Malignant Samples Research Articles

Correction for Extrinsic Background in Raman Hyperspectral Images.

Raman hyperspectral microscopy is a valuable tool in biological and biomedical imaging. Because Raman scattering is often weak in comparison to other phenomena, prevalent spectral fluctuations and contaminations have brought advancements in analytical and chemometric methods for Raman spectra. These chemometric advances have been key contributors to the applicability of Raman imaging to biological systems. As studies increase in scale, spectral contamination from extrinsic background, intensity from sources such as the optical components that are extrinsic to the sample of interest, has become an emerging issue. Although existing baseline correction schemes often reduce intrinsic background such as autofluorescence originating from the sample of interest, extrinsic background is not explicitly considered, and these methods often fail to reduce its effects. Here, we show that extrinsic background can significantly affect a classification model using Raman images, yielding misleadingly high accuracies in the distinction of benign and malignant samples of follicular thyroid cell lines. To mitigate its effects, we develop extrinsic background correction (EBC) and demonstrate its use in combination with existing methods on Raman hyperspectral images. EBC isolates regions containing the smallest amounts of sample materials that retain extrinsic contributions that are specific to the device or environment. We perform classification both with and without the use of EBC, and we find that EBC retains biological characteristics in the spectra while significantly reducing extrinsic background. As the methodology used in EBC is not specific to Raman spectra, correction of extrinsic effects in other types of hyperspectral and grayscale images is also possible.

Hybrid Mahalanobis Taguchi System with Binary Whale Optimisation Feature Selection for the Wisconsin Breast Cancer Dataset

The Mahalanobis-Taguchi System (MTS) is a statistical approach used in breast cancer research to facilitate early detection and promote efficient treatment. The technique analyses mammogram images for significant features using a multivariate statistical analysis technique. It combines the Mahalanobis distance (MD) and Taguchi's method to determine the differences between benign and malignant samples. While orthogonal array (OA) has been widely used in MTS, it has been criticised for providing suboptimal results due to insufficient coverage of feature combinations during the feature optimisation process. To address this issue, the Binary Whale Optimisation Algorithm (BWOA) is proposed as an improved search algorithm for MTS. This paper aims to develop a novel hybrid method that enhances the efficiency of the Mahalanobis Taguchi System (MTS). The performance of feature selection ability due to different MTS hybrid algorithms were also compared. BWOA simulates the hunting behaviour of humpback whales and works by exploring new regions of the solution space, gradually narrowing the search space, and fine-tuning the solution. MTS-BWOA demonstrated its enhanced capability in feature optimisation compared to traditional MTS methods and has the potential to be applied in other medical imaging domains.

Performance of endobronchial ultrasound transbronchial needle aspiration as the first nodal staging procedure for the determination of programmed death ligand-1 expression in non-small cell lung cancer patients.

The determination of the programmed death ligand-1 (PD-L1) expression is part of the diagnostic algorithm for advanced non-small cell lung cancer (NSCLC) patients. We aimed to analyze the diagnostic performance of EBUS-TBNA performed as first-choice nodal staging procedure for the determination of PD-L1 expression in NSCLC patients. Longitudinal-prospective study including NSCLC patients diagnosed between January 2018 and October 2019, for whom a primary tumor biopsy sample and an EBUS-TBNA cytological malignant sample were available. Samples with fewer than 100 malignant cells were considered inadequate. PDL-1 IHC 22C3 pharmDx antibody was used. The percentage of tumor cells expressing PD-L1, setting 1% and 50% as cutoff points, was collected. The weighted kappa coefficient was used to assess the concordance of PD-L1 expression. The PD-L1 expression was compared inprecision terms. From a total of 43 patients, 53 pairs of samples were obtained, of which 23 (43.4%) were adequate and included for analysis. The weighted kappa coefficient for PD-L1 expression was 0.41 (95% CI 0.15-0.68) and 0.56 (95% CI 0.23-0.9) for cutoff values ≥ 1% and ≥ 50%, respectively. In advanced stages, the weighted kappa coefficient was 0.6 (95% CI 0.3-0.9) and 1 (95% CI 1-1) for PD-L1 expression cutoff values ≥ 1% and ≥ 50%, respectively. EBUS-TBNA showed a sensitivity, specificity, positive predictive value, and negative predictive value of 1 to detect PDL-1 expression ≥ 50% in advanced stages. EBUS-TBNA performed as first nodal staging procedure in advanced NSCLC patients provides reliable specimens for the detection of PD-L1 expression ≥ 50% and could guide immunotherapy.

