Targeted delivery of anti-miRNA21 sensitizes PD-L1high tumor to immunotherapy by promoting immunogenic cell death.

Rationale: Growing evidence has demonstrated that miRNA-21 (miR-21) upregulation is closely associated with tumor pathogenesis. However, the mechanisms by which miR-21 inhibition modulates the immunosuppressive tumor microenvironment (TME) and improves tumor sensitivity to immune checkpoint blockade therapies remain largely unexplored. In this study, we demonstrate the precise delivery of anti-miR-21 using a PD-L1-targeting peptide conjugate (P21) to the PD-L1high TME. Methods: Investigating miR-21 inhibition mechanisms involved conducting quantitative real-time PCR, western blot, flow cytometry, and confocal microscopy analyses. The antitumor efficacy and immune profile of P21 monotherapy, or combined with anti-PD-L1 immune checkpoint inhibitors, were assessed in mouse models bearing CT26.CL25 tumors and 4T1 breast cancer. Results Inhibition of oncogenic miR-21 in cancer cells by P21 efficiently activates tumor suppressor genes, inducing autophagy and endoplasmic reticulum stress. Subsequent cell-death-associated immune activation (immunogenic cell death) is initiated via the release of damage-associated molecular patterns. The in vivo results also illustrated that the immunogenic cell death triggered by P21 could effectively sensitize the immunosuppressive TME. That is, P21 enhances CD8+ T cell infiltration in tumor tissues by conferring immunogenicity to dying cancer cells and promoting dendritic cell maturation. Meanwhile, combining P21 with an anti-PD-L1 immune checkpoint inhibitor elicits a highly potent antitumor effect in a CT26.CL25 tumor-bearing mouse model and 4T1 metastatic tumor model. Conclusions: Collectively, we have clarified a miR-21-related immunogenic cell death mechanism through the precise delivery of anti-miR-21 to the PD-L1high TME. These findings highlight the potential of miR-21 as a target for immunotherapeutic interventions.

Simultaneous Protein Colorful Imaging via Raman Signal Classification.

Protein imaging aids diagnosis and drug development by revealing protein-drug interactions or protein levels. However, the challenges of imaging multiple proteins, reduced sensitivity, and high reliance on specific protein properties such as Raman peaks or refractive index hinder the understanding. Here, we introduce multiprotein colorful imaging through Raman signal classification. Our method utilized machine learning-assisted classification of Raman signals, which are the distinctive features of label-free proteins. As a result, three types of proteins could be imaged simultaneously. In addition, we could quantify individual proteins from a mixture of multiple proteins over a wide detection range (10 fg/mL-1 µg/mL). These results showed a 1000-fold improvement in sensitivity and a 30-fold increase in the upper limit of detection compared to existing methods. These advances will enhance our understanding of biology and facilitate the development of disease diagnoses and treatments.

Charge-Based Isolation of Extracellular Vesicles from Human Plasma.

Extracellular vesicles (EVs) have garnered significant attention due to their potential applications in disease diagnostics and management. However, the process of isolating EVs, primarily from blood samples, is still suboptimal. This is mainly attributed to the abundant nature of soluble proteins and lipoproteins, which are often separated together with EVs in the end products of conventional isolation methods. As such, we devise a single-step charge-based EV isolation method by utilizing positively charged beads to selectively remove negatively charged major impurities from human plasma via electrostatic interaction. By carefully controlling the buffer pH, we successfully collected EVs from undesired plasma components with superior purity and yield compared to conventional EV collection methods. Moreover, the developed process is rapid, taking only about 20 min for overall EV isolation. The charge-based isolation can ultimately benefit the EV-based liquid biopsy field for the early diagnosis of various diseases.

Dose Calculation Accuracy of Beam Models in RadCalc for a 1.5 T MR-Linac.

