Detection Of Metastases Research Articles

MRI-Enhanced metastatic ovarian tumour detection: leveraging enhanced 3D CNN and Data augmentation for exceptional accuracy

ABSTRACT Metastatic Ovarian Tumour remains a challenging medical condition that occurs in the ovaries of female reproductive organs. This severe disease may reduce the life span of the affected person by causing serious effects like imbalanced hormones, improper digestive system, pelvic pain and fertility issues leading to depression. The proposed model employs a 3D CNN architecture, proven effective in image classification tasks, to analyse MRI scans. A few existing approaches have used 2D CNN to detect the tumour, but it is lacking in capturing spatial and temporal features of MRI, which leads to information loss. The MRI-based model achieved an impressive accuracy of 98.6%, showcasing its potential as a valuable tool for early diagnosis. This level of accuracy holds promise for clinical applications, allowing for timely interventions and improved patient life span in cases of ovarian cancer.

Diagnostic Confidence of Contrast-Enhanced T1-Weighted MRI for the Detection of Brain Metastases: 3D FSE-vs. 3D GRE-Based Sequences.

This retrospective study evaluated the utility of contrast-enhanced (CE) T1-weighted 3D fast spinecho-based SPACE sequences for brain metastasis detection on 3T MRI compared to gradient recalled-echo-based 3D fast low-angle shot (FLASH) sequence. We identified all patients at a single institution who underwent SPACE and 3D FLASH sequences as part of a practice quality improvement project. Their medical records were retrospectively reviewed. Five certified neuroradiologists reviewed the images, with at least 2 weeks separation between scoring sequences for the same patient. The following parameters were evaluated: number of metastatic lesions, number of indeterminate lesions, lesion margin, contrast-to-noise ratio (CNR), extent of image artifacts, and overall image quality. CNR was also quantified for solidly enhancing lesions > 1 cm. We identified 220 patients who underwent SPACE and 3D FLASH sequences (the order of the sequences was equally distributed). Of these, 79 had brain metastases on imaging, and 7 were excluded; thus, 72 patients were included in the study. Twenty patients were scored by 2 radiologists. Out of the 92 evaluations, SPACE detected more lesions than did 3D FLASH in 35, while 3D FLASH detected more lesions in 10. More indeterminate lesions were seen on 3D FLASH (27) than on SPACE (9). For lesion margin, CNR, and overall image quality on a Likert scale, SPACE performed significantly better than did 3D FLASH, with less image artifacts (P < 0.00001). Higher quantitative CNRs were found on SPACE than on 3D FLASH images, although this result was not statistically significant (median = 22.9 vs. 15.5, respectively, P = 0.134). There was a high inter-reader lesion detection concordance with Krippendorf's alpha ordinals at 0.962 for SPACE, 0.870 for 3D FLASH, and 0.918 for the two sequences combined. Compared with 3D FLASH, the SPACE sequence detected more metastatic lesions and was rated higher for image quality, lesion margin, and CNR, with fewer artifacts. Importantly, the SPACE sequence resulted in increased reader confidence, with fewer indeterminate lesions detected. FLASH = fast low-angle shot; FSE = fast spin-echo; GRE = gradient-recalled echo; MP-RAGE = magnetization-prepared rapid gradient echo; SPACE = Sampling Perfection with Application-optimized Contrasts using different flip angle Evolution; VIBE = volumetric interpolated breath-hold examination.

Enhancing detection of high-level axillary lymph node metastasis after neoadjuvant therapy in breast cancer patients with nodal involvement: a combined approach of axilla ultrasound and breast elastography.

