Accuracy Of Cancer Diagnosis Research Articles

Coco-Attention for Tumor Segmentation in Weakly Paired Multimodal MRI Images

Multimodal magnetic resonance imaging (MRI) contains complementary information in anatomical and functional images that help the accurate diagnosis and treatment evaluation of lung cancers. However, effectively exploiting the complementary information in chest MRI images remains challenging due to the lack of rigorous registration. In this paper, a novel method is proposed that can effectively exploit the complementary information in weakly paired images for accurate tumor segmentation, namely coco-attention mechanism. Coco-attention module consists of two parts: the multi-modal co-attention (MultiCo-attn) and the multi-level coordinate attention (MultiCord-attn). The former aims to obtain tumor-aware deep features for accurate tumor localization, and the latter aims to highlight tumor area for more precise segmentation. Specifically, the MultiCo-attn extracts complementary information from multimodal high-dimensional semantic features using a bidirectional algorithm to generate attention maps focused on tumor region, and then uses the attention maps to enhance the feature representations. The MultiCord-attn leverages multi-level feature information to highlight tumor regions by adjusting the weight of each point in the feature. We evaluate the proposed method on lung tumor segmentation with a clinical dataset of 90 chest MRI scans of non-small cell lung cancer (NSCLC). The results show that the proposed method is effective for tumor segmentation in weakly paired images and achieves significant improvement (p < 0.005) over several commonly used multimodal segmentation methods. Furthermore, the ablation experiment results confirm the effectiveness and interpretability of the proposed coco-attention module.

Feature Selection Using a Hybrid Approach Depends on Filter and Wrapper Methods for Accurate Breast Cancer Diagnosis

Breast cancer is the biggest cause of mortality in women, outscoring all other malignancies. Diagnosing breast cancer is hard because the disease is complicated, treatment methods change, and there are many different kinds of patients. Information technology and artificial intelligence contribute to improve diagnostic procedures, which are critical for care and treatment as well as reducing and controlling cancer recurrence. The primary part of this research is to develop a new feature selection strategy based on a hybrid approach that combines two methods for selecting features: the filter and the wrapper. In two stages, this method reduces the number of features from 30 to 15 to increase and improve classification accuracy. The suggested method was tested using the Wisconsin Breast Cancer Dataset dataset (WDBC). To enhance the classification of breast cancer tumors, a soft voting classifier was used in this study. The proposed methodology outperforms previous research, achieving 1 for the F1 score, 1 for AUC, 1 for recall, 1 for precision, and 100% for accuracy. Furthermore, 10-fold cross-validation has a 98.2% accuracy rate.

Skin Cancer Detection Using Deep Learning-A Review.

Skin cancer is one the most dangerous types of cancer and is one of the primary causes of death worldwide. The number of deaths can be reduced if skin cancer is diagnosed early. Skin cancer is mostly diagnosed using visual inspection, which is less accurate. Deep-learning-based methods have been proposed to assist dermatologists in the early and accurate diagnosis of skin cancers. This survey reviewed the most recent research articles on skin cancer classification using deep learning methods. We also provided an overview of the most common deep-learning models and datasets used for skin cancer classification.

Biomimetic Theranostic Agents with Superior NIR-II Photoacoustic and Magnetic Resonance Imaging Performance for Targeted Photothermal Therapy of Prostate Cancer.

The accurate diagnosis and treatment of prostate cancer at an early stage is crucial to reduce mortality rates. However, the limited availability of theranostic agents with active tumor-targeting abilities hinders imaging sensitivity and therapeutic efficiency. To address this challenge, we have developed biomimetic cell membrane-modified Fe2O3 nanoclusters implanted in polypyrrole (CM-LFPP), achieving photoacoustic/magnetic resonance dual-modal imaging-guided photothermal therapy of prostate cancer. The CM-LFPP exhibits strong absorption in the second near-infrared window (NIR-II, 1000-1700 nm), showing high photothermal conversion efficiency of up to 78.7% under 1064 nm laser irradiation, excellent photoacoustic imaging capabilities, and good magnetic resonance imaging ability with a T2 relaxivity of up to 48.7 s-1 mM-1. Furthermore, the lipid encapsulation and biomimetic cell membrane modification enable CM-LFPP to actively target tumors, leading to a high signal-to-background ratio of ~30.2 for NIR-II photoacoustic imaging. Moreover, the biocompatible CM-LFPP enables low-dose (0.6 W cm-2) photothermal therapy of tumors under 1064 nm laser irradiation. This technology offers a promising theranostic agent with remarkable photothermal conversion efficiency in the NIR-II window, providing highly sensitive photoacoustic/magnetic resonance imaging-guided prostate cancer therapy.

