Tumor Position Research Articles

Correlation analysis of Hashimoto’s thyroiditis with papillary thyroid carcinoma occurrence and its central lymph node metastasis: a single center experience

PurposeThis study investigates the clinicopathological characteristics of papillary thyroid carcinoma (PTC) with coexisting Hashimoto’s thyroiditis (HT) and further explores the risk factors for central lymph node metastasis (CLNM) in PTC.MethodA retrospective analysis was conducted on 415 PTC patients who underwent surgical treatment for thyroid cancer at the First Affiliated Hospital of Anhui University of Chinese Medicine from 2016 to 2022. Clinicopathological features were compared between PTC patients with and without HT. Univariate and multivariate logistic regression were used to analyze the risk factors of CLNM.ResultThe PTC+HT group had a higher proportion of female patients (85.5%) than the PTC group (P<0.05). Univariate analysis revealed no statistically significant difference between the two groups in eight aspects (all P>0.05). Multivariate analysis showed that HT was positively associated with the total number of central lymph node (CLN) dissected, Thyroid-stimulating hormone (TSH), Thyroid peroxidase antibody (TPOAb), and Thyroglobulin antibodies (TgAb), while identified as a protective factor against invasion with an odds ratio of 0.422 (95%CI 0.209-0.853, P=0.016). Through univariate and multivariate logistic regression, we proved that tumor position, Capsule + Extrathyroidal extension (ETE), multifocal tumors, and the total number of CLN dissected were independent risk factors for CLNM. Multiple linear regression analysis told us that invasion (β= 0.093, p=0.048) had a positively predictive impact on CLN positive rate.ConclusionFemale PTC patients are more prone to concurrent HT, which elevates the levels of TSH, TPOAb, and TgAb. HT not only promotes the longitudinal growth of nodules and PTC development, but also reduces the risk of invasion and CLNM. Therefore, we posit that the impact of HT on PTC patients is a “double-edged sword”. Isthmus, Capsule + ETE, multifocality, age < 55 years old, and male are high-risk factors for CLNM in PTC, while HT is regarded as a protective factor. Capsule + ETE is the primary risk factor affecting the CLN positive rate.

Deep Learning-Assisted Computer-Aided Diagnosis System for Early Detection of Lung Cancer.

The largest cause of cancer-related fatalities worldwide is lung cancer. The dimensions and positioning of the primary tumor, the presence of lesions, the type of lung cancer like malignant or benign, and the good mental health diagnosis all play a part in the diagnosis of the disease. Three processes should be used by standard computer-assisted diagnosis (CAD) systems to detect lung cancer: preprocessing, feature extraction, and classification. Fast nonlocal means filter is first used for preprocessing (FNLM). The lung pictures are processed using the Binary Grasshopper Optimization Algorithm (BGOA) to extract the features. The 10 levels of the neural network architecture which automatically gathers data and categorizes them are added to the current study's suggested model, which subtracts the five levels of Imagenet. Using the same Modèle dataset, the proposed model was compared to deep learning techniques. In terms of accuracy and sensitivity, the suggested model performed better than the existing techniques (99.53% accuracy and 98.95% sensitivity). The effectiveness of the suggested strategy is superior to that of alternative methods when it is near the true positive values at the ROC curve.

A real-time tumor position prediction based multi-dimensional respiratory motion compensation puncture method

Objective.This study proposes a real-time tumor position prediction-based multi-dimensional respiratory motion compensation puncture method to accurately track real-time lung tumors and achieve precise needle puncture.Approach.A hybrid model framework integrating prediction and correlation models is developed to enable real-time tumor localization. A Long Short-Term Memory neural network with bidirectional and attention modules (Bi-LSTM-ATT) is employed for predicting external respiratory signals. Subsequently, a backpropagation neural network is constructed to correlate these signals with tumor positions. Tumor trajectory decomposition and the determination of an optimal puncture window based on multiple criteria ensure accurate needle puncture.Main results.When the delay time of Bi-LSTM-ATT model is 500 ms, its RMSE, MAE, andR2are 0.0482 mm, 0.0414 mm, and 97.90% respectively. The correlation model locates lung tumors in 10 cases with a target registration error within 0.74 mm. The proposed puncture method achieves a puncture error ranging from 1.00 mm to 1.32 mm, with an average error of 1.2 mm.Significance.The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for percutaneous biopsy procedures within the lung.Clinical trial registrationClinical trial registration was not required for this research.

