Tumor Boundary Research Articles

A comparative study of pre-processing methods to improve glioma segmentation performance in brain MRI using deep learning

Background: Glioma is the most common brain tumor in adult patients and requires accurate treatment. The delineation of tumor boundaries must be accurate and precise, which is crucial for treatment planning. Currently, delineating boundaries for tumors is a tedious, time-consuming task and may be prone to human error among oncologists. Therefore, artificial intelligence plays a vital role in reducing these problems. Objective: This study aims to find a relationship between improving image enhancement and evaluating the performance of deep learning models for segmenting glioma image data on brain MRI images. Materials and methods: The BraTs2023 dataset was used in this study. The image dataset was converted from three dimensions to two dimensions and then subjected to pre-processing via four image enhancement techniques, including contrast-limited adaptive histogram equalization (CLAHE), gamma correction (GC), non-local mean filter (NLMF), and median and Wiener filter (MWF). Subsequently, it was evaluated for structural similarity index (SSIM) and mean squared error. The deep learning segmentation model was created using the U-Net architecture and assessed for dice similarity coefficient (DSC), accuracy, precision, recall, F1-score, and Jaccard index for tumor segmentation. Results: The performance of enhanced image results for CLAHE, GC, NLMF, and MWF techniques shows SSIM values of 0.912, 0.905, 0.999, and 0.911, respectively. The dice similarity coefficient (DSC) for segmentation without image enhancement was 0.817. The DSC of segmentation for CLAHE, GC, NLMF, and MWF techniques were 0.818, 0.812, 0.820, and 0.797, respectively. Conclusion: The enhanced image technique could affect the performance of tumor segmentation. by the enhanced image for use in a trained model may increase or decrease performance depending on the chosen image enhancement technique and the parameters determined by each method.

A mitochondrion-targeted cyanine agent for NIR-II fluorescence-guided surgery combined with intraoperative photothermal therapy to reduce prostate cancer recurrence

Poorly identified tumor boundaries and nontargeted therapies lead to the high recurrence rates and poor quality of life of prostate cancer patients. Near-infrared-II (NIR-II) fluorescence imaging provides certain advantages, including high resolution and the sensitive detection of tumor boundaries. Herein, a cyanine agent (CY7-4) with significantly greater tumor affinity and blood circulation time than indocyanine green was screened. By binding albumin, the absorbance of CY7-4 in an aqueous solution showed no effects from aggregation, with a peak absorbance at 830 nm and a strong fluorescence emission tail beyond 1000 nm. Due to its extended circulation time (half-life of 2.5 h) and high affinity for tumor cells, this fluorophore was used for primary and metastatic tumor diagnosis and continuous monitoring. Moreover, a high tumor signal-to-noise ratio (up to ~ 10) and excellent preferential mitochondrial accumulation ensured the efficacy of this molecule for photothermal therapy. Therefore, we integrated NIR-II fluorescence-guided surgery and intraoperative photothermal therapy to overcome the shortcomings of a single treatment modality. A significant reduction in recurrence and an improved survival rate were observed, indicating that the concept of intraoperative combination therapy has potential for the precise clinical treatment of prostate cancer.Graphical

Abstract PO5-22-09: Positive pegulicianine fluorescence rate in the lumpectomy cavity correlates with tumor distance to margins in excised tissue

Abstract Title: Positive pegulicianine fluorescence rate in the lumpectomy cavity correlates with tumor distance to margins in excised tissue Background: A randomized, two-arm pivotal study of pegulicianine fluorescence guided surgery (pFGS) at 14 US sites was conducted using the Lumicell Direct Visualization System (DVS) to identify residual cancer in the lumpectomy cavity after the standard of care (SoC) tumor excision. We reported that the Lumicell DVS detects residual cancer in the lumpectomy cavity, including in orientations pathologically negative on the SoC specimen. In addition, 15% (9 of 62) of patients with positive SoC margins were converted to final negative margins by taking Lumicell DVS directed cavity margins, avoiding a second unnecessary surgery. Interestingly, 8 of these 9 patients had no tumor found in the Lumicell DVS guided specimens. We hypothesize that fluorescence decreases as the distance from the tumor boundary increases. We analyzed data from the pivotal study (NCT03686215) to assess correlation of pegulicianine signal with tumor-to-margin distance, and its potential clinical value. Methods: The pivotal study enrolled 406 patients undergoing breast conserving surgery (BCS) for stage 0-III breast cancers. Patients received an IV injection of pegulicianine 2-6 hours prior to surgery. After the standard of care lumpectomy was completed, additional Lumicell DVS-guided cavity margins were excised at sites of positive pegulicianine fluorescence signal in the lumpectomy cavity walls using a hand-held imaging device and patient-calibrated cancer detection software. The positive fluorescence rate, that is, the rate at which the Lumicell DVS indicates areas suspected to contain cancer, was calculated as the ratio of orientations with positive pegulicianine signal to the total number of orientations with (1) negative margins, (2) positive margins and (3) positive margins with ink on tumor, on the initial SoC lumpectomy specimen. Calculations were made on a per-margin basis. Positive margins were defined as ink on tumor for invasive cancer and tumor less than 2 mm from the margin for ductal carcinoma in situ (DCIS) alone. Results: Results are presented in Table 1. We found a statistically significant higher positive fluorescence rate in orientations with positive margins (p=0.044). The positive fluorescence rate was even higher (21.1%) when only considering cases with ink on tumor (a subset of the positive margin group). Discussion: By design, pegulicianine is activated by enzymes within the tumor and in stromal cells at the invasive front surrounding the tumor. Thus, when tumor is closer to the edge of the lumpectomy specimen, the rate of positive pegulicianine fluorescence in the cavity increases, being highest when there is tumor present at the inked margin. This feature of pegulicianine fluorescence guides the surgeon to resect more tissue in these areas in order to achieve a negative margin of the required width. The conversion of positive margins after the SoC lumpectomy to appropriately wide final negative margins prevents a second surgery, even when there is no tumor in the DVS guided margins. These findings have strengthened our understanding of the utility of this technology. Table 1 Positive cavity wall fluorescence rate for SoC lumpectomy positive margins, SoC lumpectomy negative margins and SoC lumpectomy ink on tumor margins Citation Format: Irene Wapnir, E Shelley Hwang, Kelly Hunt, David Carr, Peter Blumencranz, Manna Chang, Kate Smith, Jorge Ferrer, Barbara Smith. Positive pegulicianine fluorescence rate in the lumpectomy cavity correlates with tumor distance to margins in excised tissue [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-22-09.

Abstract PO2-25-07: PEGylated Functional Upstream Domain (PEG-FUD): an anti-cancer therapy for breast Cancer

Abstract Background: Breast cancer (BC) is the most common cancer diagnosed in women. In breast cancers, the extracellular matrix (ECM) is known to play a key role in disease progression by mediating cell signaling processes that drive cancer cell proliferation, migration and invasion. Work by our group identified that aligned collagen fibers perpendicular to the tumor boundary are prognostic of poor patient prognosis. Further, through matrix targeted proteomics we determined that the ECM protein, Fibronectin (FN), organizes with aligned collagen fibers in BC tissues. FN is known as the master regulator of ECM assembly because FN deposition precedes and regulates the deposition of several other ECM proteins. Clinical evidence shows that high expression of FN levels in BC patients correlates with an increased mortality risk. Due to the abundant expression of FN in the tumor microenvironment, FN has been a useful biomarker for cancer imaging and therapy. Despite multiple efforts in developing therapies that target the ECM, currently there are no effective ECM treatments available due to toxicity and lack of specificity. Methods: To target abnormal FN deposition, we used a FN binding peptide, Functional Upstream Domain (FUD), derived from the F1 adhesin from Streptococcus pyogenes. PEGylated-FUD (PEG-FUD) is a potent inhibitor of FN assembly which binds the 70 kDa N-terminal region of FN with high affinity. We hypothesize that PEG-FUD will localize to the tumor site and that targeted disruption of FN assembly with PEG-FUD will reduce ECM deposition, thus block tumor progression in-vivo. In this study, we used an in vivo imaging system to assess the biodistribution of 20-kDa PEG-FUD following subcutaneous injection of Cy5 labeled peptide in 4T1 mammary tumor bearing mice. Additionally, in a second therapeutic experiment, we treated 4T1 mammary tumor bearing mice with 20-kDa PEG-FUD every 48 hr for a total of 10 treatments and measured tumor volume as an indicator of primary tumor burden. Results: As anticipated, we observed PEG-FUD’s accumulation and localization in 4T1 tumors. Additionally, PEG-FUD treatment significantly reduced tumor growth compared to saline treatment. There were no observed changes in standard toxicity assessments such as body weight and spleen weight among treatment groups. After PEG-FUD’s therapeutic treatment, we observed a significant reduction in the deposition of FN matrix and an increase in cleaved caspase-3, a known marker for apoptosis by western blotting providing a potential mechanism of action of PEG-FUD inducing changes in tumor growth. Conclusions: PEG-FUD’s localization in tumors suggests its utility as a solo cancer imaging agent while providing indications that PEG-FUD can be used to deliver other therapeutics to the tumor site as a conjugate in the future. In addition, therapeutic treatment of PEG-FUD inhibited tumor growth providing preliminary evidence for PEG-FUD’s use as a tumor targeting anti-cancer agent. Citation Format: Metti Gari. PEGylated Functional Upstream Domain (PEG-FUD): an anti-cancer therapy for breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-25-07.