CW-NET for Multi-type Cell Detection and Classification in Bone Marrow Examination and Mitotic Figure Examination.

Bone marrow examination is one of the most important indicators in diagnosing hematologic disorders and is typically performed under the microscope via oil-immersion objective lens with a total 100X objective magnification. On the other hand, mitotic detection and identification is critical not only for accurate cancer diagnosis and grading but also for predicting therapy success and survival. Fully automated bone marrow examination and mitotic figure examination from whole-slide images is highly demanded but challenging and poorly explored. Firstly, the complexity and poor reproducibility of microscopic image examination are due to the cell type diversity, delicate intra-lineage discrepancy within the multi-type cell maturation process, cells overlapping, lipid interference and stain variation. Secondly, manual annotation on whole-slide images is tedious, laborious and subject to intra-observer variability, which causes the supervised information restricted to limited, easily identifiable and scattered cells annotated by humans. Thirdly, when the training data is sparsely labeled, many unlabeled objects of interest are wrongly defined as background, which severely confuses AI learners. This paper presents an efficient and fully automatic CW-Net approach to address the three issues mentioned above and demonstrates its superior performance on both bone marrow examination and mitotic figure examination. The experimental results demonstrate the robustness and generalizability of the proposed CW-Net on a large bone marrow WSI dataset with 16,456 annotated cells of 19 bone marrow cell types and a large-scale WSI dataset for mitotic figure assessment with 262,481 annotated cells of five cell types. An online web-based system of the proposed method has been created for demonstration. (see https://youtu.be/MRMR25Mls1A). Supplementary data are available at Bioinformatics online.

Impact of MRI on the post-therapeutic diagnosis of T4 esophageal cancer.

Opportunities for T4b esophageal cancer patients to receive curative surgery are increasing with the development of multidisciplinary treatments. However, the best modality to accurately diagnose infiltration to the organs surrounding T4b esophageal cancer is still unknown. The aim of this study was to determine the performance of CT and MRI in diagnosing T stage in T4b esophageal cancer, with reference to the pathological diagnosis. A retrospective medical records review of patients with T4b esophageal cancer patients from January 2017 to December 2021 was conducted. Among 125 patients who were treated for cT4b esophageal cancer in Osaka University Hospital, 30 patients were diagnosed with cT4b esophageal cancer by CT, ycT staging with CT (contrast-enhanced images) and MRI (T2-FSE images), and curative R0 resection was performed. Preoperative MRI staging was independently performed by two experienced radiologists. The diagnostic performance of CT and MRI were examined using McNemar's test. Nineteen and 12 patients were diagnosed with ycT4b by CT and MRI, respectively. Combined T4b organ resection was performed in 15 patients. A pathological diagnosis of ypT4b was made in 11 cases. In comparison to CT, MRI showed a higher diagnostic performance, specificity (47% vs. 89%, p = 0.013), and accuracy (60% vs. 90%, p = 0.015) for CT vs. MRI. Our results-with reference to the pathological diagnosis-revealed that MRI had a superior diagnostic performance to CT for diagnosing T4b esophageal cancer invading the surrounding organs. An accurate diagnosis of T4b esophageal cancer may facilitate the implementation of appropriate treatment strategies.

Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework

We present an analysis and evaluation of breast cancer detection and diagnosis using segmentation models. We used an advanced semantic segmentation method and a deep convolutional neural network to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images. To improve the segmentation results, we used six models to analyse 309 patients, including 151 benign and 158 malignant tumour images. We compared the Unet3+ architecture with several other models, such as FCN, Unet, SegNet, DeeplabV3+ and pspNet. The Unet3+ model is a state-of-the-art, semantic segmentation architecture that showed optimal performance with an average accuracy of 82.53% and an average intersection over union (IU) of 52.57%. The weighted IU was found to be 89.14% with a global accuracy of 90.99%. The application of these types of segmentation models to the detection and diagnosis of breast cancer provides remarkable results. Our proposed method has the potential to provide a more accurate and objective diagnosis of breast cancer, leading to improved patient outcomes.

One Single Tube Reaction of Aptasensor-Based Magnetic Sensing System for Selective Fluorescent Detection of VEGF in Plasma.

In this study, a simple, easy and convenient fluorescent sensing system for the detection of the vascular endothelial growth factor (VEGF) based on VEGF aptamers, aptamer-complementary fluorescence-labeled probe and streptavidin magnetic beads was developed in one single tube. The VEGF is the most important biomarker in cancer, and it is investigated that the serum VEGF level varied according to the different types and courses of cancers. Hence, efficient quantification of VEGF is able to improve the accuracy of cancer diagnoses and the precision of disease surveillance. In this research, the VEGF aptamer was designed to be able to bind with the VEGF by forming G-quadruplex secondary structures; then, the magnetic beads would capture the non-binding aptamers due to non-steric interference; and finally, the fluorescence-labeled probes were hybridized with the aptamers captured by the magnetic beads. Therefore, the fluorescent intensity in the supernatant would specifically reflect the present VEGF. After an overall optimization, the optimal conditions for the detection of VEGF were as followed, KCl, 50 μM; pH 7.0; aptamer, 0.1 μM; and magnetic beads, 10 μL (4 μg/μL). The VEGF could be well quantified within a range of 0.2-2.0 ng/mL in plasma, and the calibration curve possessed a good linearity (y = 1.0391x + 0.5471, r = 0.998). The detection limit (LOD) was calculated to be 0.0445 ng/mL according to the formula (LOD = 3.3 × σ/S). The specificity of this method was also investigated under the appearance of many other serum proteins, and the data showed good specificity in this aptasensor-based magnetic sensing system. This strategy provided a simple, sensitive and selective biosensing platform for the detection of serum VEGF. Finally, it was expected that this detection technique can be used to promote more clinical applications.

Multimodal microscopic imaging with deep learning for highly effective diagnosis of breast cancer

Accurate and rapid diagnosis of breast cancer is particularly important since it becomes the primary malignant tumor threatening women's health. Although pathological diagnosis with H&E staining section is the gold standard in tumor diagnosis, it is hard to meet quick intraoperative inspection because of its complex procedures and hysteresis. This study adopts multimodal microscopic imaging technology (bright-field imaging, auto-fluorescence imaging and orthogonal polarization imaging) combined with deep learning to achieve rapid intelligent diagnosis of breast cancer by getting the rich information of tissue morphology, content and structure of collagen in tissue slices. By using both fusion classification models of multimodal images at pixel level and decision level, the AUC score and accuracy is 0.9366 and 89.01% for the pixel fusion classification, as well as 0.9421 and 87.53% for the decision fusion classification, respectively. The results suggest that the multimodal microscopic imaging technique proposed in this study has unique advantages in accuracy, speed and feasibility in clinic for breast cancer diagnosis, and has strong clinical potential and application prospects when combined with deep learning.

Unique Electron-Transfer-Mediated Electrochemiluminescence of AuPt Bimetallic Nanoclusters and the Application in Cancer Immunoassay.