CDCG-UNet: Chaotic Optimization Assisted Brain Tumor Segmentation Based on Dilated Channel Gate Attention U-Net Model.

Brain tumours are one of the most deadly and noticeable types of cancer, affecting both children and adults. One of the major drawbacks in brain tumour identification is the late diagnosis and high cost of brain tumour-detecting devices. Most existing approaches use ML algorithms to address problems, but they have drawbacks such as low accuracy, high loss, and high computing cost. To address these challenges, a novel U-Net model for tumour segmentation in magnetic resonance images(MRI) is proposed. Initially, images are claimed from the dataset and pre-processed with the Probabilistic Hybrid Wiener filter (PHWF) to remove unwanted noise and improve image quality. To reduce model complexity, the pre-processed images are submitted to a feature extraction procedure known as 3D Convolutional Vision Transformer (3D-VT). To perform the segmentation approach usingchaotic optimization assisted Dilated Channel Gate attention U-Net (CDCG-UNet)model to segment brain tumour regions effectively. The proposed approach segments tumour portions as whole tumour (WT), tumour Core (TC), and Enhancing Tumour (ET) positions. The optimization loss function can be performed using the Chaotic Harris Shrinking Spiral optimization algorithm (CHSOA). The proposed CDCG-UNet model is evaluated with three datasets: BRATS 2021, BRATS 2020, and BRATS 2023. For the BRATS 2021 dataset, the proposed CDCG-UNet model obtained a dice score of 0.972 for ET, 0.987 for CT, and 0.98 for WT. For the BRATS 2020 dataset, the proposed CDCG-UNet model produced a dice score of 98.87% for ET, 98.67% for CT, and 99.1% for WT. The CDCG-UNet model is further evaluated using the BRATS 2023 dataset, which yields 98.42% for ET, 98.08% for CT, and 99.3% for WT.

Feasibility study of using next-generation reservoir computing (NG-RC) model to estimate liver tumor motion from external breathing signals.

Respiratory motion is a challenge for accurate radiotherapy that may be mitigated by real-time tracking. Commercial tracking systems utilize a hybrid external-internal correlation model (ECM), integrating continuous external breathing monitoring with sparse X-ray imaging of the internal tumor position. This study investigates the feasibility of using the next generation reservoir computing (NG-RC) model as a hybrid ECM to transform measured external motions into estimated 3D internal motions. The NG-RC model utilizes the nonlinear vector autoregressive (NVAR) machine to account for the hysteresis or phase differences between external and internal motions. The datasets used to evaluate the efficacy of the NG-RC model include 57 motion traces from the CyberKnife system. The datasets were divided into three regions (central, lower, and upper livers) and three motion patterns. These patterns include linear and nonlinear motion patterns (Group A), hysteresis motion patterns (Group B), and all motion patterns (Group C). Moreover, various updating techniques were examined, such as continuously updating the NG-RC model using the first-in-first-out (FIFO) approach and sampling the internal tumor position every 0 s (strategy A), 60 s (strategy B), 30 s (strategy C), and 50 s (strategy D). The NG-RC model combined with strategy C resulted in better estimation accuracy than the reported CyberKnife cases (Wilcoxon signed rank p < 0.05). For linear and nonlinear motion patterns, the 3D radial estimation accuracy (mean ± SD) using the NG-RC model combined with strategy C and the CyberKnife system was 1.20 ± 0.78 and 1.1 ± 0.20 mm in the central liver, 0.66 ± 0.25 and 1.49 ± 0.50 mm in the lower liver, and 1.73 ± 0.86 and 1.61 ± 0.42 mm in the upper liver. For hysteresis motion patterns, the corresponding values were 1.13 ± 0.37 and 1.45 ± 0.33 mm, 1.43 ± 1.30 and 1.67 ± 0.42 mm, and 1.20 ± 0.68 and 1.46 ± 0.54 mm in the central, lower, and upper livers, respectively. This study proposed a new hybrid correlation model for real-time tumor tracking, which can be used to account for both linear and nonlinear motion patterns, as well as hysteresis motion patterns. Additionally, the NG-RC model required shorter training data sets (15 s) during pre-treatment and short internal motion sampling (every 30 s) during treatment compared to other ECMs.