Abstract PO1-26-01: Leveraging Clinicopathological Factors and Deep Learning-Based Morphometrics for PDX Engraftment Success Prediction in Breast Cancer

Abstract Background: Patient-derived xenografts (PDXs) are pivotal in cancer research. Despite histopathological insights into factors driving PDX success, the role of artificial intelligence (AI) in predicting PDX engraftment remains unexplored. We aimed to bridge this gap by integrating clinicopathological data and AI-based morphometric analysis to predict PDX success in breast cancer. Methods: PDXs were generated from tumor tissues derived from breast cancer patients who had undergone surgical intervention. Clinicopathological information including subtypes, pathological diagnosis, modified Bloom-Richardson system histologic grades, treatment with neoadjuvant chemotherapy (NAC), Miller Payne grade, residual caner burden score, invasive tumor size, lymphovascular invasion status, AJCC 8th T and N stages and the percentage of tumor infiltrating lymphocytes were collected and analyzed. For the image analysis component, whole-slide images (WSIs) of hematoxylin and eosin–stained tissue samples from 64 surgically resected breast cancer patients were used as a training set for an AI model under the supervision of 2 pathologists to extract morphometric features. The model transformed image tiles into patches of morphologically similar patterns, and categorized them into adipose tissue, background, necrosis, invasive carcinoma, carcinoma in situ, stroma, and terminal ductal lobular unit. This trained model was subsequently applied to the WSIs employed in the establishment of PDXs. The classified patches and their relative ratios within the invasive tumor boundary were compiled. The consolidated data from clinicopathological and image analyses were subjected to logistic regression to discern correlates of successful PDX engraftment. Results: Out of the 311 patient tumor samples used for generating PDXs, (131 post-chemotherapy, 180 chemo-naïve), 47 PDXs were successfully established (15.1%). Logistic regression revealed several factors for successful engraftment including NAC treatment with an odds ratio of 6.71 (28.2% vs 5.6%, 95% CI: 2.53 - 17.80, p < 0.001), higher histologic grades (25.3% vs 2.2%, OR = 5.80, 95% CI: 1.49 - 22.63, p = 0.01), triple negative breast cancer compared to hormone receptor-positive cancers (32.0% vs 3.3%, OR = 10.99, 95% CI: 1.26 - 95.89, p = 0.03), and tumors of larger size (OR = 1.34, 95% CI: 1.02 - 1.76, p = 0.03). Interestingly, the percentage of specific tissue patch types within tumor did not significantly impact the likelihood of successful PDX engraftment. However, in our logistic regression analysis based solely on morphometric features, presence of necrosis within the tumor notably enhanced PDX establishment. Specifically, each percent increase in necrosis within tumor boosted the odds of successful PDX creation by 0.01% (OR = 1.0001, 95% CI: 1.00003 - 1.00024, p = 0.01). Conclusions: PDXs are often successfully established from clinically aggressive breast cancers, particularly those with NAC treatment, higher histologic grades, TNBC subtype, and larger tumor size. While morphometric features contribute to the prediction of PDX engraftment success, their importance is surpassed by these clinicopathological factors. However, presence of necrosis emerged as a key morphometric predictor of successful PDX engraftment. Keywords: breast cancer; patient-derived xenograft (PDX); Breast Cancer Morphometrics; Cancer Predictive Modeling. Citation Format: Jongwon Lee, GeonHee Lee, Gyungyub Gong, Hee Jin Lee. Leveraging Clinicopathological Factors and Deep Learning-Based Morphometrics for PDX Engraftment Success Prediction in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-26-01.

