Pushing boundaries: Anterolateral thigh free flaps for extensive scalp defects beyond previous limits, leveraging imaging modalities with ultrasound and indocyanine green.

BACKGROUND: Scalp defect reconstruction poses considerable challenges, with ongoing debates regarding the most effective strategies. While the latissimus dorsi (LD) flap has traditionally been favored, the anterolateral thigh (ALT) flap has been well described as a versatile alternative for addressing extensive scalp defects. This study underscores the success of scalp reconstruction using ALT flaps, notably pushing the boundaries of previously reported flap sizes. Our approach leverages the use of indocyanine green (ICG) perfusion to guide precise preoperative planning and vascular modification, contributing to improved outcomes in challenging cases. METHODS: We performed 43 ALT flap reconstructions for scalp defects between 2016 and 2023. We collected patients' demographic and clinical data and evaluated flap size and recipient vessels and additional surgical techniques. Detailed preoperative plans with ultrasound and ICG use for intraoperative plans were performed to find perforators location. The cohort was divided into two, with or without complications on flaps, and analyzed depending on its surgical details. RESULTS: This study involved 38 patients with extensive scalp defects (mean age: 69.4 ± 11 years) who underwent ALT perforator flap transfers (mean flap size: 230.88 ± 145.6 cm2). There was only one case of unsuccessful flap transfer, and four cases had a few complications. The characteristics of the complication group included a large flap size (303.1 ± 170.9 vs. 214.9 ± 136.6 cm2, P = .211), few perforator numbers without pedicle manipulation, lack of intraoperative indocyanine green administration (75% vs. 25%, P = .607), and the use of superficial temporal vessels as recipient vessels. CONCLUSIONS: Scalp reconstruction using large ALT free flaps with the aid of imaging modalities facilitates the optimization of surgical techniques, such as pedicle manipulation, perforator numbers, and vein considerations, thereby contributing to successful reconstruction.

Ectopic cervical thymoma: a rare entity in the anterior neck.

Thymoma is a rare malignancy with usual location in the antero-superior mediastinum. Ectopic cervical thymoma (ECT) is an extremely rare tumor that originates from ectopic tissue, and is caused by the aberrant migration of the embryonic thymus. Our patient was a 56-year-old man who had a nodular lesion in the neck for several years. Computed tomography and Enhanced magnetic resonance imaging were performed. He underwent surgery, and a histological examination resulted in a diagnosis of type AB thymoma.

Dataset of weekly intra-treatment diffusion weighted imaging in head and neck cancer patients treated with MR-Linac.

Radiation therapy (RT) is a crucial treatment for head and neck squamous cell carcinoma (HNSCC); however, it can have adverse effects on patients' long-term function and quality of life. Biomarkers that can predict tumor response to RT are being explored to personalize treatment and improve outcomes. While tissue and blood biomarkers have limitations, imaging biomarkers derived from magnetic resonance imaging (MRI) offer detailed information. The integration of MRI and a linear accelerator in the MR-Linac system allows for MR-guided radiation therapy (MRgRT), offering precise visualization and treatment delivery. This data descriptor offers a valuable repository for weekly intra-treatment diffusion-weighted imaging (DWI) data obtained from head and neck cancer patients. By analyzing the sequential DWI changes and their correlation with treatment response, as well as oncological and survival outcomes, the study provides valuable insights into the clinical implications of DWI in HNSCC.

Large atypical lipomatous tumour of the neck with mediastinal extension managed by transcervical excision.

Tumours of adipose tissue origin are relatively rare in the head and neck. Here, we report a case of an unfamiliar lipomatous lesion that involved the neck and mediastinum. A nil-comorbid man in his 40s presented with a slowly progressive anterior neck swelling of 3 years, which was diagnosed as lipoma by histopathological sampling. Computed tomography demonstrated the lesion to be involving parapharyngeal and retropharyngeal spaces with mediastinal extension. The lesion was removed by the transcervical approach. The final histology of the excised specimen, with immunohistochemistry for mouse double minute 2 (MDM2) and p16, suggested an atypical lipomatous tumour (ALT). This report accentuates the occurrence of this rare neoplasm in the neck, which often mimics lipoma clinically. Although radiology can demonstrate suggestive features, histology with MDM2 and/or p16 positivity can confirm the diagnosis of ALT as against the lipoma. A successful transcervical excision, despite the deeper extension of the lesion between the critical structures of the neck and mediastinum, demonstrates the non-infiltrating nature of the tumour.