Influencing Factors on the Registration Accuracy of a Learned Feature Descriptor in Laparoscopic Liver Surgery

Abstract In laparoscopic liver surgery, image-guided navigation systems provide crucial support to surgeons by supplying information about tumor and vessel positions. For this purpose, these information from a preoperative CT or MRI scan is overlaid onto the laparoscopic video. One option is performing a registration of preoperative 3D data and 3D reconstructed laparoscopic data. A robust registration is challenging due to factors like limited field of view, liver deformations, and 3D reconstruction errors. Since in reality various influencing factors always intertwine, it is crucial to analyze their combined effects. This paper assesses registration accuracy under various synthetically simulated influences: patch size, spatial displacement, Gaussian deformations, holes, and downsampling. The objective is to provide insights into the required quality of the intraoperative 3D surface patches. LiverMatch serves as the feature descriptor, and registration employs the RANSAC algorithm. The results of this paper show that ensuring a large field of view of at least 15-20% of the liver surface is necessary, allowing tolerance for less accurate depth estimation.

Super-resolution reconstruction of time-resolved four-dimensional computed tomography (TR-4DCT) with multiple breathing cycles based on TR-4DMRI.

Respiratory motion irregularities in lung cancer patients are common and can be severe during multi-fractional (∼20 mins/fraction) radiotherapy. However, the current clinical standard of motion management is to use a single-breath respiratory-correlated four-dimension computed tomography (RC-4DCT or 4DCT) to estimate tumor motion to delineate the internal tumor volume (ITV), covering the trajectory of tumor motion, as a treatment target. To develop a novel multi-breath time-resolved (TR) 4DCT using the super-resolution reconstruction framework with TR 4D magnetic resonance imaging (TR-4DMRI) as guidance for patient-specific breathing irregularity assessment, overcoming the shortcomings of RC-4DCT, including binning artifacts and single-breath limitations. Six lung cancer patients participated in the IRB-approved protocol study to receive multiple T1w MRI scans, besides an RC-4DCT scan on the simulation day, including 80 low-resolution (lowR: 5×5×5mm3) free-breathing (FB) 3D cine MRFB images in 40s (2Hz) and a high-resolution (highR: 2×2×2mm3) 3D breath-hold (BH) MRBH image for each patient. A CT (1×1×3mm3) image was selected from 10-bin RC-4DCT with minimal binning artifacts and a close diaphragm match (<1cm) to the MRBH image. A mutual-information-based Freeform deformable image registration (DIR) was used to register the CT and MRBH via the opposite directions (namely F1: and F2: ) to establish CT-MR voxel correspondences. An intensity-based enhanced Demons DIR was then applied for , in which the original MRBH was used in D1: , while the deformed MRBH was used in D2: . The deformation vector fields (DVFs) obtained from each DIR were composed to apply to the deformed CT (D1) and original CT (D2) to reconstruct TR-4DCT images. A digital 4D-XCAT phantom at the end of inhalation (EOI) and end of exhalation (EOE) with 2.5cm diaphragmatic motion and three spherical targets (ϕ=2, 3, 4cm) were first tested to reconstruct TR-4DCT. For each of the six patients, TR-4DCT images at the EOI, middle (MID), and EOE were reconstructed with both D1 and D2 approaches. TR-4DCT image quality was evaluated with mean distance-to-agreement (MDA) at the diaphragm compared with MRFB, tumor volume ratio (TVR) referenced to MRBH, and tumor shape difference (DICE index) compared with the selected input CT. Additionally, differences in the tumor center of mass (|∆COMD1-D2|), together with TVR and DICE comparison, was assessed in the D1 and D2 reconstructed TR-4DCT images. In the phantom, TR-4DCT quality is assessed by MDA=2.0±0.8mm at the diaphragm, TVR=0.8±0.0 for all tumors, and DICE=0.83±0.01, 0.85±0.02, 0.88±0.01 for ϕ=2, 3, 4cm tumors, respectively. In six patients, the MDA in diaphragm match is -1.6±3.1mm (D1) and 1.0±3.9mm (D2) between the reconstructed TR-4DCT and lowR MRFB among 18 images (3 phases/patient). The tumor similarity is TVR=1.2±0.2 and DICE=0.70±0.07 for D1 and TVR=1.4±0.3 (D2) and DICE=0.73±0.07 for D2. The tumor position difference is |∆COMD1-D2|=1.2±0.8mm between D1 and D2 reconstructions. The feasibility of super-resolution reconstruction of multi-breathing-cycle TR-4DCT is demonstrated and image quality at the diaphragm and tumor is assessed in both the 4D-XCAT phantom and six lung cancer patients. The similarity of D1 and D2 reconstruction suggests consistent and reliable DIR results. Clinically, TR-4DCT has the potential for breathing irregularity assessment and dosimetry evaluation in radiotherapy.

Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy.

Respiratory-induced tumor motion presents a critical challenge in lung cancer radiotherapy, potentially impacting treatment precision and efficacy. This study introduces an innovative, deep learning-based approach for real-time, markerless lung tumor tracking utilizing orthogonal X-ray projection images. It incorporates three key components: (1) a sophisticated data augmentation technique combining a hybrid deformable model with 3D thin-plate spline transformation, (2) a state-of-the-art Transformer-based segmentation network for precise tumor boundary delineation, and (3) a CNN regression network for accurate 3D tumor position estimation. We rigorously evaluated this approach using both patient data from The Cancer Imaging Archive and dynamic thorax phantom data, assessing performance across various noise levels and comparing it with current leading algorithms. For TCIA patient data, the average DSC and HD95 values were 0.9789 and 1.8423 mm, respectively, with an average centroid localization deviation of 0.5441 mm. On CIRS phantoms, DSCs were 0.9671 (large tumor) and 0.9438 (small tumor) with corresponding HD95 values of 1.8178 mm and 1.9679 mm. The 3D centroid localization accuracy was consistently below 0.33 mm. The processing time averaged 90 ms/frame. Even under high noise conditions (S2 = 25), errors for all data remained within 1 mm with tracking success rates mostly at 100%. In conclusion, the proposed markerless tracking method demonstrates superior accuracy, noise robustness, and real-time performance for lung tumor localization during radiotherapy. Its potential to enhance treatment precision, especially for small tumors, represents a significant step toward improving radiotherapy efficacy and personalizing cancer treatment.

On the trail of CBCT-guided adaptive rectal boost radiotherapy, does daily delineation require a radiation oncologist?

IntroductionDose-escalation radiotherapy for rectal tumours is increasingly considered as a non-operative approach, with online-adaptive radiotherapy (oART) supporting this approach by correcting inter-fraction tumour position errors. However, using cone-beam computed tomography (CBCT)-guided oART requires daily target volume delineation by different operators, leading to inter-operator delineation variability and potential dosimetric issues. This study aims to compare and quantify the inter-operator and inter-professional delineation variability of the rectal boost volume on CBCT, including volumes by an automatically delineated oART treatment planning system. Materials and methodsA rectal boost volume, defined as the primary tumour extended to the entire adjacent rectal wall, was delineated on 10 CBCTs from 5 patients by 15 operators: 4 expert radiation oncologists (ROs), 4 radiation therapists (RTTs) and 7 non-expert ROs. These contours were compared between the different professional groups. A comparison to the average volume of the group (ROs, RTTs, or non-expert ROs) with the lowest delineation variability was also performed for each individual volume including the volume automatically generated by an oART treatment planning system. ResultsDelineation variability was the highest in the superior (range: 2.3–6.0 mm), and inferior (2.3–12.4 mm) directions, compared to the left (0.2–4.4 mm), right (0.3–2.0 mm), anterior (0.1–2.9 mm), and posterior (0.5–4.0 mm) directions. Non-expert ROs, RTTs, and automatic oART volume showed similar ranges of delineation errors when compared to the expert ROs’ volume, which was chosen as reference volume since this professional group showed the lowest variability. DiscussionExpert ROs showed consistent results. Other professional groups exhibit similar variability, comparable to the automatic oART volume. Therefore, RTTs could safely perform the rectal boost delineation without non-expert ROs supervision in the absence of expert ROs during CBCT-based oART. Moreover, these findings provide quantitative data to compute accurate margins for the rectal boost planning target volume in a CBCT-guided oART workflow.

Impact of metadata in multimodal classification of bone tumours

The accurate classification of bone tumours is crucial for guiding clinical decisions regarding treatment and follow-up. However, differentiating between various tumour types is challenging due to the rarity of certain entities, high intra-class variability, and limited training data in clinical practice. This study proposes a multimodal deep learning model that integrates clinical metadata and X-ray imaging to improve the classification of primary bone tumours. The dataset comprises 1,785 radiographs from 804 patients collected between 2000 and 2020, including metadata such as age, affected bone site, tumour position, and gender. Ten tumour types were selected, with histopathology or tumour board decisions serving as the reference standard.MethodsOur model is based on the NesT image classification model and a multilayer perceptron with a joint fusion architecture. Descriptive statistics included incidence and percentage ratios for discrete parameters, and mean, standard deviation, median, and interquartile range for continuous parameters.ResultsThe mean age of the patients was 33.62 ± 18.60 years, with 54.73% being male. Our multimodal deep learning model achieved 69.7% accuracy in classifying primary bone tumours, outperforming the Vision Transformer model by five percentage points. SHAP values indicated that age had the most substantial influence among the considered metadata.ConclusionThe joint fusion approach developed in this study, integrating clinical metadata and imaging data, outperformed state-of-the-art models in classifying primary bone tumours. The use of SHAP values provided insights into the impact of different metadata on the model’s performance, highlighting the significant role of age. This approach has potential implications for improving diagnostic accuracy and understanding the influence of clinical factors in tumour classification.

Outcomes of a modified technique of partial parotidectomy and novel parotid tumour position classification from a single surgeon prospective database.

Conservative parotidectomy for benign tumours reduces facial nerve palsy, without increasing local recurrence. We report a modified technique of partial parotidectomy and using a novel description of tumour position, explore relationships between tumour position and histological margins, facial nerve palsy and local recurrence. A prospectively collected single surgeon parotidectomy database was analysed, including tumour location (superficial/deep lobe; central/peripheral) and outcomes. A partial parotidectomy identified the facial nerve and the proximal portion of its branches with a macroscopically clear resection margin. Mean follow up was 5.9 years for pleomorphic adenomas. Three hundred and three patients underwent parotidectomy; 257 (84.8%) were superficial and 46 (15.2%) deep lobe. Tumour position was recorded in 291: 236 (81.1%) were peripheral tumours and 55 (18.9%) central. Histological margin involvement was similar in central and peripheral tumours, both overall and for superficial and deep lobe tumours, but was commoner in central deep lobe tumours, (P = 0.003). Temporary partial facial nerve palsy occurred in 21 (6.9%), with one permanent partial nerve palsy (0.3%). Deep lobe tumours and total parotidectomy were associated with facial nerve palsy (P = 0.01). Facial nerve monitoring reduced the risk of palsy (P < 0.01). Local recurrence of pleomorphic adenomas was uncommon, occurring in 3 (2.0%) of 151 patients. This series confirms the safety and adequacy of more conservative partial parotidectomy for benign tumours, highlighting most tumours are peripheral, but not more prone to histological margin involvement or local recurrence, and with routine intraoperative facial nerve monitoring, is achieved with low facial nerve palsy rates.

The application and effect of presuturing with clips in endoscopic full-thickness resection.

There are few studies on presuturing for full-thickness resection. To explore the effect of using clips as a presuturing technique for endoscopic snare resection with an elastic band (ESR-EB). The clinical data of patients who underwent ESR-EB at Shenzhen Second People's Hospital between May 2023 and May 2024 were collected. The patients were divided into presuture and non-presuture groups according to whether tissues were stitched before resection. The general clinical characteristics, tumor growth position, tumor size, tumor growth pattern, pathological type, operation time, resection time, complication rate, number of clips, and postoperative antibiotic usage rate were compared. A total of 73 patients were enrolled, 55 of whom were included in the presuture group and 18 were included in the non-presuture group. There was no difference in age, sex, tumor position, tumor size, or tumor growth pattern between the 2 groups (P > .05). There was no significant difference between the 2 groups in terms of operation time, resection time, pathological diagnosis, number of clips, or complication rate (P > .05). Complete resection was achieved in all of the patients. The perforation diameter in the presuture group was significantly smaller than that in the non-presuture group ([3.20 ± 1.56] vs [4.67 ± 2.79], [P = .006]). Thirty-three (60%) patients in the presuture group and 16 (88.89%) patients in the non-presuture group received postoperative preventive antibiotics, and the difference between the 2 groups was significant (P = .024). Gastric myometrial lesions <10 mm in diameter can be completely removed via ESR-EB. Clips as a means of presuturing can significantly reduce the perforation diameter and the use of postoperative preventive antibiotics. Moreover, clips as a means of presuturing does not increase the total number of clips used for the procedure and therefore should be considered a feasible, safe and effective technique.

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. Artificial intelligence (AI) has recently 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 presents the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provides a literature summary on the topic. We will also discuss the limitations of these AI-based studies and propose potential improvements.

Automated brain tumor diagnostics: Empowering neuro-oncology with deep learning-based MRI image analysis.

Brain tumors, characterized by the uncontrolled growth of abnormal cells, pose a significant threat to human health. Early detection is crucial for successful treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the primary diagnostic tool for brain tumors, providing detailed visualizations of the brain's intricate structures. However, the complexity and variability of tumor shapes and locations often challenge physicians in achieving accurate tumor segmentation on MRI images. Precise tumor segmentation is essential for effective treatment planning and prognosis. To address this challenge, we propose a novel hybrid deep learning technique, Convolutional Neural Network and ResNeXt101 (ConvNet-ResNeXt101), for automated tumor segmentation and classification. Our approach commences with data acquisition from the BRATS 2020 dataset, a benchmark collection of MRI images with corresponding tumor segmentations. Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. This involves extracting features based on tumor shape, position, shape, and surface characteristics. To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). AWO mimics the hunting behavior of humpback whales to iteratively search for the optimal feature subset. With the selected features, we perform image segmentation using the ConvNet-ResNeXt101 model. This deep learning architecture combines the strengths of ConvNet and ResNeXt101, a type of ConvNet with aggregated residual connections. Finally, we apply the same ConvNet-ResNeXt101 model for tumor classification, categorizing the segmented tumor into distinct types. Our experiments demonstrate the superior performance of our proposed ConvNet-ResNeXt101 model compared to existing approaches, achieving an accuracy of 99.27% for the tumor core class with a minimum learning elapsed time of 0.53 s.

Development and validation of 3D printed anthropomorphic head phantom with eccentric holes for medical LINAC quality assurance testing in stereotactic radiosurgery

Stereotactic Radiosurgery (SRS) for brain tumors using Medical Linear Accelerator (LINAC) demands high precision and accuracy. A specific Quality Assurance (QA) is essential for every patient undergoing SRS to protect nearby non-cancerous cells by ensuring that the X-ray beams are targeted according to tumor position. In this work, a water-filled generic anthropomorphic head phantom consisting of two removable parts with eccentric holes was developed using Additive Manufacturing (AM) process for performing QA in SRS. In the patient specific QA, the planned radiation dose using Treatment Planning System (TPS) was compared with the dose measured in the phantom. Also, the energy consistency of radiation beams was tested at 200 MU for different energy beams at the central and eccentric holes of the phantom using an ionization chamber. Experimentally examined results show that planned doses in TPS are reaching the target within a 5% deviation. The ratio of the dose delivered in the eccentric hole to the dose delivered to the central hole shows variations of less than 2% for the energy consistency test. The designed, low-cost water-filled anthropomorphic phantom is observed to improve positioning verification and accurate dosimetry of patient-specific QA in SRS treatment.