3DSN-Net: A 3-D Scale-Aware convNet With Nonlocal Context Guidance for Kidney and Tumor Segmentation From CT Volumes.

Automatic kidney and tumor segmentation from CT volumes is a critical prerequisite/tool for diagnosis and surgical treatment (such as partial nephrectomy). However, it remains a particularly challenging issue as kidneys and tumors often exhibit large-scale variations, irregular shapes, and blurring boundaries. We propose a novel 3-D network to comprehensively tackle these problems; we call it 3DSN-Net. Compared with existing solutions, it has two compelling characteristics. First, with a new scale-aware feature extraction (SAFE) module, the proposed 3DSN-Net is capable of adaptively selecting appropriate receptive fields according to the sizes of targets instead of indiscriminately enlarging them, which is particularly essential for improving the segmentation accuracy of the tumor with large scale variation. Second, we propose a novel yet efficient nonlocal context guidance (NCG) mechanism to capture global dependencies to tackle irregular shapes and blurring boundaries of kidneys and tumors. Instead of directly harnessing a 3-D NCG mechanism, which makes the number of parameters exponentially increase and hence the network difficult to be trained under limited training data, we develop a 2.5D NCG mechanism based on projections of feature cubes, which achieves a tradeoff between segmentation accuracy and network complexity. We extensively evaluate the proposed 3DSN-Net on the famous KiTS dataset with many challenging kidney and tumor cases. Experimental results demonstrate our solution consistently outperforms state-of-the-art 3-D networks after being equipped with scale aware and NCG mechanisms, particularly for tumor segmentation.

Preclinical evaluation of AGTR1-Targeting molecular probe for colorectal cancer imaging in orthotopic and liver metastasis mouse models

Despite advancements in colorectal cancer (CRC) treatment, the prognosis remains unfavorable for patients with distant liver metastasis. Fluorescence molecular imaging with specific probes is increasingly used to guide CRC surgical resection in real-time and treatment planning. Here, we demonstrate the targeted imaging capacity of an MPA-PEG4-N3-Ang II probe labeled with near-infrared (NIR) fluorescent dye targeting the angiotensin II (Ang II) type 1 receptor (AGTR1) that is significantly upregulated in CRC. MPA-PEG4-N3-Ang II was highly selective and specific to in vitro tumor cells and in vivo tumors in a mouse CRC xenograft model. The favorable ex vivo imaging and in vivo biodistribution of MPA-PEG4-N3-Ang II afforded tumor-specific accumulation with low background and >10 contrast tumor-to-colorectal values in multiple subcutaneous CRC models at 8 h following injection. Biodistribution analysis confirmed the probe's high uptake in HT29 and HCT116 orthotopic and liver metastatic models of CRC with signal-to-noise ratio (SNR) values of tumor-to-colorectal and -liver fluorescence of 5.8 ± 0.6, 5.3 ± 0.7, and 2.7 ± 0.5, 2.6 ± 0.5, respectively, enabling high-contrast intraoperative tumor visualization for surgical navigation. Given its rapid tumor targeting, precise tumor boundary delineation, durable tumor retention and docking study, MPA-PEG4-N3-Ang II is a promising high-contrast imaging agent for the clinical detection of CRC.

Role of sodium fluorescein in pediatric low-grade glioma surgery: an update.

Complete surgical resection is still the mainstay in the treatment of central nervous system low-grade tumors, eventually resulting curative. The complete surgical removal of these lesions, however, may be difficult in some cases because of their infiltrative nature. Intraoperative adjuncts may be a game changer. Sodium fluorescein (SF) is among the ideal candidates as intraoperative tools to favor the actual recognition of the tumor extension, since it accumulates in areas of altered blood-brain barrier, a typical characteristic of pediatric gliomas, and has a low rate of adverse events. This work proposes an update of previous works about the evaluation of the feasibility and usefulness of a systematic use of SF in a low-grade lesion group of pediatric patients. Pediatric patients operated on for a resection or a biopsy of a low-grade glial or glioneuronal lesion (WHO grade I and II) at our Institution between September 2021 and December 2023, with the intraoperative use of sodium fluorescein (SF), were enrolled in the study. We collected pre-operative and postoperative clinical and radiological data, intraoperative findings, and post-operative pathological diagnoses. No adverse events were registered related to the intraoperative use of SF. SF appeared useful for the localization of boundaries of tumors, especially when characterized by a high degree of infiltration or by a deep-seated location, and for the checking of possible tumor remnants at the end of surgery. A good tumor-to-healthy tissue contrast was registered when tumor visualization was in a range between 1 to 2h and 30min after SF injection. Possible "false positives" due to intraoperative vascular wall injury and clearance of SF from both tumor and healthy tissue were observed in some cases and still remain open issues. SF is a feasible and safe intraoperative adjunct tool in the surgical removal of pediatric low-grade tumors. SF may show its usefulness especially in selected cases, such as deep-seated lesions and infiltrating tumors. Its safety profile, user-friendly management, and potential utility in both tumor resections and neuronavigated biopsies favor its wider use in the surgical treatment of pediatric low-grade tumors.