The purpose of this study is to evaluate RadCalc, an independent dose verification software, for patient-specific quality assurance (PSQA) in online adaptive planning with a magnetic resonance linear accelerator (MR-linac) of a 1.5 T. Version 7.1.4 of RadCalc to introduce the capability to establish a beam model that incorporates MR field characteristics. A total of six models were established, with one using manufacturer-provided data and the others differing in percentage depth dose (PDD) data sources. Overall, two models utilized PDD data from the treatment planning system (TPS), and three used commissioned PDD data from gantry angles of 0° and 270°. Simple tests on a virtual water phantom assessed dose-calculation accuracy, revealing percentage differences ranging from -0.5% to -20.6%. Excluding models with significant differences, clinical tests on 575 adaptive plans (prostate, liver, and breast) showed percentage differences of -0.51%, 1.12%, and 4.10%, respectively. The doses calculated using RadCalc demonstrated similar trends to those of the PSQA-based measurements. The newly released version of RadCalc enables beam modeling that considers the characteristics of the 1.5 T magnetic field. The accuracy of the software in calculating doses at 1.5 T magnetic fields has been verified, thereby making it a reliable and effective tool for PSQA in adaptive plans.

Increased intracellular diffusivity of macromolecules within a mammalian cell by low-intensity pulsed ultrasound.

Whilst a number of studies have demonstrated that low-intensity pulsed ultrasound (LIPUS) is a promising therapeutic ultrasound technique that can be used for delivering mild mechanical stimuli to target tissue non-invasively, the underlying biophysical mechanisms still remain unclear. Most mechanism studies have focused explicitly on the effects of LIPUS on the cell membrane and mechanosensitive receptors. In the present study, we propose an additional mechanism by which LIPUS propagation through living cells may directly impact intracellular dynamics, particularly the diffusion transport of biomolecules. To support our hypothesis, human epithelial-like cells (SaOS-2 and HeLa) seeded on a confocal dish placed on a microscope stage were exposed to LIPUS with various exposure conditions (ultrasound frequencies of 0.5, 1 and 3 MHz, peak acoustic pressure of 200 and 400 kPa, a pulse repetition frequency of 1 kHz and a 20 % duty cycle), and the diffusivities of various sizes of biomolecules in the cytoplasm area were measured using fluorescence recovery after photobleaching (FRAP). Furthermore, giant unilamellar vesicles (GUVs) filled with macromolecules were used to examine the physical causal relationship between LIPUS and molecular diffusion changes. Nucleocytoplasmic transport coefficients were also measured by modified FRAP that bleaches the whole cell nuclear region. Extracellular signal-regulated kinases (ERK) activity (the phosphorylation dynamics) was monitored using fluorescence resonance energy transfer (FRET) microscopy. All the measurements were taken during, before and after the LIPUS exposure. Our experimental results clearly showed that the diffusion coefficients of macromolecules within the cell increased with acoustic pressure by 12.1 to 33.5 % during the sonication, and the increments were proportional to their molecular sizes regardless of the ultrasound frequency used. This observation in living cells was consistent with the GUVs exposed to the LIPUS, which indicated that the diffusivity increase was a passive physical response to the acoustic energy of LIPUS. Under the 1 MHz LIPUS exposure with 400 kPa, the passive nucleocytoplasmic transport of enhanced green fluorescent protein (EGFP) was accelerated by 21.4 %. With the same LIPUS exposure condition, both the diffusivity and phosphorylation of ERK induced by EGF treatment were significantly elevated simultaneously, which implied that LIPUS could also modify the kinase kinetics in the signal transduction process. Taken together, this study is the first attempt to uncover the physical link between LIPUS and the dynamics of intracellular macromolecules and related biological processes that LIPUS can possibly increase the diffusivity of intracellular macromolecules, leading to the changes in the basic cellular processes: passive nucleocytoplasmic transport and ERK. Our findings can provide a novel perspective that the mechanotransduction process that the intracellular region, in addition to the cell membrane, can convert the acoustic stimuli of LIPUS to biochemical signals.

