalen (3D) Volumens ist jedoch zeitaufwändig und schwer zu erreichen. Obwohl standardisierte Kriterien für die Beurteilung des Ansprechens auf die Behandlung die Messung von Lebermetastasen vereinfacht haben, bleibt die Arbeitsbelastung für Radiologen bei der Nachbeobachtung von Patienten mit neuroendokrinen Lebermetastasen (NELMs) aufgrund der höheren Fallzahlen durch erhöhte Morbidität und verlängerter Überlebenszeit hoch. Unter den zahlreichen bildgebenden Verfahren hat die Gadoxetsäure (Gd-EOB)-verstärkte Magnetresonanztomographie (MRT) die höchste Genauigkeit gezeigt. Methoden: Manuelle 3D-Segmentierungen von NELM und Lebern wurden in 278 Gd-EOB-MRT-Scans von 118 Patienten durchgeführt. 80% (222 Scans) davon wurden nach dem Zufallsprinzip in den Trainingsdatensatz eingeteilt, die übrigen 20% (56 Scans) waren interne Validierungsdatensätze. Zur externen und klinischen Validierung (n = 66) wurden weitere 33 Patienten aus einer späteren Zeitspanne des Multidisziplinäre Krebskonferenzen (MCC) erfasst, welche sich sowohl bei der Erstuntersuchung als auch bei der Nachuntersuchung nach 12 Monaten einer Gd-EOB-MRT unterzogen hatten. Die Messergebnisse des Modells (NELM-Volumen; hepatische Tumorlast (HTL)) mit den entsprechenden absoluten (ΔabsNELM; ΔabsHTL) und relativen Veränderungen (ΔrelNELM; ΔrelHTL) bei der Erstuntersuchung und der Nachuntersuchung wurden zum Vergleich mit MCC-Entscheidungen (Behandlungserfolg/-versagen) herangezogen. Drei Leser segmentierten die MRT-Bilder jeder Schicht manuell, geblindet und unabhängig. Alle Bilder wurden von einem weiteren Radiologen überprüft. Ergebnisse: Die Leistung des Modells zeigte sowohl bei der internen als auch bei der externen Validierung eine hohe Genauigkeit zwischen NELM und Leber (Matthew's Korrelationskoeffizient (ϕ): 0,76/0,95 bzw. 0,80/0,96). Und im internen Validierungsdatensatz zeigte die Gruppe mit höherem NELM-Volumen (> 16,17 cm3) einen höheren ϕ als die Gruppe mit geringerem NELM-Volumen (ϕ = 0,80 vs. 0,71; p = 0,0025). Im externen Validierungsdatensatz wiesen alle Antwortvariablen (∆absNELM; ∆absHTL; ∆relNELM; ∆relHTL) signifikante Unterschiede zwischen den MCC-Entscheidungsgruppen auf (alle p < 0,001). Das KI-Modell erkannte das Therapieansprechen auf der Grundlage von ∆relNELM und ∆relHTL bei allen 33 MCC-Patienten korrekt und zeigte bei +56,88% bzw. +57,73% eine optimale Unterscheidung zwischen Behandlungserfolg und -versagen (AUC: 1,000; P < 0,001). Schlussfolgerungen: Das Modell zeigte eine hohe Genauigkeit bei der dreidimensionalen Quantifizierung des NELMs-Volumens und der HTL in der Gd-EOB-MRT. Darüber hinaus zeigte das Modell eine gute Übereinstimmung bei der Bewertung des Ansprechens auf die Behandlung mit der Entscheidung des Tumorboards

Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications

As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization

Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice