Detection Of Metastases Research Articles

Improved DeTraC Binary Coyote Net-Based Multiple Instance Learning for Predicting Lymph Node Metastasis of Breast Cancer From Whole-Slide Pathological Images.

Early detection of lymph node metastasis in breast cancer is vital for improving treatment outcomes and prognosis. This study introduces an Improved Decompose, Transfer, and Compose Binary Coyote Net-based Multiple Instance Learning (ImDeTraC-BCNet-MIL) method for predicting lymph node metastasis from Whole Slide Images (WSIs) using multiple instance learning. The method involves segmenting WSIs into patches using Otsu and double-dimensional clustering techniques. The developed multiple instance learning approach introduces a paradigm into computational pathology by shaping pathological data and constructing features. ImDeTraC-BCNet-MIL was utilised for feature generation during both training and testing to differentiate lymph node metastasis in WSIs. The proposed model achieves the highest accuracy of 95.3% and 99.8%, precision values of 98% and 99.8%, and recall rates of 92.9% and 99.8% on the Camelyon16 and Camelyon17 datasets. These findings underscore the effectiveness of ImDeTraC-BCNet-MIL in enhancing the early detection of lymph node metastasis in breast cancer.

Multi-omics with dynamic network biomarker algorithm prefigures organ-specific metastasis of lung adenocarcinoma.

Efficacious strategies for early detection of lung cancer metastasis are of significance for improving the survival of lung cancer patients. Here we show the marker genes and serum secretome foreshadowing the lung cancer site-specific metastasis through dynamic network biomarker (DNB) algorithm, utilizing two clinical cohorts of four major types of lung cancer distant metastases, with single-cell RNA sequencing (scRNA-seq) of primary lesions and liquid chromatography-mass spectrometry data of sera. Also, we locate the intermediate status of cancer cells, along with its gene signatures, in each metastatic state trajectory that cancer cells at this stage still have no specific organotropism. Furthermore, an integrated neural network model based on the filtered scRNA-seq data is successfully constructed and validated to predict the metastatic state trajectory of cancer cells. Overall, our study provides an insight to locate the pre-metastasis status of lung cancer and primarily examines its clinical application value, contributing to the early detection of lung cancer metastasis in a more feasible and efficacious way.

A novel electrochemiluminescent cytosensor using dual-target magnetic probe recognition and nanozymes-catalyzed cascade signal amplification for precise phenotypic enumeration of CTCs.

The inability of surgical biopsy to monitor the dynamic evolution of cancer cells hampers its capacity to reflect real-time tumor heterogeneity. Circulating tumor cells (CTCs), as a crucial target in liquid biopsy, offer a novel approach for accurate monitoring of tumors. However, the rarity and complex phenotype resulting from epithelial-mesenchymal transition pose challenges for conventional methods such as CellSearch and immunohistochemistry, which have insufficient ability for simultaneous phenotyping and enumeration of CTCs. The enumeration of a single phenotype CTCs is insufficient for accurately assessing disease progression. Herein, we propose a strategy to address this issue by fabricating an electrochemiluminescence cytosensor via the integration of dual-target enrichment and nanozymes-catalyzed cascade signal amplification. The graphene oxide@hollow mesoporous Prussian blue/Pt (GO@HMPB/Pt) complex, possessing a large specific surface area and exceptional catalytic activity, is employed for loading a substantial amount of luminol as the signal probe. Dual-target magnetic PPy@Fe3O4/Au-antibody/aptamer is utilized for the magnetic capture of both epithelial and interstitial CTCs. Glutathione (GSH) can disrupt theAu-S bond on aptamer by a thiol exchange reaction and selectively releases a specific subset of phenotypic CTCs, thereby facilitating the efficient capture, accurate classification, and ultrasensitive detection of CTCs in peripheral blood. Using the epithelial MCF-7 and mesenchymal Hela cells as models, the ECL cytosensor demonstrates excellent performance in identifying cells spiked into whole blood. This study presents a novel approach for early detection of metastasis, tracking tumor recurrence, and monitoring therapeutic efficacy.

Machine learning and deep learning models for preoperative detection of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis.