Metacognitive Awareness and the Hot Hand: When Winning, No Amount of Awareness Will Have Strong Believers Avoid the Heuristic.

In some instances, such as in sports, individuals will cheer on the player with the "hot hand". But is the hot hand phenomenon a fallacy? The current research investigated (1) whether the hot hand fallacy (HHF) was related to risky decisions during a gambling scenario, and (2) whether metacognitive awareness might be related to optimal decisions. After measuring for baseline tendencies of using the hot hand heuristic, participants were presented with a series of prior card gambling results that included either winning streaks or losing streaks and asked to choose one of two cards: a good card or a bad card. In addition, we examined whether high metacognitive awareness-as measured by the ability to discriminate between correct and incorrect responses-would be negatively related to the risky decisions induced by the hot hand heuristic. The results showed that our predictions were partially supported. For winning streaks, individuals who had a weak tendency for using the heuristic exhibited fewer risky decisions with higher metacognitive awareness. However, those with a strong baseline tendency for using the hot hand showed no sign of decrease with metacognitive awareness. On the whole, the complex data suggest that further research on the HHF would be helpful for implementing novel ways of avoiding the fallacy, if needed.

Plasma Exosome Analysis for Protein Mutation Identification Using a Combination of Raman Spectroscopy and Deep Learning.

Protein mutation detection using liquid biopsy can be simply performed periodically, making it easy to detect the occurrence of newly emerging mutations rapidly. However, it has low diagnostic accuracy since there are more normal proteins than mutated proteins in body fluids. To increase the diagnostic accuracy, we analyzed plasma exosomes using nanoplasmonic spectra and deep learning. Exosomes, a promising biomarker, are abundant in plasma and stably carry intact proteins originating from mother cells. However, the mutated exosomal proteins cannot be detected sensitively because of the subtle changes in their structure. Therefore, we obtained Raman spectra that provide molecular information about structural changes in mutated proteins. To extract the unique features of the protein from complex Raman spectra, we developed a deep-learning classification algorithm with two deep-learning models. Consequently, controls with wild-type proteins and patients with mutated proteins were classified with high accuracy. As a proof of concept, we discriminated the lung cancer patients with mutations in the epidermal growth factor receptor (EGFR), L858R, E19del, L858R + T790M, and E19del + T790M, from controls with an accuracy of 0.93. Moreover, the protein mutation status of the patients with primary (E19del, L858R) and secondary (+T790M) mutations was clearly monitored. Overall, our technique is expected to be applied as a novel method for companion diagnostic and treatment monitoring.

Immune checkpoint-targeted drug conjugates: A promising tool for remodeling tumor immune microenvironment.

Immune checkpoint blockade (ICB) therapy has shown remarkable outcomes along with multiple cases of complete regression in clinical practice. But unfortunately, most patients who have an immunosuppressive tumor immune microenvironment (TIME) respond poorly to these therapies. To improve the response rate of the patients, various treatment modalities that can boost cancer immunogenicity and remove immune tolerance have been combined with ICB therapies. However, the systemic administration of multiple immunotherapeutic agents can potentially cause severe off-target toxicities and immune-related adverse events, diminishing antitumor immunity and increasing the risk of additional complications. To address these problems, Immune Checkpoint-Targeted Drug Conjugates (IDCs) have been widely investigated for their ability to offer distinct advantages in remodeling the TIME for cancer immunotherapy. IDCs, consisting of immune checkpoint-targeting moieties, cleavable linkers, and payloads of immunotherapeutic agents, have a similar structure to conventional antibody-drug conjugates (ADCs) but target and block the immune checkpoint receptors, and then release the payloads conjugated through cleavable linkers. These unique mechanisms of IDCs prompt an immune-responsive TIME by modulating the multiple steps related to the cancer-immunity cycle, ultimately leading to tumor eradication. This review outlines the mode of action and advantages of IDCs. In addition, various IDCs for combinational immunotherapy are reviewed. Finally, the potential and challenges of IDCs for clinical translation are discussed.