Validating Sam68 expression and protein level in breast cancer.

Breast carcinoma ranks as the second most common cancer among women worldwide. Despite significant therapeutic advancements, approximately 25% of breast carcinoma cases have resistance to current treatment modalities, posing a significant challenge for patient management. This study aimed to investigate the role of Sam68 mRNA and its protein in promoting oncogenesis and breast cancer progression. Sam68 protein levels were assessed in tissue samples using an Enzyme-Linked Immunosorbent Assay kit from Sun Long Biotech. Whole RNA was isolated from malignant breast tissue samples obtained from patients. The RNA concentration was determined using an Eppendorf photometer, yielding an average concentration of 62.1±10.07 ng/µl. The purity of the isolated RNA was evaluated by measuring the A260/A280 ratio (1.9±0.07) and the A260/A230 ratio (1.7±0.3). The results indicated a significant upregulation of Sam68 mRNA expression in breast cancer tissues, supporting the findings from previous studies and indicating the correlation between altered Sam68 expression and the development of breast carcinoma, highlighting the potential significance of Sam68 in the pathogenesis of the disease. Estimating Sam68 in the blood may serve as a potential biomarker for assessing the malignant grade and metastatic spread of breast carcinoma cells.

REDH: A database of RNA editome in hematopoietic differentiation and malignancy.

The conversion of adenosine (A) to inosine (I) through deamination is the prevailing form of RNA editing, impacting numerous nuclear and cytoplasmic transcripts across various eukaryotic species. Millions of high-confidence RNA editing sites have been identified and integrated into various RNA databases, providing a convenient platform for the rapid identification of key drivers of cancer and potential therapeutic targets. However, the available database for integration of RNA editing in hematopoietic cells and hematopoietic malignancies is still lacking. We downloaded RNA sequencing (RNA-seq) data of 29 leukemia patients and 19 healthy donors from National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, and RNA-seq data of 12 mouse hematopoietic cell populations obtained from our previous research were also used. We performed sequence alignment, identified RNA editing sites, and obtained characteristic editing sites related to normal hematopoietic development and abnormal editing sites associated with hematologic diseases. We established a new database, "REDH", represents RNA editome in hematopoietic differentiation and malignancy. REDH is a curated database of associations between RNA editome and hematopoiesis. REDH integrates 30,796 editing sites from 12 murine adult hematopoietic cell populations and systematically characterizes more than 400,000 edited events in malignant hematopoietic samples from 48 cohorts (human). Through the Differentiation, Disease, Enrichment, and knowledge modules, each A-to-I editing site is systematically integrated, including its distribution throughout the genome, its clinical information (human sample), and functional editing sites under physiological and pathological conditions. Furthermore, REDH compares the similarities and differences of editing sites between different hematologic malignancies and healthy control. REDH is accessible at http://www.redhdatabase.com/ . This user-friendly database would aid in understanding the mechanisms of RNA editing in hematopoietic differentiation and malignancies. It provides a set of data related to the maintenance of hematopoietic homeostasis and identifying potential therapeutic targets in malignancies.

Machine Learning-based Prediction of the Likelihood of Colorectal Cancer Using miRNA Expression

[Background] Colorectal cancer (CRC) comprises 10% of all cancer diagnoses, making it the third most diagnosed cancer globally. Despite its prevalence, most current methods for identifying CRC lack sensitivity and consistency while being invasive and costly. Thus, this study aimed to develop artificial neural network (ANN) models that could accurately detect CRC using miRNA expressions in tissue and plasma samples. [Methods] The study used miRNA expression profiles of formalin-fixed paraffin-embedded tissue and plasma samples obtained from CRC patients and healthy controls. ANNs were trained to discriminate between CRC patients from healthy controls using the relative expression of miR-21-5p, miR-196b-5p, miR- 135b-5p, miR-92a-3p, miR-29a-3p, and miR-197-3p in colorectal tissues and blood plasma. Multivariate logistic regression (MLR) and decision tree (DT) models were used to compare the performance of the ANN models. [Results] The ANNs achieved an accuracy of 98.5 and 88.2%, a sensitivity of 90.9 and 80.4%, a specificity of 92.6 and 84.7%, and an area under the ROC curve of 0.92 and 0.83 for the plasma and tissue samples, respectively. Moreover, sensitivity analysis of the ANN models showed that miR-135b-5p and miR-92a-3p had the greatest influence in distinguishing CRC from healthy plasma and malignant from neoplasm-free colorectal tissues, respectively. However, only miR-135b-5p was significantly downregulated in both CRC plasma and malignant colorectal tissue samples. Results from the MLR and DT models support the results from the ANN sensitivity analysis. [Conclusion] Our results show that the trained ANNs were able to accurately and confidently detect CRC using the considered six miRNA expression levels in colorectal tissue and plasma samples, providing an accurate, rapid, and less-invasive approach to diagnosing CRC.