To evaluate the diagnostic performance of Machine Learning (ML) and Deep Learning (DL) models for predicting preoperative Lymph Node Metastasis (LNM) in Colorectal Cancer (CRC) patients. A systematic review and meta-analysis were conducted following PRISMA-DTA and AMSTAR-2 guidelines. We searched PubMed, Web of Science, Embase, and Cochrane Library databases until February 16, 2024. Study quality and risk of bias were assessed using the QUADAS-2 tool. Data were analyzed using STATA v18, applying random-effects models to all analyses. Twelve studies involving 8321 patients were included, with most published in 2021-2024 (9/12). The pooled AUC of ML models for predicting LNM in CRC patients was 0.87 (95% CI: 0.82-0.91, I2:86.17) with a sensitivity of 78% (95% CI: 69-87%) and a specificity of 77% (95% CI: 64%-90%). In addition, when assessing the AUC reported by radiologists, both junior and senior radiologists had similar performance, significantly lower than the ML models. (P < 0.001). Subgroup analysis revealed higher AUCs in prospective studies (0.95, 95% CI: 0.87-1) compared to retrospective studies (0.85, 95% CI: 0.81-0.89) (P = 0.03). Studies without external validation exhibited significantly higher AUCs than those with external validation (P < 0.01). While there was no significant difference in AUC and sensitivity between the T1-T2 and T2-T4 stages, specificity was significantly higher in the T2-T4 stages than the low stages of T1 and T2 (95%, 95% CI: 92-98% vs. 61%, 95% CI: 44-78%; P < 0.01). ML models demonstrate strong potential for preoperative LNM staging and treatment planning in CRC, potentially reducing the need for additional surgeries and related health and financial burdens. Further prospective multicenter studies, with standardized reporting of algorithms, modality parameters, and LNM staging, are needed to validate these findings.

In Vivo Detection of Lymph Nodes Metastasis of ESCC Using CXCR4-Targeted Tracer [64Cu]Cu-NOTA-CP01.

Esophageal squamous cell carcinoma (ESCC) frequently exhibits skip metastasis to lymph nodes; however, non-invasive imaging techniques capable of directly visualizing metastatic lymph nodes (MLN) are still lacking. Although biopsy is the clinical standard method, it is invasive and poses risks to patient health. This study aims to detect MLN in an intralymphatic tumor metastasis model of ESCC using the CXCR4-targeted tracer [64Cu]Cu-NOTA-CP01. The CXCR4 expression in ESCC cell lines was assessed using Western blot and immunofluorescence. An intralymphatic tumor metastasis model was established and monitored using bioluminescence imaging (BLI). Small animal PET studies and biodistribution studies were performed to evaluate the specificity of [64Cu]Cu-NOTA-CP01 for MLN. Histopathology evaluation was employed to check for the presence of metastatic tumor cells and to assess CXCR4 expression levels in the metastatic lymph nodes. The intralymphatic tumor metastasis model was successfully established using the EC109/Luc cell line, which exhibited high CXCR4 expression, as verified by BLI. PET/CT imaging showed that the MLN uptakes in the baseline group were significantly inhibited in the blocking group. The ratios of MLN/muscle and MLN/blood werealso significantly higher in the baseline group than in the blocking group. Ex vivo PET/CT imaging of MLN corroborated the in vivo data. Biodistribution studies further supported the PET imaging studies, showing rapid clearance of the tracer from the blood and major organs, with significantly higher MLN/muscle and MLN/blood ratios in the baseline group compared to the blocking group. Histopathological staining verified positive CXCR4 expression in these lymph nodes containing metastatic tumor cells. Targeting CXCR4 with [64Cu]Cu-NOTA-CP01 for PET imaging of lymph nodes metastasis represents a promising approach that warrants further investigation. These findings have the potential to enhance diagnostic and therapeutic strategies for individuals with lymph nodes metastasis of ESCC.

AI-Assisted Detection and Localization of Spinal Metastatic Lesions.