Permanent Anticoagulation Blood-Vessel by Mezzo-Sized Double Re-Entrant Structure.

Having a permanent omniphobicity on the inner surface of the tube can bring enormous advantages, such as reducing resistance and avoiding precipitation during mass transfer. For example, such a tube can prevent blood clotting when delivering blood composed of complex hydrophilic and lipophilic compounds. However, it is very challenging to fabricate micro and nanostructures inside a tube. To overcome these, a wearability and deformation-free structural omniphobic surface is fabricated. The omniphobic surface can repel liquids by its "air-spring" under the structure, regardless of surface tension. Furthermore, it is not lost an omniphobicity under physical deformation like curved or twisted. By using these properties, omniphobic structures on the inner wall of the tube by the "roll-up" method are fabricated. Fabricated omniphobic tubes still repels liquids, even complex liquids like blood. According to the ex vivo blood tests for medical usage, the tube can reduce thrombus formation by 99%, like the heparin-coated tube. So, the surface will soon replace typical coating-based medical surfaces or anticoagulation blood vessels.

Artificial Intelligence-Based Major Depressive Disorder (MDD) Diagnosis Using Raman Spectroscopic Features of Plasma Exosomes.

In vitro diagnosis using biomarkers for major depressive disorder (MDD) can offer considerable advantages in overcoming the lack of objective tests for depression and treating more patients. Plasma exosomes can be novel biomarkers for MDD based on their ability to pass through the blood-brain barrier and offer brain-related information. Here, we demonstrate a novel and precise MDD diagnosis using deep learning analysis and surface-enhanced Raman spectroscopy (SERS) of plasma exosomes. Our system is implemented based on 28,000 exosome SERS signals, providing sample-wise prediction results. Notably, this approach shows remarkable performance in predicting 70 test samples unused in the training step, with an area under the curve (AUC) of 0.939, a sensitivity of 91.4%, and a specificity of 88.6%. In addition, we confirm that the diagnostic scores were correlated with the degree of depression. These results show the utility of exosomes as novel biomarkers for MDD diagnosis and suggest a novel approach for prescreening techniques for psychiatric disorders.

Single test-based diagnosis of multiple cancer types using Exosome-SERS-AI for early stage cancers.

Early cancer detection has significant clinical value, but there remains no single method that can comprehensively identify multiple types of early-stage cancer. Here, we report the diagnostic accuracy of simultaneous detection of 6 types of early-stage cancers (lung, breast, colon, liver, pancreas, and stomach) by analyzing surface-enhanced Raman spectroscopy profiles of exosomes using artificial intelligence in a retrospective study design. It includes classification models that recognize signal patterns of plasma exosomes to identify both their presence and tissues of origin. Using 520 test samples, our system identified cancer presence with an area under the curve value of 0.970. Moreover, the system classified the tumor organ type of 278 early-stage cancer patients with a mean area under the curve of 0.945. The final integrated decision model showed a sensitivity of 90.2% at a specificity of 94.4% while predicting the tumor organ of 72% of positive patients. Since our method utilizes a non-specific analysis of Raman signatures, its diagnostic scope could potentially be expanded to include other diseases.

Molecularly engineered siRNA conjugates for tumor-targeted RNAi therapy.