Using a deep learning neural network for the identification of malignant cells in effusion cytology material.

To evaluate the application of an artificial neural network in the detection of malignant cells in effusion samples. In this retrospective study, we selected 90 cases of effusion cytology samples over 2 years. There were 52 cases of metastatic adenocarcinoma and 38 benign effusion samples. In each case, an average of five microphotographs from the representative areas were taken at 40× magnification from Papanicolaou-stained samples. A total of 492 images were obtained from these 90 cases. We applied a deep convolutional neural network (DCNN) model to identify malignant cells in the cytology images of effusion cytology smears. The training was performed for 15 epochs. The model consisted of 783 layers with 188 convolution-max pool layers in between. In the test set, the DCNN model correctly identified 54 of 56 images of benign samples and 49 out of 56 images of malignant samples. It showed 88% sensitivity, 96% specificity and 96% positive predictive value in the screening of malignant cases in effusion. The area under the receiver operating curve was 0.92. DCNN is a unique technology that can detect malignant cells from cytological images. The model works rapidly and there is no bias in cell selection or feature extraction. The present DCNN model is promising and can have a significant impact on the diagnosis of malignancy in cytology.

The amino-acid stress sensing eIF2α kinase GCN2 is a survival biomarker for malignant mesothelioma

BackgroundMalignant mesothelioma is a tumour that is strongly associated with a history of asbestos exposure, and which derives from mesothelial cells that line the serous cavities of the body. The tumour most commonly arises in the pleural cavity, but can also arise in the pericardium, peritoneum, and tunica vaginalis. At present the lesion has a very poor prognosis and is an incurable form of cancer with median survival times of up to 19 months being quoted for some histological subtypes. A large proportion of mesotheliomas have been shown to be arginine auxotrophic, leading to new research for therapeutics which might exploit this potential vulnerability.MethodsWe measured the levels of General Control Non-derepressible 2 (GCN2) protein in malignant mesothelioma tumour samples and determined whether these levels correlate with clinical outcomes.ResultsWe observed that the expression levels of GCN2 correlated with patient survival and was an independent prognostic variable in pairwise comparisons with all available clinical data.ConclusionThese findings suggest that GCN2 levels provides prognostic information and may allow for stratification of care pathways. It may suggest that targeting GCN2 is a viable strategy for mesothelioma therapy development.

CTCs Detection and Whole-exome Sequencing Might Be Used to Differentiate Benign and Malignant Pulmonary Nodules.

Low-density computed tomography (LDCT) improved early lung cancer diagnosis but introduces an excess of false-positive pulmonary nodules data. Hence, accurate diagnosis of early-stage lung cancer remains challenging. The purpose of the study was to assess the feasibility of using circulating tumour cells (CTCs) to differentiate malignant from benign pulmonary nodules. 122 patients with suspected malignant pulmonary nodules detected on chest CT in preparation for surgery were prospectively recruited. Peripheral blood samples were collected before surgery, and CTCs were identified upon isolation by size of epithelial tumour cells and morphological analysis. Laser capture microdissection, MALBAC amplification, and whole-exome sequencing were performed on 8 samples. The diagnostic efficacy of CTCs counting, and the genomic variation profile of benign and malignant CTCs samples were analysed. Using 2.5 cells/5 mL as the cut-off value, the area under the receiver operating characteristic curve was of 0.651 (95% confidence interval: 0.538-0.764), with a sensitivity and specificity of 0.526 and 0.800, respectively, and positive and negative predictive values of 91.1% and 30.3%, respectively. Distinct sequence variations differences in DNA damage repair-related and driver genes were observed in benign and malignant samples. TP53 mutations were identified in CTCs of four malignant cases; in particular, g.7578115T>C, g.7578645C>T, and g.7579472G>C were exclusively detected in all four malignant samples. CTCs play an ancillary role in the diagnosis of pulmonary nodules. TP53 mutations in CTCs might be used to identify benign and malignant pulmonary nodules.