The integration of machine learning and radiomics in medical imaging has significantly advanced diagnostic and prognostic capabilities in healthcare. This study focuses on developing and validating an artificial intelligence (AI) model using U-Net architectures for the accurate detection and segmentation of spinal metastases from computed tomography (CT) images, addressing both osteolytic and osteoblastic lesions. Our methodology employs multiple variations of the U-Net architecture and utilizes two distinct datasets: one consisting of 115 polytrauma patients for vertebra segmentation and another comprising 38 patients with documented spinal metastases for lesion detection. The model demonstrated strong performance in vertebra segmentation, achieving Dice Similarity Coefficient (DSC) values between 0.87 and 0.96. For metastasis segmentation, the model achieved a DSC of 0.71 and an F-beta score of 0.68 for lytic lesions but struggled with sclerotic lesions, obtaining a DSC of 0.61 and an F-beta score of 0.57, reflecting challenges in detecting dense, subtle bone alterations. Despite these limitations, the model successfully identified isolated metastatic lesions beyond the spine, such as in the sternum, indicating potential for broader skeletal metastasis detection. The study concludes that AI-based models can augment radiologists' capabilities by providing reliable second-opinion tools, though further refinements and diverse training data are needed for optimal performance, particularly for sclerotic lesion segmentation. The annotated CT dataset produced and shared in this research serves as a valuable resource for future advancements.

Clinical performance of a simulated abbreviated liver MRI in combination with contrast enhanced CT for the baseline evaluation of the liver in patients with colorectal cancer

ObjectivesTo assess the diagnostic accuracy and inter-reader agreement of a simulated abbreviated gadoxetate liver MRI protocol together with CE-CT against a standard gadoxetate MRI for the detection of colorectal liver metastases at baseline. MethodsThree readers independently evaluated two sets of images per patient, recording number and location of metastases and benign lesions. Set 1 comprised T1w, T2w, DWI, multiphase CE-T1w and HPB images (standard). Set 2 included T2w, HBP, DWI (from Set 1) and CE-CT (simulated abbreviated). Diagnostic performance was compared using McNemar’s test. Level of agreement between sets 1 and 2 was determined with Cohen kappa. For agreement in the number of benign lesions and metastases, we applied ICC. Results75 patients (245 metastases, 122 benign lesions) were evaluated. There was no significant difference in diagnostic accuracy between set 1 and 2 for each reader (mean P=0.74). The total number of metastases and benign lesions showed high agreement between reading set 1 and 2 (κ = 0.81, 0.78). The total number of metastases showed substantial agreement between readers for set 1 and 2 (ICC = 0.99, 0.99). Good agreement was seen for metastatic segmental involvement (κ = 0.84–0.99). ConclusionsAt baseline, using a simulated abbreviated liver MRI together with CE-CT showed excellent agreement with standard MRI protocol for liver metastasis detection.

Staging Accuracy and Prognostic Value of Prostate-Specific Membrane Antigen PET/CT Strongly Depends on Lymph Node Tumor Burden.

Objectives: To explore the factors affecting the lymph node metastasis (LNM) detection performance of prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) and to evaluate its prognostic value for biochemical recurrence after radical prostatectomy (RP). Methods: Patients who had intermediate- or high-risk prostate cancer and underwent robot-assisted (RA)RP between 2017 and 2021 were included. Initial lymph node staging was carried out using PSMA PET/CT. Sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values were calculated. A cut-off value for LNM tumor deposit size that maximizes specificity was investigated and a post hoc specificity analysis was carried out. In survival analysis for biochemical progression-free survival (bPFS) after RP, Kaplan-Meier curves of molecular imaging (mi)N0 and miN1 patients were compared using the log-rank test and separate Cox regression models were developed to reveal the significance of PSMA PET/CT staging in pre- and post-surgery settings. Results: In 583 patients with a prevalence of pathology-proven LNM of 27.4%, overall sensitivity, specificity, PPV, and NPV of PSMA PET/CT per patient were 26.3% [95%CI 18.9-35.5], 93.9% [95%CI 84.9-100], 61.8% [95%CI 44.5-83.5], and 77.1% [95%CI 69.7-85.1], respectively. PSMA PET/CT showed a better sensitivity as LNM tumor deposit size increased (p = 0.003 OR 2.4 [95%CI 1.3-4.4]) and a better specificity in pT3-4 tumors (96.1%) versus pT2 (91.1%, p = 0.024 OR 2.7 [95%CI 1.1-6.3]). After adjustment according to 5.5 mm LNM tumor deposit size, which showed the best discriminative performance (AUC: 0.905 [95%CI 0.804-1.000, p < 0.001]), overall sensitivity tripled (90.2%, p < 0.001). The 1-year bPFS was 56.0% and 83.3% for miN1 and miN0 patients, respectively (p < 0.001). Whereas miN0pN1 was not, miN1pN1 disease was independently associated with decreased bPFS (HR:2.1 95%CI 1.3-3.4, p < 0.001). Conclusions: PSMA PET/CT has a lymph node tumor burden-dependent and cohort-driven diagnostic ability but consequently a strong independent prognostic value for predicting biochemical recurrence after RARP.