Comparison of Doppler Imaging and Microvascular Imaging in Cervical Lymph Node Blood Flow Analysis.

Cervical lymph node metastasis is an important determinant of cancer stage and the selection of an appropriate treatment plan for patients with head and neck cancer. Therefore, metastatic cervical lymph nodes should be effectively differentiated from lymphoma, tuberculous lymphadenitis, and other benign lymphadenopathies. The aim of this work is to describe the performance of Doppler ultrasound and superb microvascular imaging (SMI) in evaluating blood flow information of cervical lymph nodes. In addition, the features of flow imaging in metastatic lymph nodes, lymphoma, and tuberculous lymphadenitis were described. Compared with Doppler ultrasound, SMI, the latest blood flow imaging technology, could detect more blood flow signals because the sensitivity, specificity, and accuracy of SMI in the diagnosis of cervical lymph node disease were higher. This article summarizes the value of Doppler ultrasound and SMI in evaluating cervical lymph node diseases and focuses on the diagnostic performance of SMI.

Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images.

BACKGROUND AND OBJECTIVE: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinical observations. Unfortunately, most segmentation methods known today are limited to nuclei and cannot segment the cytoplasm. METHODS: We present a new network architecture Cyto R-CNN that is able to accurately segment whole cells (with both the nucleus and the cytoplasm) in bright-field images. We also present a new dataset CytoNuke, consisting of multiple thousand manual annotations of head and neck squamous cell carcinoma cells. Utilizing this dataset, we compared the performance of Cyto R-CNN to other popular cell segmentation algorithms, including QuPath's built-in algorithm, StarDist, Cellpose and a multi-scale Attention Deeplabv3+. To evaluate segmentation performance, we calculated AP50, AP75 and measured 17 morphological and staining-related features for all detected cells. We compared these measurements to the gold standard of manual segmentation using the Kolmogorov-Smirnov test. RESULTS: Cyto R-CNN achieved an AP50 of 58.65% and an AP75 of 11.56% in whole-cell segmentation, outperforming all other methods (QuPath 19.46/0.91%; StarDist 45.33/2.32%; Cellpose 31.85/5.61%, Deeplabv3+ 3.97/1.01%). Cell features derived from Cyto R-CNN showed the best agreement to the gold standard (D¯=0.15) outperforming QuPath (D¯=0.22), StarDist (D¯=0.25), Cellpose (D¯=0.23) and Deeplabv3+ (D¯=0.33). CONCLUSION: Our newly proposed Cyto R-CNN architecture outperforms current algorithms in whole-cell segmentation while providing more reliable cell measurements than any other model. This could improve digital pathology workflows, potentially leading to improved diagnosis. Moreover, our published dataset can be used to develop further models in the future.

Large congenital cervical mass in a neonate: prenatal diagnosis and postnatal management of teratoma: a case report.

INTRODUCTION: Cervical teratomas are rare congenital neoplasms that can cause neonatal airway obstruction if large. CASE PRESENTATION: The female Persian neonate displayed respiratory distress at birth, with a 7 cm × 8 cm cystic solid mass identified on the left side of the neck. Antenatal ultrasonography revealed polyhydramnios. Despite initial stabilization, the infant required intubation and mechanical ventilation due to persistent respiratory distress. Imaging confirmed a cystic mass compressing the trachea, ruling out cystic hygroma. Surgical resection on postnatal day 17 revealed a 10 cm × 10 cm solid cystic structure, histologically identified as an immature teratoma. CONCLUSION: Despite risks of poor fetal and postnatal outcome from large cervical teratomas, early surgical resection after airway stabilization can result in recovery. Proper multidisciplinary management of respiratory distress from such tumors is paramount.