RNA interference (RNAi) is a major cellular mechanism regulating gene expression in which short double-stranded RNA molecules called small interfering RNA (siRNA) mediate sequence-specific mRNA degradation. RNAi technology has recently emerged as a promising therapeutic platform for the effective treatment of various diseases caused by inappropriate gene activity, such as cancer. However, the clinical translation of siRNA therapeutics has been hampered by the major hurdles associated with biological instability and limited delivery efficiency. Based on the various efforts, recent siRNA delivery strategies using cationic lipids and polymers allowed to enhance pharmacokinetics and delivery efficiency, resulting in potent and liver-targeted RNAi therapy. However, non-specific protein adsorption, high liver accumulation, and severe toxicity of cationic nanocarriers still limit the possibility of transfer of siRNA therapeutics from the laboratory to the clinic. One of the promising delivery strategies to overcome the limitations of siRNA therapeutics is carrier-free bioconjugation which is chemically modified and connected with biocompatible molecules such as lipids, peptides, antibodies, aptamers, and polymers. These molecularly engineered siRNA conjugates can be utilized for RNAi delivery to tissues beyond the liver, providing opportunities for clinical translation. This review focused on introducing the recent progress in molecularly engineered siRNA conjugates and their applications toward overcoming the limitations of siRNA for tumor-targeted delivery and therapy.

Impact of Denoising on Deep-Learning-Based Automatic Segmentation Framework for Breast Cancer Radiotherapy Planning.

Objective: This study aimed to investigate the segmentation accuracy of organs at risk (OARs) when denoised computed tomography (CT) images are used as input data for a deep-learning-based auto-segmentation framework. Methods: We used non-contrast enhanced planning CT scans from 40 patients with breast cancer. The heart, lungs, esophagus, spinal cord, and liver were manually delineated by two experienced radiation oncologists in a double-blind manner. The denoised CT images were used as input data for the AccuContourTM segmentation software to increase the signal difference between structures of interest and unwanted noise in non-contrast CT. The accuracy of the segmentation was assessed using the Dice similarity coefficient (DSC), and the results were compared with those of conventional deep-learning-based auto-segmentation without denoising. Results: The average DSC outcomes were higher than 0.80 for all OARs except for the esophagus. AccuContourTM-based and denoising-based auto-segmentation demonstrated comparable performance for the lungs and spinal cord but showed limited performance for the esophagus. Denoising-based auto-segmentation for the liver was minimal but had statistically significantly better DSC than AccuContourTM-based auto-segmentation (p < 0.05). Conclusions: Denoising-based auto-segmentation demonstrated satisfactory performance in automatic liver segmentation from non-contrast enhanced CT scans. Further external validation studies with larger cohorts are needed to verify the usefulness of denoising-based auto-segmentation.

Compact bunker shielding assessment for 1.5 T MR-Linac.

This study evaluated the effect of the 1.5 T magnetic field of the magnetic resonance-guided linear accelerator (MR-Linac) on the radiation leakage doses penetrating the bunker radiation shielding wall. The evaluated 1.5 T MR-Linac Unity system has a bunker of the minimum recommended size. Unlike a conventional Linac, both primary beam transmission and secondary beam leakage were considered independently in the design and defined at the machine boundary away from the isocenter. Moreover, additional shielding was designed considering the numerous ducts between the treatment room and other rooms. The Linac shielding was evaluated by measuring the leakage doses at several locations. The intrinsic vibration and magnetic field were inspected at the proposed isocenter of the system. For verification, leakage doses were measured before and after applying the magnetic field. The intrinsic vibration and magnetic field readings were below the permitted limit. The leakage dose (0.05-12.2 µSv/week) also complied with internationally stipulated limits. The special shielding achieved a five-fold reduction in leakage dose. Applying the magnetic field increased the leakage dose by 0.12 to 4.56 µSv/week in several measurement points, although these values fall within experimental uncertainty. Thus, the effect of the magnetic field on the leakage dose could not be ascertained.

Mutual Information-Based Non-Local Total Variation Denoiser for Low-Dose Cone-Beam Computed Tomography.