Fisher discriminant analysis of multimodal ultrasound in diagnosis of cervical metastatic lymph nodes in papillary thyroid cancer.

The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher's stepwise discriminant analysis. The model's effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. The multimodal ultrasound diagnostic model, established through Fisher's stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Diagnostic accuracy of imaging modalities for detection of spinal metastases: a systematic review and meta-analysis.

Detecting spinal metastases is highly relevant in patients with oncological disorders as it can affect the staging and treatment of their disease. We aimed to evaluate the diagnostic performance of computed tomography (CT), magnetic resonance imaging (MRI), FDG positron emission tomography (PET)/CT, bone scintigraphy (BS), and single-photon emission computed tomography (SPECT) for spinal metastases detection. Medline, EMBASE, and Web of Science were systematically searched until March 2024 for diagnostic accuracy studies on spinal metastases detection (PROSPERO-registration: CRD42024540139). Data extraction and quality assessment using the QUADAS-2 tool were performed by two independent reviewers. Using bivariate random effects modeling, pooled sensitivities, specificities, and diagnostic odds ratios (DOR) were calculated, and hierarchical summary operating curves were constructed. Twenty-five studies (49 datasets), encompassing 3102 patients were included. Per-patient pooled sensitivities of CT, MRI, PET/CT, BS and SPECT were 70%, 93%, 82%, 75%, and 84%, respectively. Pooled specificities were 74%, 85%, 75%, 92%, and 81%, respectively. Per-lesion pooled sensitivities of CT, MRI, PET/CT, BS and SPECT were 76%, 91%, 92%, 77%, and 92%, respectively. Pooled specificities were 91%, 94%, 85%, 52%, and 86%, respectively. MRI had the highest DOR in per patient and lesion analyses. MRI had highest diagnostic accuracy for spinal metastases detection on patient and lesion level, suggesting a broader use in addition to the routine staging CT, at least in patients at high risk and where the detection of a spinal metastasis could alter therapy decisions. Herein, results should be considered with the limitations of each modality.

Survey on Breast Cancer analysis

Breast cancer constitutes 12% of all newly diagnosed cases globally, making it the most predominant type of cancer as a whole. Histopathology images, among all the medical image modalities, maintain the cancer's path and provide richer phenotypic relevant information. The patient's immune system is progressively predictable as a critical feature in defining the suitable treatment. Tumor-infiltrating lymphocytes (TILs), immune cells found within tumors, are emerging as key biomarkers in breast cancer. The detection of lymph node metastasis affects the management of patients with primary breast cancer significantly in terms of staging, treatment, and prognosis.Tumoour infiltrating lymphocytes score and lymph node metastasis identification plays a very important role in staging of breast cancer.This review aims to provide a comprehensive overview on how deep learning-based method is used to compute the Tumour-Infiltrating Lymphocytes (TILs) score and lymph node metastasis from breast cancer histopathological images.

Lymph nodal volume in head and neck malignancy: can adding a third dimension improve the detection of nodal metastasis?

BackgroundTo compare the diagnostic utility of computed tomography (CT) and ultrasonography (USG) for preoperative determination of metastatic lymph nodes.MethodsHead and neck squamous cell carcinoma (HNSCC) cases planned for neck dissection were included. All underwent preoperative neck USG and CT followed by histopathological evaluation after surgery. Sensitivity, specificity, positive and negative predictive value along with accuracy was calculated for each outcome variable. Efficacy was calculated by comparing these to postoperative histopathology. Both imaging modalities were compared with lymph nodal volume.ResultsEighty-seven lymph nodes were selected from 30 patients. Median volume on CT was 5412.15 mm3 for metastatic and 237.7 mm3 for benign nodes and on ultrasound, 4168.92 mm3 for metastatic and 233.1 mm3 for benign nodes (p < 0.001). Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for volume on USG were 81.8%, 98.7%, 96.6%, 90%, and 97.4% and on CT were 81.8%, 100%, 97.7%, 100%, and 97.4% respectively.ConclusionsLymph nodal volume had higher sensitivity and specificity for detection of metastatic cervical lymphadenopathy and individually or in combination should be included as one of the criteria in preoperative assessment of HNSCC.