Impact Of PET/CT On Treatment In Patients With Head And Neck Squamous Cell Carcinoma.

<b><br>Introduction:</b> Although PET/CT is effective for staging HNSCC, its impact on patient management is somewhat controversial. For this reason, we considered it necessary to carry out a study in order to verify whether PET/CT helps to improve the prognosis and treatment in patients. This study was designed to address the impact of PET-FDG imaging when used alongside CT in the staging and therapeutic management of patients with HNSCC.</br> <b><br>Material and methods:</b> Data was collected from 169 patients diagnosed with HNSCC with both CT and PET/CT (performed within a maximum of 30 days of each other). It was evaluated whether discrepancies in the diagnosis of the two imaging tests had impacted the treatment.</br> <b><br>Results:</b> The combined use of CT and PET/CT led to a change in the treatment of 67 patients, who represented 39.7% of the sample. In 27.2% of cases, it entailed a change in the type of treatment which the patient received. In 3.0% of the cases, using both diagnostic tests led to modifications of the therapeutic intention of our patients.</br> <b><br>Conclusions:</b> Using PET/CT in addition to the conventional imaging method in staging resulted in more successful staging and more appropriate therapeutic decision-making.</br>.

The Diagnostic Performance of Magnetic Resonance Imaging in the Categorization of Pediatric Neck Lymph Nodes: Radiologic and Pathologic Correlations.

BACKGROUND/AIM: To present MRI features of neck lymph nodes in benign and malignant conditions in the pediatric population. MATERIALS AND METHODS: MRIs of the neck of 51 patients 1 to 18 years old (40 boys, 11 girls [10.08±4.73]) with lymph node biopsy were retrospectively analyzed. Those were grouped as benign including reactive (27 [52.9%]) and lymphadenitis (11 [21.6%]), and malignant (13 [25.5%]). The groups were evaluated multiparametrically in terms of quantitative and qualitative variables. RESULTS: The long axis, short axis, area, and apparent diffusion coefficient (ADC) values of the largest lymph node were 21 (17 to 24) mm, 14 (12 to 18) mm, 228.60 (144.79 to 351.82) mm 2 , 2531 (2457 to 2714) mm 2 /s for reactive, 24 (19 to 27) mm, 15 (11 to 20) mm, 271.80 (231.43 to 412.20) mm 2 , 2534 (2425 to 2594) mm 2 /s for lymphadenitis, 27 (23.50 to 31.50) mm, 20 (15 to 22) mm, 377.08 (260.47 to 530.94) mm 2 , 2337 (2254 to 2466) mm 2 /s for malignant, respectively. Statistical analysis of our data suggests that the following parameters are associated with a higher likelihood of malignancy: long axis >22 mm, short axis >16 mm, area >319 cm 2 , ADC value <2367 mm 2 /s, and supraclavicular location. Perinodal and nodal heterogeneity, posterior cervical triangle location are common in lymphadenitis ( P <0.001). Reactive lymph nodes are distributed symmetrically in both neck halves ( P <0.001). CONCLUSION: In the MRI-based approach to lymph nodes, not only long axis, short axis, surface area, and ADC, but also location, distribution, perinodal, and nodal heterogeneity should be used.

Radiomic Application for Head and Neck Squamocellular Tumor: Systematic Review.

Abstract: Radiomics represents the convergence of artificial intelligence and radiological data analysis, primarily applied in the diagnosis and treatment of cancer. In the head and neck region, squamous cell carcinoma is the most prevalent type of tumor. Recent radiomics research has revealed that specific bio-imaging characteristics correlate with various molecular features of Head and Neck Squamous Cell Carcinoma (HNSCC), particularly Human Papillomavirus (HPV). These tumors typically present a unique phenotype, often affecting younger patients, and show a favorable response to radiation therapy. This study provides a systematic review of the literature, summarizing the application of radiomics in the head and neck region. It offers a comprehensive analysis of radiomics-based studies on HNSCC, evaluating its potential for tumor evaluation, risk stratification, and outcome prediction in head and neck cancer treatment.

BAF-Net: bidirectional attention-aware fluid pyramid feature integrated multimodal fusion network for diagnosis and prognosis.