A novel approach for estimating lung tumor motion based on dynamic features in 4D-CT

Due to the high expenses involved, 4D-CT data for certain patients may only include five respiratory phases (0%, 20%, 40%, 60%, and 80%). This limitation can affect the subsequent planning of radiotherapy due to the absence of lung tumor information for the remaining five respiratory phases (10%, 30%, 50%, 70%, and 90%). This study aims to develop an interpolation method that can automatically derive tumor boundary contours for the five omitted phases using the available 5-phase 4D-CT data. The dynamic mode decomposition (DMD) method is a data-driven and model-free technique that can extract dynamic information from high-dimensional data. It enables the reconstruction of long-term dynamic patterns using only a limited number of time snapshots. The quasi-periodic motion of a deformable lung tumor caused by respiratory motion makes it suitable for treatment using DMD. The direct application of the DMD method to analyze the respiratory motion of the tumor is impractical because the tumor is three-dimensional and spans multiple CT slices. To predict the respiratory movement of lung tumors, a method called uniform angular interval (UAI) sampling was developed to generate snapshot vectors of equal length, which are suitable for DMD analysis. The effectiveness of this approach was confirmed by applying the UAI-DMD method to the 4D-CT data of ten patients with lung cancer. The results indicate that the UAI-DMD method effectively approximates the lung tumor’s deformable boundary surface and nonlinear motion trajectories. The estimated tumor centroid is within 2 mm of the manually delineated centroid, a smaller margin of error compared to the traditional BSpline interpolation method, which has a margin of 3 mm. This methodology has the potential to be extended to reconstruct the 20-phase respiratory movement of a lung tumor based on dynamic features from 10-phase 4D-CT data, thereby enabling more accurate estimation of the planned target volume (PTV).

Pre-operative enhanced magnetic resonance imaging combined with clinical features predict early recurrence of hepatocellular carcinoma after radical resection.

Indentifying predictive factors for postoperative recurrence of hepatocellular carcinoma (HCC) has great significance for patient prognosis. To explore the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) combined with clinical features in predicting early recurrence of HCC after resection. A total of 161 patients with pathologically confirmed HCC were enrolled. The patients were divided into early recurrence and non-early recurrence group based on the follow-up results. The clinical, laboratory, pathological results and Gd-EOB-DTPA enhanced MRI imaging features were analyzed. Of 161 patients, 73 had early recurrence and 88 were had non-early recurrence. Univariate analysis showed that patient age, gender, serum alpha-fetoprotein level, the Barcelona Clinic Liver Cancer stage, China liver cancer (CNLC) stage, microvascular invasion (MVI), pathological satellite focus, tumor size, tumor number, tumor boundary, tumor capsule, intratumoral necrosis, portal vein tumor thrombus, large vessel invasion, nonperipheral washout, peritumoral enhancement, hepatobiliary phase (HBP)/tumor signal intensity (SI)/peritumoral SI, HBP peritumoral low signal and peritumoral delay enhancement were significantly associated with early recurrence of HCC after operation. Multivariate logistic regression analysis showed that patient age, MVI, CNLC stage, tumor boundary and large vessel invasion were independent predictive factors. External data validation indicated that the area under the curve of the combined predictors was 0.861, suggesting that multivariate logistic regression was a reasonable predictive model for early recurrence of HCC. Gd-EOB-DTPA enhanced MRI combined with clinical features would help predicting the early recurrence of HCC after operation.

Small Bowel Gastrointestinal Stromal Tumors: The Value of CT Enterography in Assessing Pathological Aggressiveness.