Artificial Intelligence-Based Sentinel Lymph Node Metastasis Detection in Cervical Cancer.

Background/objectives: Pathological ultrastaging, an essential part of sentinel lymph node (SLN) mapping, involves serial sectioning and immunohistochemical (IHC) staining in order to reliably detect clinically relevant metastases. However, ultrastaging is labor-intensive, time-consuming, and costly. Deep learning algorithms offer a potential solution by assisting pathologists in efficiently assessing serial sections for metastases, reducing workload and costs while enhancing accuracy. This proof-of-principle study evaluated the effectiveness of a deep learning algorithm for SLN metastasis detection in early-stage cervical cancer. Methods: We retrospectively analyzed whole slide images (WSIs) of hematoxylin and eosin (H&E)-stained SLNs from early-stage cervical cancer patients diagnosed with an SLN metastasis with either H&E or IHC. A CE-IVD certified commercially available deep learning algorithm, initially developed for detection of breast and colon cancer lymph node metastases, was employed off-label to assess its sensitivity in cervical cancer. Results: This study included 21 patients with early-stage cervical cancer, comprising 15 with squamous cell carcinoma, five with adenocarcinoma, and one with clear cell carcinoma. Among these patients, 10 had macrometastases and 11 had micrometastases in at least one SLN. The algorithm was applied to evaluate H&E WSIs of 47 SLN specimens, including 22 that were negative for metastasis, 13 with macrometastases, and 12 with micrometastases in the H&E slides. The algorithm detected all H&E macro- and micrometastases with 100% sensitivity. Conclusions: This proof-of-principle study demonstrated high sensitivity of a deep learning algorithm for detection of clinically relevant SLN metastasis in early-stage cervical cancer, despite being originally developed for adenocarcinomas of the breast and colon. Our findings highlight the potential of leveraging an existing algorithm for use in cervical cancer, warranting further prospective validation in a larger population.

Long-Term Outcomes of Prostate-Specific Membrane Antigen–PET Imaging of Recurrent Prostate Cancer

Although prostate-specific membrane antigen positron emission tomography (PSMA-PET) has shown improved sensitivity and specificity compared with conventional imaging for the detection of biochemical recurrent (BCR) prostate cancer, the long-term outcomes of a widespread shift in imaging are unknown. To estimate long-term outcomes of integrating PSMA-PET into the staging pathway for recurrent prostate cancer. This decision analytic modeling study simulated outcomes for patients with BCR following initial definitive local therapy. Inputs used were from the literature and a retrospective cohort study conducted at 2 institutions. The base case analysis assumed modest benefits of earlier detection and treatment, and scenario analyses considered prostate-specific antigen (PSA) level at imaging and different outcomes of earlier vs delayed treatment. The analysis was performed between April 1, 2023, and May 1, 2024. (1) Immediate PSMA-PET imaging, (2) conventional imaging (computed tomography and bone scan [CTBS]) followed by PSMA-PET if CTBS findings were negative or equivocal, and (3) CTBS alone. The main outcomes were detection of metastases, deaths from prostate cancer, and life-years and quality-adjusted life-years (QALYs) gained. The model estimated that per 1000 simulated patients with BCR (assumed median age, 66 years), PSMA-PET is expected to diagnose 611 (95% uncertainty interval [UI], 565-656) patients with metastasis compared with 630 (95% UI, 586-675) patients diagnosed using CTBS followed by PSMA-PET and 297 (95% UI, 202-410) patients diagnosed using CTBS alone. Moreover, the estimated number of prostate cancer deaths was 512 (95% UI, 472-552 deaths) with PSMA-PET, 520 (95% UI, 480-559 deaths) with CTBS followed by PSMA-PET, and 587 (95% UI, 538-632 deaths) with CTBS alone. Imaging with PSMA-PET yielded the highest number of QALYs, which were 824 (95% UI, 698-885) higher than CTBS. These results differed by PSA level at the time of testing, with the highest incremental life-years and QALYs and lowest number of deaths from prostate cancer among patients with PSA levels of at least 5.0 ng/mL. Finally, the estimates were sensitive to the expected benefit of initiating therapy for recurrent prostate cancer earlier in the disease course. The results of this decision-analytic model suggest that upfront PSMA-PET imaging for the evaluation of BCR is expected to be associated with reduced cancer mortality and gains in life-years and QALYs compared with the conventional imaging strategy, assuming modest benefits of earlier detection and treatment.