Noble Metal nanoclusters (NCs) are promising electrochemiluminescence (ECL) emitters due to their amazing optical properties and excellent biocompatibility. They have been widely used in the detection of ions, pollutant molecules, biomolecules, etc. Herein, we found that glutathione-capped AuPt bimetallic NCs (GSH-AuPt NCs) emitted strong anodic ECL signals with triethylamine as co-reactants which had no fluorescence (FL) response. Due to the synergistic effect of bimetallic structures, the ECL signals of AuPt NCs were 6.8 and 94 times higher than those of monometallic Au and Pt NCs, respectively. The electric and optical properties of GSH-AuPt NCs differed from those of Au and Pt NCs completely. An electron-transfer mediated ECL mechanism was proposed. The excited electrons may be neutralized by Pt(II) in GSH-Pt and GSH-AuPt NCs, resulting in the vanished FL. Furthermore, abundant TEA radicals formed on the anode contributed electrons to the highest unoccupied molecular orbital of GSH-Au2.5Pt NCs and Pt(II), booming intense ECL signals. Because of the ligand effect and ensemble effect, bimetallic AuPt NCs exhibited much stronger ECL than GSH-Au NCs. A sandwich-type immunoassay for alpha fetoprotein (AFP) cancer biomarkers was fabricated with GSH-AuPt NCs as signal tags, which displayed a wide linear range from 0.01 to 1000 ng·mL-1 and a limit of detection (LOD) down to 1.0 pg·mL-1 at 3S/N. Compared to previous ECL AFP immunoassays, this method not only had a wider linear range but also a lower LOD. The recoveries of AFP in human serum were around 108%, providing a wonderful strategy for fast, sensitive, and accurate cancer diagnosis.

Problems in the Pathologic Diagnosis of Suspected Lung Cancer.

Since the introduction of low-dose computed tomography (CT) screening for patients at high risk of lung cancer, the detection rate of suspicious lung cancer has increased. In addition, there have been many advances in therapeutics targeting oncogenic drivers in non-small cell lung cancer. Therefore, accurate pathological diagnosis of lung cancer, including molecular diagnosis, is increasingly important. This review examines the problems in the pathological diagnosis of suspected lung cancer. For successful pathological diagnosis of lung cancer, clinicians should determine the appropriate modality of the diagnostic procedure, considering individual patient characteristics, CT findings, and the possibility of complications. Furthermore, clinicians should make efforts to obtain a sufficient amount of tissue sample using non- or less-invasive procedures for pathological diagnosis and biomarker analysis.

GFAT1: A Potential Prognostic Biomarker in Colorectal Cancer

Introduction: There is an increasing demand for additional techniques to diagnose and treat cancer including CRC or colorectal cancer effectively. Utilizing antibodies as biomarker could contribute to accurate diagnosis of cancer due to its high specificity and sensitivity. One of the etiologies of CRC progression was proposed as the alterations of hexosamine biosynthetic pathway which could subsequently influence the rate-limiting enzyme, glutamine-fructose-6-phosphate aminotransferase (GFAT1). These increased enzymatic activities resulted in an elevation of glucose uptake that provides nutrients facilitating the progression of cancer cells. Therefore, we attempted to determine the potential of GFAT1 as the biomarker for CRC by correlating its expression with clinicopathological features of the patients. Methods: A total of 132 10% formalin-fixed paraffin embedded tissue were retrieved. Immunohistochemistry (IHC) was performed on the tissue sections and digital images were subsequently acquired. All the images were automatedly analyzed using IHC Profiler. GFAT1 immunoreactivity in colorectal tissues was calculated using an adapted H-score formula. Clinicopathological features of the patients were statistically correlated with the status of GFAT1. Results: Colorectal adenocarcinoma tissues had the significantly highest GFAT1 H-scores with the mean of 103.18 compared to adenoma and non-tumor tissues. There have been no significant associations between clinicopathological characteristics of the patients and the status of GFAT1 except for tumor size. Conclusion: Immunoreactivity of GFAT1 was significantly different between non-tumorous tissues and adenocarcinoma as well as between adenoma and adenocarcinoma tissues. GFAT1 could serve as one of the prognostic biomarkers or useful targets.