Study on Esophageal Tumor Detection Based on the MTV Algorithm in Electrical Impedance Imaging

This paper presents a method for detecting and locating esophageal tumors using electrical impedance tomography (EIT) based on the modified total variation (MTV) regularization algorithm, utilizing a four-layer electrode array balloon detection structure. The optimal structure of the electrode array was obtained using the uniform design (UD) method. By integrating esophageal tissue structure information, physical models containing tumors at different locations were constructed. Using the adjacent excitation mode, the study compared average voltage, voltage dynamic range, and boundary voltage changes of electrode pairs within one-quarter of a cycle to analyze esophageal tumor characteristics. By comparing the correlation coefficients, relative errors, and imaging times of three reconstruction algorithms, the MTV algorithm, which best matches the morphological characteristics of the esophagus, was selected for image reconstruction. The calculated tumor height showed an error (ΔH) within 1 mm, indicating that EIT can provide vital information on the position, size, and electrical properties of esophageal tumors, demonstrating significant potential for clinical application in esophageal examinations.

Quantitative assessment of intertarget position variations based on 4D-CT and 4D-CBCT simulations in single-isocenter multitarget lung stereotactic body radiation therapy

BackgroundIn single-isocenter multitarget stereotactic body radiotherapy (SBRT), geometric miss risks arise from uncertainties in intertarget position. However, its assessment is inadequate, and may be interfered by the reconstructed tumor position errors (RPEs) during simulated CT and cone beam CT (CBCT) acquisition. This study aimed to quantify intertarget position variations and assess factors influencing it.MethodsWe analyzed data from 14 patients with 100 tumor pairs treated with single-isocenter SBRT. Intertarget position variation was measured using 4D-CT simulation to assess the intertarget position variations (ΔD) during routine treatment process. Additionally, a homologous 4D-CBCT simulation provided RPE-free comparison to determine the impact of RPEs, and isolating purely tumor motion induced ΔD to evaluate potential contributing factors.ResultsThe median ΔD was 4.3 mm (4D-CT) and 3.4 mm (4D-CBCT). Variations exceeding 5 mm and 10 mm were observed in 31.1% and 5.5% (4D-CT) and 20.4% and 3.4% (4D-CBCT) of fractions, respectively. RPEs necessitated an additional 1–2 mm safety margin. Intertarget distance and breathing amplitude variability showed weak correlations with variation (Rs = 0.33 and 0.31). The ΔD differed significantly by locations (upper vs. lower lobe and right vs. Left lung). Notably, left lung tumor pairs exhibited the highest risk.ConclusionsThis study provide a reliable way to assess intertarget position variation by using both 4D-CT and 4D-CBCT simulation. Consequently, single-isocenter SBRT for multiple lung tumors carries high risk of geometric miss. Tumor motion and RPE constitute a substantial portion of intertarget position variation, requiring correspondent strategies to minimize the intertarget uncertainties.

Efficacy of Minor Salivary Glands as Indicators of Depth of Resection for Superficial Hypopharyngeal Carcinoma.