MFP-YOLO: a multi-scale feature perception network for CT bone metastasis detection.

Bone metastasis is one of the most common forms of metastasis in the late stages of malignancy. The early detection of bone metastases can help clinicians develop appropriate treatment plans. CT images are essential for diagnosing and assessing bone metastases in clinical practice. However, early bone metastasis lesions occupy a small part of the image and display variable sizes as the condition progresses, which adds complexity to the detection. To improve diagnostic efficiency, this paper proposes a novel algorithm-MFP-YOLO. Building on the YOLOv5 algorithm, this approach introduces a feature extraction module capable of capturing global information and designs a new content-aware feature pyramid structure to improve the network's capability in processing lesions of varying sizes. Moreover, this paper innovatively applies a transformer-structure decoder to bone metastasis detection. A dataset comprising 3921 CT images was created specifically for this task. The proposed method outperforms the baseline model with a 5.5% increase in precision and a 7.7% boost in recall. The experimental results indicate that this method can meet the needs of bone metastasis detection tasks in real scenarios and provide assistance for medical diagnosis.

Early experience with an artificial intelligence-based module for brain metastasis detection and segmentation.

- Accurate detection, segmentation, and volumetric analysis of brain lesions are essential in neuro-oncology. Artificial intelligence (AI)-based models have improved the efficiency of these processes. This study evaluated an AI-based module for detecting and segmenting brain metastases, comparing it with manual detection and segmentation. - MRIs from 51 patients treated with Gamma Knife radiosurgery for brain metastases were analyzed. Manual lesion identification and contouring on Leksell Gamma Plan at the time of treatment served as the gold standard. The same MRIs were processed through an AI-based module (Brainlab Smart Brush), and lesion detection and volumes were compared. Discrepancies were analyzed to identify possible sources of error. - Among 51 patients, 359 brain metastases were identified. The AI module achieved a sensitivity of 79.2% and a positive predictive value of 95.6%, compared to a 93.3% sensitivity for manual detection. However, for lesions > 0.1cc, the AI's sensitivity rose to 97.5%, surpassing manual detection at 93%. Volumetric agreement between AI and manual segmentations was high (Spearman's ρ = 0.997, p < 0.001). Most lesions missed by the AI (53.8%) were near anatomical structures that complicated detection. - The AI module demonstrated higher sensitivity than manual detection for metastases larger than 0.1cc, with robust volumetric accuracy. However, human expertise remains critical for detecting smaller lesions, especially near complex anatomical areas. AI offers significant potential to enhance neuro-oncology practice by improving the efficiency and accuracy of lesion management.

Phase 2 trial of PSMA PET CT versus planar bone scan and CT in prostate cancer patients progressing while on androgen deprivation therapy

For prostate cancer patients who experience biochemical progression during androgen deprivation therapy (ADT), prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) has not been prospectively compared to planar bone scan plus CT. This was a single-arm, head-to-head, prospective phase II trial (NCT04928820) designed to enroll 102 men with prostate cancer who experienced biochemical progression (rising prostate-specific antigen [PSA] ≥ 1 ng/mL) during ADT. All patients received 68Ga-PSMA-11 PET/CT and 99mTc-MDP planar bone scans. Each scan was interpreted by three central independent readers. The primary endpoint was the per-patient bone metastasis detection rate of PSMA PET/CT versus planar bone scan and CT. Secondary endpoints compared the number of bone metastases detected per patient and the inter-reader agreement of each imaging modality. Twenty-two men were enrolled between July 2021 and June 2022. Due to slow accrual following approval of PSMA PET radiotracers in the U.S. and a lack of a statistical signal between the two imaging modalities on interim analysis, this trial was closed early on October 2022. Median PSA was 8.5 ng/mL (interquartile range: 1.6–77.6). There was 100% agreement between the two scans. Six patients (27%) had negative findings and 16 patients (73%) had positive findings on both scans. PSMA PET/CT and bone scan plus CT detected an equal number of bone lesions for 14 patients (64%), PSMA PET/CT detected more bone lesions for six patients (27%), and bone scan plus CT detected more bone lesions for two patients (9.1%) (p = 0.092). The inter-reader agreement rates of PSMA PET/CT and bone scan plus CT were 96% and 82%, respectively (p = 0.25). In men with biochemical progression during ADT, 68Ga-PSMA-11 PET/CT and 99mTc-MDP planar bone scan plus CT had identical bone metastasis detection rates. Bone scan plus CT can continue to serve as a cost-effective and readily accessible restaging modality in patients with biochemical progression. ClinicalTrials.gov NCT04928820. Registered 16/06/2021.