SERS-based biosensor for detection of f-PSA%: Implications for the diagnosis of prostate cancer

In diagnosing prostate cancer and distinguishing it from other prostate diseases, the ratio of the concentration of free prostate-specific antigen (f-PSA) to total prostate-specific antigen (t-PSA), i.e., (f-PSA%) is more accurate than the concentration of t-PSA alone. Immunoassay based on surface-enhanced Raman scattering (SERS) frequency shift has been proven to be particularly suitable for detecting large biomolecules with high reproducibility. Along similar lines, the present study developed a SERS-based biosensor that simultaneously detects t-PSA and f-PSA. The 4-mercaptobenzoic acid (MBA) on the immunocapture substrate is coupled to the t-PSA antibody through the carboxyl group, and the combination of t-PSA induces the Raman frequency shifts of MBA. The immunocolloidal gold attached with f-PSA antibodies selectively capture the f-PSA that immobilized on the MBA-modified SERS substrates, allowing for f-PSA quantification according to the SERS intensities of the 5, 5′-Dithiobis (succinimidyl-2-nitrobenzoate) (DSNB) probe. The results show that f-PSA and t-PSA have good linear response in the concentration scale of 0.1–20 ng/mL, and 1–200 ng/mL, respectively. The biosensor combines Raman frequency shifts and intensities, which greatly simplifies traditional procedures for f-PSA% detection. All the results demonstrated the great potential of the proposed biosensor in highly reproducible and accurate diagnosis of prostate cancers.

Machine Learning-Based Label-Free SERS Profiling of Exosomes for Accurate Fuzzy Diagnosis of Cancer and Dynamic Monitoring of Drug Therapeutic Processes.

Exosomes are a class of extracellular vesicles secreted by cells, which can be used as promising noninvasive biomarkers for the early diagnosis and treatment of diseases, especially cancer. However, due to the heterogeneity of exosomes, it remains a grand challenge to distinguish accurately and reliably exosomes from clinical samples. Herein, we achieve accurate fuzzy discrimination of exosomes from human serum samples for accurate diagnosis of breast cancer and cervical cancer through machine learning-based label-free surface-enhanced Raman spectroscopy (SERS), by using "hot spot" rich 3D plasmonic AuNPs nanomembranes as substrates. Due to the existence of some weak distinguishable SERS fingerprint signals and the high sensitivity of the method, the machine learning-based SERS analysis can precisely identify three (normal and cancerous) cell lines, two of which are different types of cancer cells, without specific labeling of biomarkers. The prediction accuracy based on the machine learning algorithm was up to 91.1% for the discrimination of different cell lines (H8, HeLa, and MCF-7 cell)-derived exosomes. Our model trained with SERS spectra of cell-derived exosomes could reach 93.3% prediction accuracy for clinical samples. Furthermore, the action mechanism of the chemotherapeutic process of MCF-7 cells can be revealed by dynamic monitoring of SERS profiling of the exosomes secreted. The method would be useful for noninvasive and accurate diagnosis and postoperative assessment of cancer or other diseases in the future.

Artificial intelligence-enabled microbiome-based diagnosis models for a broad spectrum of cancer types.

Microbiome-based diagnosis of cancer is an increasingly important supplement for the genomics approach in cancer diagnosis, yet current models for microbiome-based diagnosis of cancer face difficulties in generality: not only diagnosis models could not be adapted from one cancer to another, but models built based on microbes from tissues could not be adapted for diagnosis based on microbes from blood. Therefore, a microbiome-based model suitable for a broad spectrum of cancer types is urgently needed. Here we have introduced DeepMicroCancer, a diagnosis model using artificial intelligence techniques for a broad spectrum of cancer types. Built based on the random forest models it has enabled superior performances on more than twenty types of cancers' tissue samples. And by using the transfer learning techniques, improved accuracies could be obtained, especially for cancer types with only a few samples, which could satisfy the requirement in clinical scenarios. Moreover, transfer learning techniques have enabled high diagnosis accuracy that could also be achieved for blood samples. These results indicated that certain sets of microbes could, if excavated using advanced artificial techniques, reveal the intricate differences among cancers and healthy individuals. Collectively, DeepMicroCancer has provided a new venue for accurate diagnosis of cancer based on tissue and blood materials, which could potentially be used in clinics.