Objective. To go beyond the deficiencies of the three conventional multimodal fusion strategies (i.e. input-, feature- and output-level fusion), we propose a bidirectional attention-aware fluid pyramid feature integrated fusion network (BAF-Net) with cross-modal interactions for multimodal medical image diagnosis and prognosis.Approach. BAF-Net is composed of two identical branches to preserve the unimodal features and one bidirectional attention-aware distillation stream to progressively assimilate cross-modal complements and to learn supplementary features in both bottom-up and top-down processes. Fluid pyramid connections were adopted to integrate the hierarchical features at different levels of the network, and channel-wise attention modules were exploited to mitigate cross-modal cross-level incompatibility. Furthermore, depth-wise separable convolution was introduced to fuse the cross-modal cross-level features to alleviate the increase in parameters to a great extent. The generalization abilities of BAF-Net were evaluated in terms of two clinical tasks: (1) an in-house PET-CT dataset with 174 patients for differentiation between lung cancer and pulmonary tuberculosis. (2) A public multicenter PET-CT head and neck cancer dataset with 800 patients from nine centers for overall survival prediction.Main results. On the LC-PTB dataset, improved performance was found in BAF-Net (AUC = 0.7342) compared with input-level fusion model (AUC = 0.6825;p< 0.05), feature-level fusion model (AUC = 0.6968;p= 0.0547), output-level fusion model (AUC = 0.7011;p< 0.05). On the H&N cancer dataset, BAF-Net (C-index = 0.7241) outperformed the input-, feature-, and output-level fusion model, with 2.95%, 3.77%, and 1.52% increments of C-index (p= 0.3336, 0.0479 and 0.2911, respectively). The ablation experiments demonstrated the effectiveness of all the designed modules regarding all the evaluated metrics in both datasets.Significance. Extensive experiments on two datasets demonstrated better performance and robustness of BAF-Net than three conventional fusion strategies and PET or CT unimodal network in terms of diagnosis and prognosis.

Performance evaluation of commercial and non-commercial shear wave elastography implementations for vascular applications.

BACKGROUND: Shear wave elastography (SWE) is mainly used for stiffness estimation of large, homogeneous tissues, such as the liver and breasts. However, little is known about its accuracy and applicability in thin (∼0.5-2 mm) vessel walls. To identify possible performance differences among vendors, we quantified differences in measured wave velocities obtained by commercial SWE implementations of various vendors over different imaging depths in a vessel-mimicking phantom. For reference, we measured SWE values in the cylindrical inclusions and homogeneous background of a commercial SWE phantom. Additionally, we compared the accuracy between a research implementation and the commercially available clinical SWE on an Aixplorer ultrasound system in phantoms and in vivo in patients. METHODS: SWE measurements were performed over varying depths (0-35 mm) using three ultrasound machines with four ultrasound probes in the homogeneous 20 kPa background and cylindrical targets of 10, 40, and 60 kPa of a multi-purpose phantom (CIRS-040GSE) and in the anterior and posterior wall of a homogeneous polyvinyl alcohol vessel-mimicking phantom. These phantom data, along with in vivo SWE data of carotid arteries in 23 patients with a (prior) head and neck neoplasm, were also acquired in the research and clinical mode of the Aixplorer ultrasound machine. Machine-specific estimated phantom stiffness values (CIRS phantom) or wave velocities (vessel phantom) over all depths were visualized, and the relative error to the reference values and inter-frame variability (interquartile range/median) were calculated. Correlations between SWE values and target/vessel wall depth were explored in phantoms and in vivo using Spearman's correlations. Differences in wave velocities between the anterior and posterior arterial wall were assessed with Wilcoxon signed-rank tests. Intra-class correlation coefficients were calculated for a sample of ten patients as a measure of intra- and interobserver reproducibility of SWE analyses in research and clinical mode. RESULTS: There was a high variability in obtained SWE values among ultrasound machines, probes, and, in some cases, with depth. Compared to the homogeneous CIRS-background, this variation was more pronounced for the inclusions and the vessel-mimicking phantom. Furthermore, higher stiffnesses were generally underestimated. In the vessel-mimicking phantom, anterior wave velocities were (incorrectly) higher than posterior wave velocities (3.4-5.6 m/s versus 2.9-5.9 m/s, p ≤ 0.005 for 3/4 probes) and remarkably correlated with measurement depth for most machines (Spearman's ρ = -0.873-0.969, p < 0.001 for 3/4 probes). In the Aixplorer's research mode, this difference was smaller (3.3-3.9 m/s versus 3.2-3.6 m/s, p = 0.005) and values did not correlate with measurement depth (Spearman's ρ = 0.039-0.659, p ≥ 0.002). In vivo, wave velocities were higher in the posterior than the anterior vessel wall in research (left p = 0.001, right p < 0.001) but not in clinical mode (left: p = 0.114, right: p = 0.483). Yet, wave velocities correlated with vessel wall depth in clinical (Spearman's ρ = 0.574-0.698, p < 0.001) but not in research mode (Spearman's ρ = -0.080-0.466, p ≥ 0.003). CONCLUSIONS: We observed more variation in SWE values among ultrasound machines and probes in tissue with high stiffness and thin-walled geometry than in low stiffness, homogeneous tissue. Together with a depth-correlation in some machines, where carotid arteries have a fixed location, this calls for caution in interpreting SWE results in clinical practice for vascular applications.