Improved Segmentation of Pulmonary Nodules Using Soft Computing Techniques with SegNet and Adversarial Networks

Lung cancer is seen as one of the most common lung diseases. For the patients having symptoms, the presence of lung nodules is checked by using various imaging techniques. Pulmonary nodules are detected in most of the cases having symptoms. But identifying the type of the nodule and the categorization still remains as a challenge. After confirming the presence of a nodule (benign or malignant) it takes several other steps to identify its characteristics. Improved imaging methods produce results within a short span of time. Research works are being conducted to increase the overall efficiency of the system. The proposed system considers authentic data sources for the study. The benign and malignant samples are considered for the generation of realistic large image sets. The generation of a large data set with the help of a generative adversarial network (GAN) is the first part of the work. The generated images using GAN cannot be differentiated from the original images even by a trained radiologist. This proves the importance of images generated using GAN. A GAN is able to generate 1024 × 1024 resolutions for natural images. Real data images are used to finetune the SegNet output. Through transfer learning, these weights are transferred to the system for segmentation of the images. The training process use real and generated images, which improve theefficiency of the network. The original data from LUNA 16 was used to further generate benign and malignant samples using GAN. A total of 440 images and their augmented images were used for training the GAN, and it generated 1,001,000 images. Hence the overall efficiency of the system was improved. To verify the results, the same various combinations and methods were considered and tabulated with various parameters. Methods with SegNet, GAN, and other combinations were evaluated to verify the efficiency of the system. Receiver operating characteristics were also plotted and compared with the area under the curve for verification of the results.

STNet: shape and texture joint learning through two-stream network for knowledge-guided image recognition.

The human brain processes shape and texture information separately through different neurons in the visual system. In intelligent computer-aided imaging diagnosis, pre-trained feature extractors are commonly used in various medical image recognition methods, common pre-training datasets such as ImageNet tend to improve the texture representation of the model but make it ignore many shape features. Weak shape feature representation is disadvantageous for some tasks that focus on shape features in medical image analysis. Inspired by the function of neurons in the human brain, in this paper, we proposed a shape-and-texture-biased two-stream network to enhance the shape feature representation in knowledge-guided medical image analysis. First, the two-stream network shape-biased stream and a texture-biased stream are constructed through classification and segmentation multi-task joint learning. Second, we propose pyramid-grouped convolution to enhance the texture feature representation and introduce deformable convolution to enhance the shape feature extraction. Third, we used a channel-attention-based feature selection module in shape and texture feature fusion to focus on the key features and eliminate information redundancy caused by feature fusion. Finally, aiming at the problem of model optimization difficulty caused by the imbalance in the number of benign and malignant samples in medical images, an asymmetric loss function was introduced to improve the robustness of the model. We applied our method to the melanoma recognition task on ISIC-2019 and XJTU-MM datasets, which focus on both the texture and shape of the lesions. The experimental results on dermoscopic image recognition and pathological image recognition datasets show the proposed method outperforms the compared algorithms and prove the effectiveness of our method.

Effusion cytology of metastatic carcinosarcoma.

Carcinosarcomas (CSs) are rare gynecological neoplasms seen in elderly females. These are composed of malignant epithelial and mesenchymal components, which appear as adenocarcinoma and high-grade sarcoma. Effusions are encountered uncommonly in CS. The study focuses on the cytomorphology of 10 cases of metastatic CS in effusions. In 6 years, there were 10 (0.45%) cases of metastatic CS in effusion samples out of 2240 malignant effusion samples. The samples were processed by SurePath™ and centrifuge technique. Both May-Grünwald-Giemsa and Papanicolaou stained smears were evaluated for cytomorphological features, and the findings were correlated with subsequent histopathology. The cells were predominantly arranged in ball-like clusters and discretely. The cells had abundant vacuolated cytoplasm and enlarged pleomorphic nuclei. Occasional cases showed scattered spindle cells. The cases were diagnosed as metastatic adenocarcinoma (7/10) and positive for malignant cells (3/10). None of the cases was diagnosed as CS. The primary of these cases was in the uterus (7/10) and ovary (3/10). The cytological evaluation of such effusion samples rarely demonstrates the classical biphasic pattern of these tumors. Mostly, the carcinomatous component is evident, and the sarcomatous element is inapparent and readily missed.