To develop a combined approach using shear wave elastography (SWE) and conventional ultrasound (US) to determine the extent of positive axillary lymph nodes (LNs) following neoadjuvant therapy (NAT) in breast cancer patients with nodal involvement. This prospective, multicenter study was registered on the Chinese Clinical Trial Registry (ChiCTR2400085035). From October 2018 to February 2024, a total of 303 breast cancer patients with biopsy-proven positive LN were enrolled. The conventional US features of axillary LNs and SWE characteristics of breast lesions after NAT were analyzed. The diagnostic performances of axilla US, breast SWE, and their combination in detecting residual metastasis in axillary level III after NAT were assessed. Pathologically positive LN(s) in axilla level III were detected in 13.75% of cases following NAT. The kappa value for the axilla level with positive LN confirmed by surgical pathology and detected by US is 0.39 (p < 0.001). The AUC of conventional axilla US to determine the status of axilla level III LNs after NAT was 0.67, with a sensitivity of 51.52%, a specificity of 74.36%. The breast SWE displayed moderate performance for detecting residual metastasis in axilla level III following NAT, with an AUC of 0.79, sensitivity of 84.85%, and specificity of 74.36%. Compared to axilla US and breast SWE alone, the combination of axilla US with breast SWE achieved a stronger discriminatory ability (AUC, 0.86 vs 0.67 vs 0.79, p < 0.05, Delong's test) and precise calibration (X2 = 13.90, p = 0.085, HL test), with an improved sensitivity of 93.94% and a comparable specificity of 75.64%%. SWE outperformed conventional US in identifying the axilla levels with nodal metastasis following NAT in patients with initially diagnosed positive axilla. Furthermore, combining breast SWE with axilla US showed good diagnostic performance for detecting residual metastasis in axilla level III after NAT.

Metastasis Detection Using True and Artificial T1-Weighted Postcontrast Images in Brain MRI

Objectives Small lesions are the limiting factor for reducing gadolinium-based contrast agents in brain magnetic resonance imaging (MRI). The purpose of this study was to compare the sensitivity and precision in metastasis detection on true contrast-enhanced T1-weighted (T1w) images and artificial images synthesized by a deep learning method using low-dose images. Materials and Methods In this prospective, multicenter study (5 centers, 12 scanners), 917 participants underwent brain MRI between October 2021 and March 2023 including T1w low-dose (0.033 mmol/kg) and full-dose (0.1 mmol/kg) images. Forty participants with metastases or unremarkable brain findings were evaluated in a reading (mean age ± SD, 54.3 ± 15.1 years; 24 men). True and artificial T1w images were assessed for metastases in random order with 4 weeks between readings by 2 neuroradiologists. A reference reader reviewed all data to confirm metastases. Performances were compared using mid-P McNemar tests for sensitivity and Wilcoxon signed rank tests for false-positive findings. Results The reference reader identified 97 metastases. The sensitivity of reader 1 did not differ significantly between sequences (sensitivity [precision]: true, 66.0% [98.5%]; artificial, 61.9% [98.4%]; P = 0.38). With a lower precision than reader 1, reader 2 found significantly more metastases using true images (sensitivity [precision]: true, 78.4% [87.4%]; artificial, 60.8% [80.8%]; P &lt; 0.001). There was no significant difference in sensitivity for metastases ≥5 mm. The number of false-positive findings did not differ significantly between sequences. Conclusions One reader showed a significantly higher overall sensitivity using true images. The similar detection performance for metastases ≥5 mm is promising for applying low-dose imaging in less challenging diagnostic tasks than metastasis detection.

Frequency of Positive Findings on MRI of The Patients Diagnosed with Vertebral Metastases on Bone Scintigraphy

Objective: The present study is conducted with the aim to compare the bone scan and MRI findings for detecting spinal metastasis in our local population. Study design and duration: This cross sectional study was conducted at Radiology Department of Pakistan Institute of medical Sciences, Islamabad from May 2019-May 2020. Material and Methods: Total 30patients with confirmed diagnosis of cancer who were suspected for spinal metastasis on Tc-99m bone scan and sent for radiologic workup for confirm diagnosis of s pine lesions were enrolled. MRI was performed on 1.5-tesla system using standard protocol. Spine was divided into four regions: cervical, thoracic, lumber and sacral regions. Region-wise bone metastasis were analyzed and compared for bone scan and MRI. Interpretations were considered concordant if both reports showed positive for metastatic lesions, while discordant if readings differ. Results: 63.3% of the study population were female. Mean age of the enrolled patients was 59.3±14.9 years. All the lesions of cervical region were concordant. Most of the discordance for MRI was noted for lumbar region, where 7 patients were reported as negative who were positive on bone scan. In case of bone scintigraphy, 2 patients were discordant for thoracic region who were positive on MRI. Conclusion: Very low discordance rate exists among MRI and bone scan. MRI is an effective imaging tool for detection of spinal metastasis with added benefits of no radiation dose to the patient and wider availability as compare to the bone scintigraphy.