This study aimed to investigate the utility of minor salivary glands in the hypopharynx as novel indicators for safe resection of superficial hypopharyngeal carcinomas with fewer complications. Cadaveric study. Cadavers were stored in the pathology laboratory at Kansai Medical University. Twenty-three cadaveric specimens were examined for minor salivary glands in the pyriform sinus, posterior wall, and postcricoid regions of the hypopharynx. Their count, size, and depth were assessed. Resected specimens from 5 consecutive patients with superficial hypopharyngeal carcinomas were pathologically analyzed to determine the positional relationship between cancer and minor salivary glands. Minor salivary glands were present in more than 70% of patients in each region during autopsy, with the postcricoid region having a larger count and size. The glands were universally present, regardless of sex, height, or body mass index. Minor salivary glands in the pyriform sinus and postcricoid region were present at a depth of 30% from the bottom of the submucosal layer, whereas those in the posterior wall were present in the shallow muscularis. During surgery, endoscopic findings revealed minor salivary glands as small white nodules in the submucosal layer. Pathological examination of the resected specimen confirmed that the white nodule was a minor salivary gland. In addition, tumor position in relation to minor salivary glands provided an adequate margin for resection. Minor salivary glands may serve as reliable indicators for determining adequate deep safety margins during surgery for superficial hypopharyngeal carcinoma.

Impact of setup and geometric uncertainties on the robustness of free-breathing photon radiotherapy of small lung tumors

Purpose:Respiratory motion and patient setup error both contribute to the dosimetric uncertainty in radiotherapy of lung tumors. Managing these uncertainties for free-breathing treatments is usually done by margin-based approaches or robust optimization. However, breathing motion can be irregular and concerns have been raised for the robustness of the treatment plans. We have previously reported the dosimetric effects of the respiratory motion, without setup uncertainties, in lung tumor photon radiotherapy using free-breathing images. In this study, we include setup uncertainty. Methods:Tumor positions from cine-CT images acquired in free-breathing were combined with per-fraction patient shifts to simulate treatment scenarios. A total of 14 patients with 300 tumor positions were used to evaluate treatment plans based on 4DCT. Four planning methods aiming at delivering 54 Gy as median tumor dose in three fractions were compared. The planning methods were denoted robust 4D (RB4), isodose to the PTV with a central higher dose (ISD), the ISD method normalized to the intended median tumor dose (IRN) and homogeneous fluence to the PTV (FLU). Results:For all planning methods 95% of the intended dose was achieved with at least 90% probability with RB4 and FLU having equal CTV D50% values at this probability. FLU gave the most consistent results in terms of CTV D50% spread and dose homogeneity. Conclusions:Despite the simulated patient shifts and tumor motions being larger than observed in the 4DCTs the dosimetric impact was suggested to be small. RB4 or FLU are recommended for the planning of free-breathing treatments.

The detection rate for prostate cancer in systematic and targeted prostate biopsy in biopsy-naive patients, according to the localization of the lesion at the mpMRI: A single-center retrospective observational study.

Evaluate the detection rates of systematic, targeted and combined cores at biopsy according to tumor positions in biopsy-naïve patients. A retrospective analysis of a single-center patient cohort (n = 501) that underwent transrectal prostate biopsy between January 2017 and December 2019 was performed. Multi-parametric MRI was executed as a prebiopsy investigation. Biopsy protocol included, for each patient, 12 systematic cores plus 3 to 5 targeted cores per lesion identified at the mpMRI. Pearson and McNemar chi-squared tests were used for statistical analysis to compare tumor location-related detection rates of systematic, targeted and combined (systematic + targeted) cores at biopsy. Median age of patients was 70 years (IQR 62-72), with a median PSA of 8.5 ng/ml (IQR 5.7-15.6). Positive biopsies were obtained in 67.7% of cases. Overall, targeted cores obtained higher detection rates compared to systematic cores (54.3% vs. 43.1%, p < 0.0001). Differences in detection rates were, however, higher for tumors located at the apex (61.1% vs. 26.3%, p < 0.05) and anteriorly (44.4% vs. 19.3%, p < 0.05). Targeted cores similarly obtained higher detection rates in the posterior zone of the prostate gland for clinically significant prostate cancer. A poor agreement was reported between targeted and systematic cores for the apex and anterior zone of the prostate with, respectively κ = 0.028 and κ = -0.018. A combined approach of targeted and systematic biopsy delivers the highest detection rate in prostate cancer (PCa). The location of the tumor could however greatly influence overall detection rates, indicating the possibility to omit (as for the base or posterior zone of the gland) or add (as for the apex or anterior zone of the gland) further targeted cores.