Conventional non-local total variation (NLTV) approaches use the weight of a non-local means (NLM) filter, which degrades performance in low-dose cone-beam computed tomography (CBCT) images generated with a low milliampere-seconds (mAs) parameter value because a local patch used to determine the pixel weights comprises noisy-damaged pixels that reduce the similarity between corresponding patches. In this paper, we propose a novel type of NLTV based on a combination of mutual information (MI): MI-NLTV. It is based on a statistical measure for a similarity calculation between the corresponding bins of non-local patches vs. a reference patch. The weight is determined in terms of a statistical measure comprising the MI value between corresponding non-local patches and the reference-patch entropy. The MI-NLTV denoising process is applied to CBCT images generated by the analytical reconstruction algorithm using a ray-driven backprojector (RDB). The MI-NLTV objective function is minimized based on the steepest gradient descent optimization to augment the difference between a real structure and noise, cleaning noisy pixels without significant loss of the fine structure and details that remain in the reconstructed images. The proposed method was evaluated using patient data and actual phantom measurement data acquired with lower mAs. The results show that integrating the RDB further enhances the MI-NLTV denoising-based analytical reconstruction algorithm to achieve a higher CBCT image quality when compared with those generated by NLTV denoising-based approach, with an average of 15.97% higher contrast-to-noise ratio, 2.67% lower root mean square error, 0.12% lower spatial non-uniformity, 1.14% higher correlation, and an average of 18.11% higher detectability index. These quantitative results indicate that the incorporation of MI makes the NLTV more stable and robust than the conventional NLM filter for low-dose CBCT imaging. In addition, achieving clinically acceptable CBCT image quality despite low-mAs projection acquisition can reduce the burden on common online CBCT imaging, improving patient safety throughout the course of radiotherapy.

GCC2 as a New Early Diagnostic Biomarker for Non-Small Cell Lung Cancer.

No specific markers have been identified to detect non-small cell lung cancer (NSCLC) cell-derived exosomes circulating in the blood. Here, we report a new biomarker that distinguishes between cancer and non-cancer cell-derived exosomes. Exosomes isolated from patient plasmas at various pathological stages of NSCLC, NSCLC cell lines, and human pulmonary alveolar epithelial cells isolated using size exclusion chromatography were characterized. The GRIP and coiled-coil domain-containing 2 (GCC2) protein, involved in endosome-to-Golgi transport, was identified by proteomics analysis of NSCLC cell line-derived exosomes. GCC2 protein levels in the exosomes derived from early-stage NSCLC patients were higher than those from healthy controls. Receiver operating characteristic curve analysis revealed the diagnostic sensitivity and specificity of exosomal GCC2 to be 90% and 75%, respectively. A high area under the curve, 0.844, confirmed that GCC2 levels could effectively distinguish between the exosomes. These results demonstrate GCC2 as a promising early diagnostic biomarker for NSCLC.

Recent Advances in Exosome-Based Drug Delivery for Cancer Therapy.

Exosomes are a class of extracellular vesicles, with a size of about 100 nm, secreted by most cells and carrying various bioactive molecules such as nucleic acids, proteins, and lipids, and reflect the biological status of parent cells. Exosomes have natural advantages such as high biocompatibility and low immunogenicity for efficient delivery of therapeutic agents such as chemotherapeutic drugs, nucleic acids, and proteins. In this review, we introduce the latest explorations of exosome-based drug delivery systems for cancer therapy, with particular focus on the targeted delivery of various types of cargoes.

Tumor microenvironmental cytokines bound to cancer exosomes determine uptake by cytokine receptor-expressing cells and biodistribution.

Metastatic spread of a cancer to secondary sites is a coordinated, non-random process. Cancer cell-secreted vesicles, especially exosomes, have recently been implicated in the guidance of metastatic dissemination, with specific surface composition determining some aspects of organ-specific localization. Nevertheless, whether the tumor microenvironment influences exosome biodistribution has yet to be investigated. Here, we show that microenvironmental cytokines, particularly CCL2, decorate cancer exosomes via binding to surface glycosaminoglycan side chains of proteoglycans, causing exosome accumulation in specific cell subsets and organs. Exosome retention results in changes in the immune landscape within these organs, coupled with a higher metastatic burden. Strikingly, CCL2-decorated exosomes are directed to a subset of cells that express the CCL2 receptor CCR2, demonstrating that exosome-bound cytokines are a crucial determinant of exosome-cell interactions. In addition to the finding that cytokine-conjugated exosomes are detected in the blood of cancer patients, we discovered that healthy subjects derived exosomes are also associated with cytokines. Although displaying a different profile from exosomes isolated from cancer patients, it further indicates that specific combinations of cytokines bound to exosomes could likewise affect other physiological and disease settings.