Residual neural network‐assisted one‐class classification algorithm for melanoma recognition with imbalanced data

AbstractSkin cancer, also known as melanoma, is a deadly form of skin cancer that can significantly improve survival rates when diagnosed at an early stage. It is usually diagnosed visually from dermoscopic images, and such visual assessment of skin cancer by the naked eye is a challenging and arduous task. Therefore, the detection of melanoma from dermoscopic images using trained artificial intelligence models is of great importance today. However, since melanoma is a rare disease, existing databases of skin lesions often contain highly unbalanced numbers of benign and malignant samples. In this paper, we propose a new one‐class classification‐based skin lesion classification strategy for small and unbalanced datasets. One‐class classification (OCC) is a special case of multi‐classification. OCC aims to learn a descriptive paradigm from positive class data (true data) during training and reject pseudo data (fake data) that do not conform to the paradigm during inference. OCC has great potential for application in anomaly detection problems. We have analyzed several approaches to the OCC task in recent years and propose a new design paradigm for the OCC problem, taking into account the unbalanced data set of the melanoma classification task. We have designed an improved OCC network based on this design paradigm, where the network is based on the architecture of a residual neural network, combining the coding and decoding idea of variational self‐encoder and the adversarial training idea of an adversarial neural network, using binary cross‐entropy as the loss function and introducing the channel attention mechanism. Tests on several publicly available dermatology datasets show that this improved OCC network addresses the unbalanced dataset situation in melanoma image classification to some extent while having relatively excellent performance. Compared with some traditional networks, it can obtain more stable training results and perform more consistently on complex datasets.

Abstract A006: Loss of PMEPA1 gene isoform facilitates the development of hormone and radiation therapies resistance in prostate cancer cells

Abstract Introduction: Nearly all the patients receiving hormone treatment eventually progress into castration resistance stage. In addition, approximately 30% to 40% of patients receiving radiation treatment will develop resistance. The aberrant alterations of androgen receptor (AR) and transforming growth factor-β (TGF-β) signaling play critical roles in such processes. Our studies identified five PMEPA1 (Prostate Transmembrane Protein Androgen Induced 1) gene isoforms in prostate cancer cells. PMEPA1-b isoform was androgen responsive and inhibited androgen signaling. In contrast, PMEPA1 isoforms a and d were TGF-β inducible and suppressed TGF-β signaling. Loss or reduced expressions of PMEPA1 was detected in over 60% of prostate cancer samples. In this study, we investigated the impacts of PMEPA1 isoforms (a, b and d) on the development of hormone and radiation therapies resistance in prostate cancer. Methods: RNA-seq data of unmatched 50 benign and 499 malignant prostate samples from The Cancer Genome Atlas (TCGA) dataset, as well as 101 metastatic castration resistant prostate cancer (mCRPC) biopsy samples from Quigley lab at UCSF (WCDT dataset) were used to study the transcript levels of PMEPA1 isoforms (a, b and d) and their correlations to disease progression. Hormone dependent LNCaP cells and androgen independent PC-3, DU-145 cells were utilized to investigate the impacts of PMEPA1 isoforms (a, b and d) on the responses to of androgen inhibitors (enzalutamide and bicalutamide, 0, 5.0 and 25.0 nM) and radiation (0, 1.0 and 5.0 Gy). The vectors of pcDNA3.1-PMEPA1-a, -b and -d were transfected into prostate cancer cells via Lipofectamine, and endogenous PMEPA1 isoforms were knocked down by isoforms specific siRNAs. The cell growth was assessed by cell counting, BrdU incorporation and cell plating efficiency assays. The protein levels of AR and PSA were evaluated by immunoblotting. The transcript levels of PSA, NKX3.1, NEDD9 and THBS1 were assessed by RT-PCR technology. Results: The reduced transcript levels of PMEPA1 isoforms (a, b and d) were detected in mCRPC group. Lower expression of PMEPA1-b was significantly associated with earlier biochemical recurrence. The growth inhibition and androgen signaling blockage mediated by androgen inhibitors were explicitly reversed by depletion of PMEPA1-b in LNCaP cells. Ectopic PMEPA1-b in isoform b depleted LNCaP cells reinstituted the growth inhibition induced by androgen inhibitors. Knockdown of isoforms a and d conspicuously rescued the decreased cell proliferation and suppressed TGF-β signaling resulted from radiation treatment in PC-3 and DU-145 cells. Similarly, over-expression of PMEPA1-a and -d in siRNA treated cells recapitulated the growth inhibitory effects of radiation treatments. Conclusion: Loss of PMEPA1 gene isoforms (a, b and d) induced the development of resistance to hormone ablation and radiation treatments in prostate cancer cells in vitro through activating androgen and TGF-β signaling. Our research provided a new insight into treatment resistance mechanism of prostate cancer cells. Citation Format: Shashwat Sharad, Hua Li. Loss of PMEPA1 gene isoform facilitates the development of hormone and radiation therapies resistance in prostate cancer cells [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A006.