Image denoising and model-independent parameterization for IVIM MRI.

Objective. To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging quality using a new image denoising technique and model-independent parameterization of the signal versusb-value curve.Approach. IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five of these patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the ill-posed mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the correlations between parameter changes in parotid glands with radiotherapy dose levels. The validity of blur kernel predictions was assessed by the testing the method's ability to recover artificial 'pseudokernels'. AUC parameters were compared with monoexponential, biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) around parotid glands, and relative importance via principal component analysis.Main results. Image denoising improved blind image quality metrics, smoothed the signal versusb-value curve, and strengthened correlations between IVIM parameters and dose levels. Image TV was reduced and parameter CNRs generally increased following denoising.AUCparameters were more correlated with dose and had higher relative importance than exponential model parameters.Significance. IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versusb-value curve with model-independent parameters like theAUCand preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility.

Application value of a hybrid tracer during sentinel lymph node biopsy for head and neck malignancies: A systematic review and meta-analysis.

To address the limitations of conventional sentinel lymph node biopsy (SLNB), a novel hybrid tracer (indocyanine green [ICG]-99mTc-nanocolloid) has been developed. This meta-analysis aimed to compare the differences between the novel hybrid tracer and conventional methods using ICG or radioisotope (RI) for SLNB in head and neck malignancies. This study was registered in the International Prospective Register of Systematic Reviews (CRD42023409127). PubMed, Embase, Web of Science, and the Cochrane Library were systematically searched. This study included raw data on the number of sentinel lymph nodes (SLNs) identified using different modalities during surgery for head and neck malignancies. The identification rate of SLNs was the main outcome of interest. Prognostic data and complication rate cannot be deduced from this article. The heterogeneity test (I2) determined the use of a fixed- or random-effects model for the pooled risk ratio (RR). Overall, 1275 studies were screened, of which 11 met the inclusion criteria for the meta-analysis. In SLN identification of head and neck malignancies, ICG-99mTc-nanocolloid was superior to ICG or RI. In the subgroup analyses, the detection rates of ICG and RI tracers in SLNB were comparable, regardless of the device, tumor type, or tumor stage. In conclusion, in SLN identification of head and neck malignancies, the use of ICG-99mTc-nanocolloid is superior to the single technique of ICG or RI. This study suggests that Hospitals using ICG or RI may find it beneficial to change their practice to ICG-99mTc-nanocolloid, especially in the head and neck area, owing to its superior effectiveness.

Malignancy and "Violated Neck" Rates in Consecutive Cohort of 79 Adult Patients With Solitary Cystic Neck Mass-Lessons Learned and Recommendations for Clinical Practice Guidelines.