Patient-Specific Quality Assurance Using a 3D-Printed Chest Phantom for Intraoperative Radiotherapy in Breast Cancer.

This study aims to confirm the usefulness of patient-specific quality assurance (PSQA) using three-dimensional (3D)-printed phantoms in ensuring the stability of IORT and the precision of the treatment administered. In this study, five patient-specific chest phantoms were fabricated using a 3D printer such that they were dosimetrically equivalent to the chests of actual patients in terms of organ density and shape around the given target, where a spherical applicator was inserted for breast IORT treatment via the INTRABEAM™ system. Models of lungs and soft tissue were fabricated by applying infill ratios corresponding to the mean Hounsfield unit (HU) values calculated from CT scans of the patients. The two models were then assembled into one. A 3D-printed water-equivalent phantom was also fabricated to verify the vendor-provided depth dose curve. Pieces of an EBT3 film were inserted into the 3D-printed customized phantoms to measure the doses. A 10 Gy prescription dose based on the surface of the spherical applicator was delivered and measured through EBT3 films parallel and perpendicular to the axis of the beam. The shapes of the phantoms, CT values, and absorbed doses were compared between the expected and printed ones. The morphological agreement among the five patient-specific 3D chest phantoms was assessed. The mean differences in terms of HU between the patients and the phantoms was 2.2 HU for soft tissue and -26.2 HU for the lungs. The dose irradiated on the surface of the spherical applicator yielded a percent error of -2.16% ± 3.91% between the measured and prescribed doses. In a depth dose comparison using a 3D-printed water phantom, the uncertainty in the measurements based on the EBT3 film decreased as the depth increased beyond 5 mm, and a good agreement in terms of the absolute dose was noted between the EBT3 film and the vendor data. These results demonstrate the applicability of the 3D-printed chest phantom for PSQA in breast IORT. This enhanced precision offers new opportunities for advancements in IORT.

Clinical Implications of Geometric and Dosimetric Uncertainties of Inter- and Intra-Fractional Movement during Volumetric Modulated Arc Therapy for Breast Cancer Patients.

With the introduction of modern sophisticated radiotherapy (RT) techniques, the significance of accuracy has increased considerably. This study evaluated the necessity of pre-treatment and intra-fractional cone-beam computed tomography (CBCT) by analyzing inter- and intra-fractional CBCT images of breast cancer patients receiving RT. From 57 patients, 1206 pre-treatment CBCT and 1067 intra-fractional CBCT images were collected. Geometric movements of patients were measured quantitively in both inter- and intra-fractional CBCT, and changes in dosimetric parameters were evaluated in selected patients with extreme intra-fractional movement. For right-sided breast cancer patients, left-sided breast cancer patients treated using deep-inspiration breath hold (DIBH), and left-sided breast cancer patients treated using continuous positive airway pressure (CPAP), median inter-fractional deviations were 0.53 (range 0.06-2.98) cm, 0.66 (range 0.08-4.41) cm, and 0.69 (range 0.04-3.80) cm, and median intra-fractional deviations were 0.14 (range 0.00-0.62) cm, 0.23 (range 0.02-0.96) cm, and 0.24 (0.00-1.15) cm, respectively. Modified plans reflecting large changes in intra-fractional position in 10 selected cases revealed insufficient target coverage in seven cases and more than 20-fold increase in the volume of heart receiving at least 25 Gy in two cases. Intra-fractional verification, as well as pre-treatment verification, might be considered in patients using DIBH or CPAP.