A combinatorial model of plasma proteins and LDCT imaging and the diagnosis of pulmonary nodules.

8568 Background: Low dosage computer tomography (LDCT) has been widely adopted as a sensitive method to detect early-stage lung cancer; however, debate regarding its accuracy and overdiagnosis is still ongoing. An accurate non-invasive test is needed to identify malignant nodules and reduce unnecessary invasive procedures. Studies show that plasma proteins and LDCT imaging features may be used to discriminate malignant pulmonary nodules from benign ones. We aimed to develop a combinatorial approach integrating serum protein markers and imaging features to improve the classification of pulmonary nodules. Methods: This study established a prospective research cohort, which enrolled 608 patients of pulmonary nodules. Plasma samples were collected to measure protein levels using Proximity Extension Assay (PEA) technology. The imaging features of the pulmonary nodules were extracted using the python 'radiomics' package. Following feature extraction, a deep learning networks model was built using training cases. The model was then tested in the testing set to evaluate its accuracy and robustness. Results: From the study cohort, 184 benign (BN) and 184 malignant (MT) samples matched for sex and age were chosen to have a representative training set. The rest 240 samples (81 BN and 159 MT) were used as a testing set. Image features were extracted from 448 patients (119 BN and 153 MT in training; 57 BN and 119 MT in testing) with raw LDCT image available. In the testing set, the model trained using only protein levels had an AUC of 0.83 [0.782-0.877] (sensitivity = 71.1% [95% CI 63.6-77.6]; specificity = 82.7% [73.1-89.4]) when classifying plasma samples of lung cancers from those of benign nodules. In comparison, the imaging features-only model had an AUC of 0.874 [0.821-0.916] (sensitivity = 81.5 [73.6-87.5]; specificity = 73.7 [61.0-83.4]). The combinatorial model integrating both protein and imaging features had an AUC of 0.878 [0.830-0.921] (sensitivity = 83.2% [75.5-88.8]; specificity = 77.2% [64.8-86.2]). Notably, the combinatorial model was highly sensitive for early-stage lung cancer, achieving a sensitivity of 93.1% [78.0-98.1] when classifying stage-0 lung cancer cases(n = 29) and 94.8% [85.9-98.2] for stage-I cases (N = 58). Its sensitivity for stage II-IV (n = 7) and clinically diagnosed lung cancers cases (n = 25) were 100% [64.6-100] and 40.0% [23.4-59.3], respectively. Conclusions: This study aimed to construct a model to distinguish malignant pulmonary nodules from benign lung diseases using protein levels and LDCT imaging features. The resulted combinatorial model showed by utilizing both types of features, it was able to accurately differentiate benign and malignant pulmonary nodules, suggesting it may provide guidance for the clinical management of these nodules.

Trained dogs can accurately discriminate between scents of saliva samples from dogs with cancer versus healthy controls.