New bidirectional recurrent neural network optimized by improved Ebola search optimization algorithm for lung cancer diagnosis

The early detection of cancerous and malignant lung cancer by medical imaging techniques, CT-scan for example, which never needs to do sampling reduces the risk of cancer growth and spreading. Accordingly, computer image processing and diagnostic system development, followed by cancer's classification into malignant and benign, is of primary importance in the early discovery of lung cancer which plays a pivotal role in the treatment improvement and saving the patient's life. This work intended to improve malignant and benign gland categorization accuracy and, as a result, detection accuracy. Here, a new methodology has been proposed to get an accurate lung cancer diagnosis system using an improved Bidirectional Recurrent neural network. The improvement of the network has been done by designing an improved form of an Ebola optimization search algorithm. Before applying the major diagnosis system, some preprocessing techniques have been done. The model is then applied to IQ-OTH/NCCD lung cancer dataset and its results are compared with some published works to indicate the eminence of the suggested method toward the comparative ones.

Development of an anatomical breast phantom from polyvinyl chloride plastisol with lesions of various shape, elasticity and echogenicity for teaching ultrasound examination.

The WHO reported an increasing trend in the number of new cases of breast cancer, making it the most prevalent cancer in the world. This fact necessitates the availability of highly qualified ultrasonographers, which can be achieved by the widespread implementation of training phantoms. The goal of the present work is to develop and test an inexpensive, accessible, and reproducible technology for creating an anatomical breast phantom for practicing ultrasound diagnostic skills in grayscale and elastography imaging, as well as ultrasound-guided biopsy sampling. We used FDM 3D printer and PLA plastic for printing an anatomical breast mold. We made a phantom using a mixture of polyvinyl chloride plastisol, graphite powder, and metallic glitter to simulate soft tissues and lesions. Various degrees of elasticity were imparted using plastisols of stiffness ranging from 3 to 17 on the Shore scale. The lesions were shaped by hand. The materials and methods used are easily accessible and reproducible. Using the proposed technology, we have developed and tested a basic, differential, and elastographic versions of the breast phantom. The three versions of the phantom are anatomical and intended for use in medical education: the basic version is for practicing primary hand-eye coordination skills; the differential one is for practicing the differential diagnosis skills; the elastographic version helps developing the skills needed for assessing the stiffness of tissues. The proposed technology allows the creation of breast phantoms for practicing hand-eye coordination and develop the critical skills for navigation and assessment of the shape, margins, and size of the lesion, as well as performing an ultrasound-guided biopsy. It is cost-effective, reproducible, and easily implementable, and could be instrumental in generating ultrasonographers with crucial skills for accurate diagnosis of breast cancer, especially in low-resource settings.

Hollow Core-Shell Metal Oxide Heterojunctions for the Urinary Metabolic Fingerprint-Based Noninvasive Diagnostic Strategy.

Urine is a preferred object for noninvasive diagnostic strategies. Urinary metabolic analysis is speculatively regarded as an ideal tool for screening diseases closely related to the genitourinary system in view of the intimate relationship between metabolomics and phenotype. Herein, we propose a urinary metabolic fingerprint-based noninvasive diagnostic strategy by designing hollow core-shell metal oxide heterojunctions (denoted as MOHs). With outstanding light absorption and electron-hole separation ability, MOHs aid in the extraction of high-performance urine metabolic fingerprints. Coupled with optimized machine learning algorithms, we establish a metabolic marker panel for accurate diagnosis of prostate cancer (PCa), which is the most common malignant tumor of the male genitourinary system, achieving accuracies of 84.72 and 83.33% in the discovery and validation sets, respectively. Furthermore, metabolite variations and related pathway analyses confirm the credibility and change correlation of key metabolic features in PCa. This work tends to advance the noninvasive diagnostic strategy toward clinical realities.