This study aimed to characterize the computed tomography (CT) enterography features of the small bowel gastrointestinal stromal tumors (GIST) and to determine the association with pathological aggressiveness. Computed tomography enterography images of 30 patients with the histologically confirmed small bowel GIST were retrospectively enrolled. Tumor size, location, border, growth pattern, enhancement pattern, necrosis, calcification, ulceration, internal air, nodal metastasis, liver metastasis, peritoneal metastasis, and draining vein were evaluated. Relationships between imaging features and pathological aggressiveness were analyzed using χ 2 test or Fisher exact test. Correlations among CT features were analyzed using Spearman correlation analysis. There were significant differences in tumor size between different risk levels ( F = 8.388, P < 0.001). There were statistically significant differences in the 5 imaging manifestations of necrosis, ulcer, tumor boundary, drainage vein, and intratumoral gas ( P < 0.05). There was a significant negative correlation between tumor size and enhancement type as well as clear tumor boundary. There was a significant positive correlation between tumor size and necrosis, ulcer, drainage vein, intratumoral gas, liver metastasis, and peritoneal metastasis. Some CT enterography imaging features might be useful in the determination of the pathological aggressiveness in the patients with small bowel GIST.

Brain tumor detection using proper orthogonal decomposition integrated with deep learning networks

Background and ObjectiveThe central organ of the human nervous system is the brain, which receives and sends stimuli to the various parts of the body to engage in daily activities. Uncontrolled growth of brain cells can result in tumors which affect the normal functions of healthy brain cells. An automatic reliable technique for detecting tumors is imperative to assist medical practitioners in the timely diagnosis of patients. Although machine learning models are being used, with minimal data availability to train, development of low-order based models integrated with machine learning are a tool for reliable detection. MethodsIn this study, we focus on comparing a low-order model such as proper orthogonal decomposition (POD) coupled with convolutional neural network (CNN) on 2D images from magnetic resonance imaging (MRI) scans to effectively identify brain tumors. The explainability of the coupled POD-CNN prediction output as well as the state-of-the-art pre-trained transfer learning models such as MobileNetV2, Inception-v3, ResNet101, and VGG-19 were explored. ResultsThe results showed that CNN predicted tumors with an accuracy of 99.21% whereas POD-CNN performed better with about 1/3rd of computational time at an accuracy of 95.88%. Explainable AI with SHAP showed MobileNetV2 has better prediction in identifying the tumor boundaries. ConclusionsIntegration of POD with CNN is carried for the first time to detect brain tumor detection with minimal MRI scan data. This study facilitates low-model approaches in machine learning to improve the accuracy and performance of tumor detection.

Enhancing the Extent of Resection in Glioma Surgery Through the Integration of Intraoperative Contrast-Enhanced Ultrasound and Fluorescein Sodium

Due to the infiltrative nature and high local recurrence of gliomas, particularly high-grade gliomas, gross total resection (GTR) of a tumor is the first critical step in treatment. This study aimed to determine whether the integration of intraoperative contrast-enhanced ultrasound (CEUS) and fluorescein sodium can improve the identification of tumor boundaries and residuals, and increasethe extent of resection (EOR) to better protect neurological function. We retrospectively analysed clinical data from 87 glioma surgeries and categorised the patients into 3 groups: CEUS plus fluorescein sodium, fluorescein sodium alone and microsurgery alone. In terms of EOR, GTR was achieved in 22 (91.7%) patients in the CEUS plus fluorescein sodium group, which was significantly higher than that in other groups. In the subgroup analysis of tumors with lobulated or satellite lesions and WHO grade III or IV gliomas, CEUS plus fluorescein sodium group showed the highest GTR (86.7% and 88.9% respectively) among the groups. Logistic regression analysis of factors that may affect the GTR of tumors showed that the functional areas involvement and the presence of lobulated or satellite lesions were risk factors, whereas CEUS plus fluorescein sodium group was a protective factor. However, CEUS plus fluorescein sodium group had the longest surgery time. Intraoperative CEUS with fluorescein sodium is a real-time, straightforward, safe, and effective approach to perform surgical resection of gliomas. This approach assists surgeons in identifying tumor boundaries, residual tumors, and normal brain parenchyma, which increases the EOR.