OBJECTIVE: The neck region is a common site for solitary cystic neck mass (SCNM) of various etiologies, including congenital, inflammatory, and neoplastic. In adults, the primary focus is excluding malignancy. The objective of this study was to retrospectively analyze the accuracy of available diagnostic technologies for the differentiation of benign and malignant SCNM in adult patients. The study aimed to develop new clinical practice guidelines for evaluating and managing SCNM. METHODS: The primary predictive variables were the diagnostic utilities of fine-needle aspiration cytology (FNAC), ultrasound (U/S), multislice computed tomography, and magnetic resonance imaging. The study's endpoint was the overall diagnostic accuracy in differentiating between benign and malignant SCNM. The final diagnosis was based on histopathology. RESULTS: The study included 79 adult patients: 55 (69.62%) male and 24 (30.38%) female ( P <0.05). The mean age at presentation was 42.1 years (range: 18-84 years). Solitary cystic neck mass was distributed in the anterior neck region in 30 (37.97%) patients and the posterolateral neck regions in 49 (62.03%) patients ( P <0.05). The posterolateral neck regions had a significantly higher rate of malignant SCNM than the anterior neck region [19/49 (38.78%) versus 1/30 (3.33%)] ( P <0.05). There was no statistically significant difference between the U/S+FNAC and U/S+FNAC+multislice computed tomography and/or magnetic resonance imaging groups in differentiating benign and malignant SCNM (40/42 versus 36/37, P >0.05). "Violated neck" was recorded in 2 cases. CONCLUSION: A systematic investigation protocol should be applied to evaluate adult patients with SCNM.

Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.

BACKGROUND: This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI). METHODS: A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility. RESULTS: Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model. CONCLUSIONS: The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.

MRI radiomics in head and neck cancer from reproducibility to combined approaches.

The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.

Application of stoichiometric CT number calibration method for dose calculation of tissue heterogeneous volumes in boron neutron capture therapy.

BACKGROUND: Monte Carlo simulation code is commonly used for the dose calculation of boron neutron capture therapy. In the past, dose calculation was performed assuming a homogeneous mass density and elemental composition inside the tissue, regardless of the patient's age or sex. Studies have shown that the mass density varies with patient to patient, particularly for those that have undergone surgery or radiotherapy. A method to convert computed tomography numbers into mass density and elemental weights of tissues has been developed and applied in the dose calculation process using Monte Carlo codes. A recent study has shown the variation in the computed tomography number between different scanners for low- and high-density materials. PURPOSE: The aim of this study is to investigate the effect of the elemental composition inside each calculation voxel on the dose calculation and the application of the stoichiometric CT number calibration method for boron neutron capture therapy planning. METHODS: Monte Carlo simulation package Particle and Heavy Ion Transport code System was used for the dose calculation. Firstly, a homogeneous cubic phantom with the material set to ICRU soft tissue (four component), muscle, fat, and brain was modelled and the NeuCure BNCT system accelerator-based neutron source was used. The central axis depth dose distribution was simulated and compared between the four materials. Secondly, a treatment plan of the brain and the head and neck region was simulated using a dummy patient dataset. Three models were generated; (1) a model where only the fundamental materials were considered (simple model), a model where each voxel was assigned a mass density and elemental weight using (2) the Nakao20 model, and (3) the Schneider00 model. The irradiation conditions were kept the same between the different models (irradiation time and irradiation field size) and the near maximum (D1%) and mean dose to the organs at risk were calculated and compared. RESULTS: A maximum percentage difference of approximately 5% was observed between the different materials for the homogeneous phantom. With the dummy patient plan, a large dose difference in the bone (greater than 12%) and region near the low-density material (mucosal membrane, 7%-11%) was found between the different models. CONCLUSIONS: A stoichiometric CT number calibration method using the newly developed Nakao20 model was applied to BNCT dose calculation. The results indicate the importance of calibrating the CT number to elemental composition for each individual CT scanner for the purpose of BNCT dose calculation along with the consideration of heterogeneity of the material composition inside the defined region of interest.

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images.

Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images.Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics-the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)-to evaluate the agreement between the manual and semi-automatic methods.Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91.Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.