Mapping Immune-Tumor Bidirectional Dialogue Using Ultrasensitive Nanosensors for Accurate Diagnosis of Lung Cancer.

Lung cancer is one of the most common cancers with high mortality worldwide despite the development of molecularly targeted therapies and immunotherapies. A significant challenge in managing lung cancer is the accurate diagnosis of cancerous lesions owing to the lack of sensitive and specific biomarkers. The current procedure necessitates an invasive tissue biopsy for diagnosis and molecular subtyping, which presents patients with risk, morbidity, anxiety, and high false-positive rates. The high-risk diagnostic approach has highlighted the need to search for a reliable, low-risk noninvasive diagnostic approach to capture lung cancer heterogeneity precisely. The immune interaction profile of lung cancer is driven by immune cells' distinctive, precise interactions with the tumor microenvironment. Here, we hypothesize that immune cells, particularly T cells, can be used for accurate lung cancer diagnosis by exploiting the distinctive immune-tumor interaction by detecting the immune-diagnostic signature. We have developed an ultrasensitive T-sense nanosensor to probe these specific diagnostic signatures using the physical synthesis process of multiphoton ionization. Our research employed predictive in vitro models of lung cancers, cancer-associated T cells (PCAT, MCAT) and CSC-associated T cells (PCSCAT, MCSCAT), from primary and metastatic lung cancer patients to reveal the immune-diagnostic signature and uncover the molecular, functional, and phenotypic separation between patient-derived T cells (PDT) and healthy samples. We demonstrated this by adopting a machine learning model trained with SERS data obtained using cocultured T cells with preclinical models (CAT, CSCAT) of primary (H69AR) and metastatic lung cancer (H1915). Interrogating these distinct signatures with PDT captured the complexity and diversity of the tumor-associated T cell signature across the patient population, exposing the clinical feasibility of immune diagnosis in an independent cohort of patient samples. Thus, our predictive approach using T cells from the patient peripheral blood showed a highly accurate diagnosis with a specificity and sensitivity of 94.1% and 100%, respectively, for primary lung cancer and 97.9% and 94.4% for metastatic lung cancer. Our results prove that the immune-diagnostic signature developed in this study could be used as a clinical technology for cancer diagnosis and determine the course of clinical management with T cells.

Intravesical BCG therapy with photodynamic diagnosis-guided transurethral resection of bladder tumors improves recurrence-free survival

BackgroundIntravesical Bacille Calmette–Guerin (BCG) therapy has been reported to be effective in preventing recurrence and progression in non-muscle invasive bladder cancer. Furthermore, photodynamic diagnosis (PDD)-assisted transurethral resection of bladder tumor (TURBT) improves the accuracy of cancer diagnosis and contributes to lower recurrence rates. The purpose of this study is to investigate whether more tumor resection with PDD-TURBT rather than conventional TURBT before BCG therapy outweighs the benefit of BCG therapy alone. MethodsPatients who underwent intravesical BCG therapy following TURBT in our institution from 2010 to 2021 were included. They were divided into the following two groups: those who received PDD-TURBT before BCG treatment (PDD + BCG group) and those who received conventional TURBT before BCG treatment (WL + BCG group). The 2-year recurrence-free survival (RFS) and progression-free survival (PFS) of the two groups were retrospectively analyzed and compared. ResultsThe 2-year RFS was significantly improved in the PDD + BCG group (hazard ratio [HR]: 2.41, 95% confidence interval [CI]: 1.26–4.60; p = 0.025). No significant difference in 2-year PFS was noted between the two groups. Analysis of prognostic factors for RFS showed that PDD-TURBT w We think that this text does not adequately express the meaning that we want to deliver to the reader.as a significant prognostic factor in univariate analysis (HR: 0.41, 95% CI: 0.18–0.92; p = 0.03). ConclusionBCG treatment following PDD-TURBT significantly improved RFS more than BCG therapy following WL-TURBT. More accurate tumor localization and more efficient tumor resection by PDD-TURBT may have a positive impact on subsequent BCG treatments even if the treatment is administered postoperatively.