Analysis of surgical approaches and mandibular treatment strategies for different types of tumors involving parapharyngeal space

In view of the surgical complexity of parapharyngeal space tumors involved, this paper summarized the disease data of patients with parapharyngeal space tumors involved in the Department of Oral and Maxillofacial Surgery, the First Hospital of Shanxi Medical University from January 2015 to January 2021. It also summarized the surgical approach and mandibular management, so as to explore surgical strategies for different characteristics of parapharyngeal space tumors involved. A total of 49 patients, including 28 males and 21 females, median age 52 years (range 24-72 years). They were treated with four surgical approaches for tumor resection, 25 cervical approach, 5 cheek and neck approach, 3 transoral approach, and 16 cervical-maxillary approach. Among the patients treated with cervical-maxillary approach, 3 patients were treated with mandible square resection, and 6 patients were treated with temporary mandible dissection. Seven cases were treated with tumor resection and partial mandibular resection. There are various surgical approaches and mandibular management methods involving tumors in the parapharyngeal space, and clinical decisions should be made based on tumor diameter, location, boundary, blood supply and pathological types.

A review of clinical use of surface-enhanced Raman scattering-based biosensing for glioma.

Glioma is the most common malignant tumor of the nervous system in recent centuries, and the incidence rate of glioma is increasing year by year. Its invasive growth and malignant biological behaviors make it one of the most challenging malignant tumors. Maximizing the resection range (EOR) while minimizing the impact on normal brain tissue is crucial for patient prognosis. Changes in metabolites produced by tumor cells and their microenvironments might be important indicators. As a powerful spectroscopic technique, surface-enhanced Raman scattering (SERS) has many advantages, including ultra-high sensitivity, high specificity, and non-invasive features, which allow SERS technology to be widely applied in biomedicine, especially in the differential diagnosis of malignant tumor tissues. This review first introduced the clinical use of responsive SERS probes. Next, the sensing mechanisms of microenvironment-responsive SERS probes were summarized. Finally, the biomedical applications of these responsive SERS probes were listed in four sections, detecting tumor boundaries due to the changes of pH-responsive SERS probes, SERS probes to guide tumor resection, SERS for liquid biopsy to achieve early diagnosis of tumors, and the application of free-label SERS technology to detect fresh glioma specimens. Finally, the challenges and prospects of responsive SERS detections were summarized for clinical use.

Delineation of three-dimensional tumor margins based on normalized absolute difference mapping via volumetric optical coherence tomography

The extent of surgical resection is an important prognostic factor in the treatment of patients with glioblastoma. Optical coherence tomography (OCT) imaging is one of the adjunctive methods available to achieve the maximal surgical resection. In this study, the tumor margins were visualized with the OCT image obtained from a murine glioma model. A commercialized human glioblastoma cell line (U-87) was employed to develop the orthotopic murine glioma model. A swept-source OCT (SS-OCT) system of 1300 nm was used for three-dimensional imaging. Based on the OCT intensity signal, which was obtained via accumulation of each A-scan data, an en-face optical attenuation coefficient (OAC) map was drawn. Due to the limited working distance of the focused beam, OAC values decrease with depth, and using the OAC difference in the superficial area was chosen to outline the tumor boundary, presenting a challenge in analyzing the tumor margin along the depth direction. To overcome this and enable three-dimensional tumor margin detection, we converted the en-face OAC map into an en-face difference map with x- and y-directions and computed the normalized absolute difference (NAD) at each depth to construct a volumetric NAD map, which was compared with the corresponding H&E-stained image. The proposed method successfully revealed the tumor margin along the peripheral boundaries as well as the margin depth. We believe this method can serve as a useful adjunct in glioma surgery, with further studies necessary for real-world practical applications.

Preliminary application of indocyanine green fluorescence imaging technology in nasal endoscopic tumor surgery

Objective:To preliminarily study the practical value of Indocyanine green（ICG） molecular fluorescence imaging technology in nasal endoscopic tumor surgery. Methods:Five patients with tumors related to nasal sinuses, orbital wall and skull base in the Department of Otolaryngology head and Neck Surgery, General Hospital of Xinjiang Military Command from December 2022 to April 2023 were enrolled. Among them, 3 were benign tumors and 2 were malignant tumors. All patients underwent surgery under the guidance of ICG molecular fluorescence imaging. ICG was administered intravenously through cubital vein at a dose of 0.5 mg/kg 12 to 24 h before surgery. Tumors were labeled by fluorescence imaging during the operation. surgeons cleared the tumor tissue strictly according to the labeled range and depth, malignant tumors were further expanded and cleaned according to pathology results. Results:All 5 patients achieved accurate tumor localization with the aid of fluorescence imaging technology. Resections were performed with reference to fluorescent labeling boundaries, all patients achieved complete tumor cleanup or negative margins. Conclusion:For tumor-related surgery under nasal endoscopy, ICG molecular fluorescence imaging technology can not only achieve accurate real-time positioning, but also provide evidence for surgeons to judge tumor boundaries. Therefore, we believe that the technology should have certain practical value in nasal endoscopic tumor surgery.