A Novel Approach to Harnessing Acoustic A-Lines to Detect Circulating Tumor Cells in Flowing Blood.

Circulating tumor cells (CTCs) are associated with tumor burden and treatment response and, as hallmarks of the initiation of tumor dissemination, can predict the likelihood of metastatic progression before widespread tumors can be detected by standard anatomic imaging. However, early diagnosis of recurrence through the detection of CTCs is limited by their low prevalence in blood and the limited sensitivity of existing technologies. To address these challenges, we investigated the use of ultrasound and targeted microbubbles (MBs) for early CTC detection. While MBs have been used in cardiovascular/molecular tumor imaging, there is limited research on their acoustic properties when bound to CTCs. We developed a hydrophone system for detecting characteristic A-lines from CTCs encapsulating MBs. Our study is the first to identify distinctive characteristics in the acoustic frequency response of MBs bound to different cancer CTCs using in vitro suspensions and in vivo mice that will benefit metastatic cancer detection and management.

Abstract C013: Single cell analysis reveals tumor size-driven immune changes in primary breast tumors and corresponding lymph nodes

Abstract Introduction A sentinel lymph node is the primary node draining the tumor and is likely affected early in the metastatic process. The sentinel node holds a key position in the immune response against tumor in breast and represents a unique connection between the tumor and the host immune response. Detection of lymph node metastasis is important for staging a patient’s disease and is closely associated with prognosis. Although the clinical significance is debated, isolated tumor cells (&amp;lt;0.2 mm) appear to have no impact on survival while macro-metastasis (&amp;gt;2 mm) are linked to a poorer prognosis. Alterations in the immune response in lymph nodes with large versus small metastases have been observed, but little is known about the relationship between the immune response in the primary tumor and the metastatic status of the corresponding lymph nodes. Methods We analyzed single-cell suspensions from primary tumors and lymph nodes of 38 patients with early, operable breast cancer enrolled in the OSLO2 clinical observational trial using mass cytometry with a 47- antibody immune panel (including PanKeratin and EpCAM). In total 3.7 million single cells were analyzed and clustered into 40 metaclusters using FlowSOM. A second set of 22 metastatic lymph nodes from the OSLO2 cohort was used to validate results form the metastatic tumor cells. Additionally, immunofluorescence with E-cadherin and PanKeratin was applied, and intensity measured in 4 metastatic lymph nodes and imaging mass-cytometry was applied on one metastatic lymph node Results We found that the immune microenvironment in primary tumor was dominated by CD4 and CD8 T cells, particularly of exhausted and memory phenotype and with a higher frequency of activated regulatory T cells (Treg). In contrast, the immune composition in lymph nodes was predominantly composed of naturally occurring naive B and T cells. Notably, in line with previous publications we observed a skewing towards memory and exhausted phenotype in T cells, along with increased abundance of activated Treg in lymph nodes with larger metastasis to those with smaller or no metastasis. Furthermore, there were no correlation between the immune profile in the primary tumor and corresponding lymph node. The immune profile in the primary tumor was influenced by tumor size, with larger tumors showing a higher frequency of activated Tregs and exhausted CD8 T cells. Furthermore, In situ analysis revealed that the microenvironment in smaller metastase had an mesenchymal phenotype, while larger tumor deposits exhibited a more epithelial-like phenotype. ConclusionThe immune profile of the primary tumor did not predict the immune profile or metastatic status of the corresponding lymph node. Tumor size in both primary tumors and metastatic lymph nodes is the main drivers of changes in immune cell composition. Tumor cells from smaller metastases exhibited an mesenchymal phenotype, while larger metastasis displayed an epithelial phenotype. Citation Format: Inga Hansine H. Rye, Marit Otterlei Fjørtoft, Inger Riise Bergheim, Karin Teien Lande, Kanutte H. Huse, June Helen H. Myklebust, Ole Christian H. Lingjærde, Øystein. Garred, Jon H. Lømo, Hege H. Russnes. Single cell analysis reveals tumor size-driven immune changes in primary breast tumors and corresponding lymph nodes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr C013.

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.

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.