Diagnostic value of [18F]FDG PET/MRI in the detection of lymph node metastasis of cervical cancer: a meta-analysis

Diagnostic value of [18F]FDG PET/MRI in the detection of lymph node metastasis of cervical cancer: a meta-analysis

Effect of Fluorescence Lymph Node Mapping on Improving Diagnostic Values of CT D3 Lymph Node Staging for Right-Sided Colon Cancer

Background/Objectives: This study evaluated the impact of fluorescence lymph node mapping (FLNM) using indocyanine green (ICG) on the diagnostic accuracy of preoperative computed tomography (CT) in right-sided colon cancer. Methods: A total of 218 patients who underwent laparoscopic right hemicolectomy with D3 lymph node dissection (LND) were analyzed: 86 patients in the FLNM group and 132 in the conventional surgery group. The FLNM technique allowed for enhanced intraoperative visualization of lymph node (LN) and more precise dissection, improving the identification of metastatic LNs. The diagnostic value of preoperative CT staging was assessed in both the FLNM and control groups by calculating the apparent prevalence, true prevalence, sensitivity, specificity, positive predictive value (PPV), negative predictive value, positive likelihood ratio (PLR), negative likelihood ratio, false positive and false negative proportions, and accuracy. Results: FLNM increased the accuracy of CT staging for detecting D3 LN metastasis in advanced cancer cases, with a higher PPV, PLR, and accuracy. In the FLNM group, the false-positive rate was significantly reduced, and the specificity was higher compared to the control group. Multivariate analysis identified FLNM as an independent factor associated with improved D3 LN metastasis detection. These findings suggest that incorporating FLNM into surgical procedures enhances the diagnostic value of preoperative CT by improving the precision of LND, particularly in patients with advanced colon cancer. Conclusions: The use of FLNM for D3 LND enhances the diagnostic accuracy of cN staging in right-sided colon cancer by improving surgical precision.

Clinical impact of whole-body MRI in staging and surveillance of patients with myxoid liposarcoma: a 14-year single-centre retrospective study.

To assess the clinical impact of regular whole-body magnetic resonance imaging (WBMRI) surveillance in myxoid liposarcoma patients. This was a retrospective cohort study of myxoid liposarcoma patients who underwent at least one WBMRI at our institution between October 2006 and December 2020. The effect of WBMRI on clinical management, namely treatment modification or additional diagnostic investigations was studied. A standardised WBMRI surveillance protocol was instituted in 2015. We compared patient outcomes for the metastatic patients who had and had not received regular WBMRI surveillance and performed survival analysis for both subgroups. Of the 56 patients (60.7% male, median age: 48.1 years) who underwent 345 WBMRI, 17 (30.3%) had metastases, and 168 WBMRI were performed in this group. The median imaging follow-up for the entire cohort was 35 months; the metastatic group had a median follow-up of 42 months. WBMRI changed the clinical management in 13 (76.5%) metastatic patients, with 33 instances of treatment modification. Thirty-five lesions were labelled 'indeterminate,' 16 (45.7%) had additional investigations/interventions, and 4 (11.4%) were confirmed to be metastatic. Twenty-one metastatic lesions were missed initially on WBMRI and confirmed on subsequent WBMRI, of which 5 (23.8%) were clinically significant. The 5-year survival since the detection of metastasis was better in the regular surveillance subgroup (85.7% vs. 45%), but this was not statistically significant (p = 0.068). Five patients (8.9%) developed their first metastasis more than 5 years after diagnosing the primary lesion. Regular WBMRI surveillance of myxoid liposarcoma patients considerably impacts clinical management by frequently influencing treatment decisions. WBMRI has been recently recommended as an imaging option for the staging and surveillance of myxoid liposarcoma patients. Our study highlights the impact of regular WBMRI surveillance on the clinical management of these patients and how it affects their survival.