Brain Tumor Detection Using Classification Model

This study aims to develop automated methods for detecting and segmenting brain tumors in MRI scans using neural networks and conventional image processing. Accurately identifying tumor location and boundaries is crucial for diagnosis and determining the most effective treatment approach. Manual review by radiologists is time-consuming and subjective. However, recent advances in deep learning now enable more efficient analysis of large MRI datasets.&#x0D; A variety of neural network models have been explored for tumor segmentation, including recurrent networks, autoencoders and convolutional networks. RNNs are well-suited to capture contextual information across sequential MRI slices. Autoencoders perform unsupervised feature learning from the images. CNNs have shown promise due to their ability to directly learn visual patterns from pixel values. Unsupervised clustering techniques such as fuzzy c-means and k-means are also investigated to group tumor pixels based on intensity similarity without labeled data.&#x0D; Previous research combining these techniques has achieved high segmentation accuracy between 84-97% using additional image processing steps. Automated segmentation provides benefits over manual inspection such as reduced workload and more consistent delineation of tumor margins. It can precisely outline growths to aid radiologists in diagnosis and pathologists in assessing malignancy.&#x0D; The detection and segmentation of brain tumors has applications in both radiology and oncology. Radiologists could benefit from faster scan review and automated report generation. Neurosurgeons would gain insights into a tumor's size, location and infiltration to determine the most suitable surgical approach. Oncologists could utilize quantitative tumor features to select the most targeted radiation therapy or chemotherapy protocols. Overall, continued advances in deep and machine learning hold promise to improve diagnosis and management of brain cancer patients.

Enhancing Brain Tumor Diagnosis: A Synergistic Approach with Support Vector Machines and Decision Trees for Improved Detection and Segmentation

Abstract: Brain tumor identification and segmentation are critical components of modern medical imaging, facilitating timely diagnoses and effective therapy planning. This paper introduces an innovative methodology that harmoniously integrates Support Vector Machines (SVM) and Decision Tree methods. SVM adeptly classifies brain images into tumor and nonneoplastic regions, harnessing its proficiency with complex datasets. Decision Tree methods complement this process, providing transparent segmentation that accurately delineates intricate tumor boundaries. The synergy between SVM and Decision Tree methods establishes a robust framework for brain tumor identification. This novel approach holds promise for enhanced diagnosis speed, particularly valuable in time-sensitive medical scenarios. Emphasizing potential advancements in patient care outcomes, the methodology enables tailored treatment plans with greater precision. Positioned at the core of brain image analysis, this novel methodology signifies a substantial step forward in healthcare practices, fostering personalized approaches to brain tumor diagnosis and treatment.

A Real-time augmented reality robot integrated with artificial intelligence for skin tumor surgery - experimental study and case series.

Skin tumors affect many people worldwide, and surgery is the first treatment choice. Achieving precise preoperative planning and navigation of intraoperative sampling remains a problem and is excessively reliant on the experience of surgeons, especially for Mohs surgery for malignant tumors. To achieve precise preoperative planning and navigation of intraoperative sampling, we developed a real-time augmented reality (AR) surgical system integrated with artificial intelligence (AI) to enhance three functions: AI-assisted tumor boundary segmentation, surgical margin design, and navigation in intraoperative tissue sampling. Non-randomized controlled trials were conducted on manikin, tumor-simulated rabbits, and human volunteers in xxx Laboratory to evaluate the surgical system. The results showed that the accuracy of the benign and malignant tumor segmentation were 0.9556 and 0.9548, respectively, and the average AR navigation mapping error was 0.644mm. The proposed surgical system was applied in 106 skin tumor surgeries, including intraoperative navigation of sampling in 16 Mohs surgery cases. Surgeons who have used this system highly recognize it. The surgical system highlighted the potential to achieve accurate treatment of skin tumors and to fill the gap in global research on skin tumor surgery systems.