To determine whether dogs can be trained to utilize olfaction to differentiate between saliva samples from dogs with cancer and those from healthy control dogs. Canine patient saliva samples were collected (October 2020 to July 2022) from 139 dogs diagnosed with malignant tumors and from 161 healthy dogs (control samples) for use during training and testing of the dog detection team. Samples from canine patients were collected prior to treatment with radiation therapy or chemotherapy. Six pet dogs (mean ± SD age, 5.4 ± 1.9 years) were trained for odor discrimination between healthy control and malignant tumor samples. Training of the dogs, using a reward-based positive reinforcement method, took place 1 to 3 times per week for a period of 6 months (January to June 2022). After training was complete, a subset of samples not utilized during the training sessions were selected for use during odor discrimination testing of the dog team. The trained dogs could accurately distinguish between samples from cancer patients versus control dogs with a mean sensitivity of 90% and mean specificity of 98%, and with mean positive and negative predictive values of 95%. This study serves as preliminary evidence that dogs can be trained to detect differences in scent between saliva samples from cancer and normal patients. Further studies should expand upon these results with a larger sample, varied tumor types, use of non-cancer diseases as controls, and exploration of this technique in feline patients.

Comparative analysis of the mutation detection using malignant pleural effusion and blood cell-free DNA samples in lung cancer.

e20518 Background: Malignant pleural effusion (MPE) is an abnormal amount of fluid to collect between the chest wall and the lung, and it denotes an advanced malignant disease process. MPE presents a severe medical condition which can result in breathlessness, pain, cachexia and reduced physical activity. MPE is considered to bear cancer signatures because it contains tumor cells released from malignancies, thus can be applied in high-throughput sequencing for cancer screening. Cell-free DNA (cfDNA) in blood consists of small fragments released from apoptotic cells or cancer cells, and pathological conditions can influence this process. Therefore, detection of cfDNA in peripheral blood can identify abnormalities of individuals in a noninvasive manner. Here, we wonder the difference of MPE and cfDNA in lung cancer-related mutation detection. Methods: In this study, MPE and blood samples from 216 patients with late-stage lung cancer were collected and applied to library construction and next-generation sequencing. All samples were collected during August 2018 to December 2022. We focused on the key mutations in cancer-causing genes and drug treatment-related genes (e.g., EGFR, KRAS, BRAF, ERBB2, etc.). Statistical analysis was conducted to compare the detection results between MPE and cfDNA samples by Fisher’s exact test. Results: Among the 216 patients, 60 patients (60/216 = 27.8%) were identified as negative detection with no mutation detected in cancer-causing genes in MPE and blood samples. Of the remaining 156 pairs of samples, there were 118 (118/216 = 54.6%) pairs showing the positive detection in both MPE and blood samples. 12 patients (12/216 = 5.6%) demonstrated the positive detection in blood samples while negative detection in MPE samples. The other 26 patients (26/216 = 12%) demonstrated the positive detection in MPE but negative detection in blood samples. There is no significant difference in detection rates between two types of samples (p = 0.194). Similarity, we next focused on the mutations in drug treatment-related genes and identified 91 patients (91/216 = 42.1%) as negative detection. There were 85 patients (85/216 = 39.4%) showing the same positive detection both in MPE and blood samples. 13 patients (13/216 = 6%) demonstrated the positive detection in blood samples but negative detection in MPE samples. The other 27 patients (27/216 = 12.5%) demonstrated the positive detection in MPE but negative detection in blood samples. No significant difference in detection rates was observed between two types of samples (p = 0.211). Conclusions: The mutation detection results in MPE and blood samples turned out to be consistent in most conditions, suggesting an alternative application of MPE or blood samples in cancer screening. Nonetheless, it is still valuable to integrate the information in both MPE and blood samples for a higher detection sensitivity.

Immunohistochemical Expression of CD90, CD133, and TPM1 in Relation to Gastric Cancer and H. pylori Association.

Gastric cancer (GC) is the second most common cause of cancer-related death worldwide. Multiple malignancies overexpress CD90, making it a helpful diagnostic and prognostic marker. CD133 is suggested to be related to poor prognosis in GC. Tropomyosin-1 (TPM1) tumor-suppressor gene low expression may predict poor survival in GC. Our study aimed to investigate CD90, CD133, and TPM1 immunohistochemical expression in GC in relation to diagnosis, prognosis, and Helicobacter pylori (H. pylori) infection. 144 paraffin blocks containing gastric cancerous (108 cases), and non-cancerous (36 cases) tissue were analyzed histopathologically for the type of lesion, grade, and stage of malignancy and by using an immunohistochemical assay for studying the expression of CD90, CD133, and TPM1. Data analysis was carried out using the Statistical Package for the Social Sciences (SPSS) version 20.0. The obtained results showed a significantly higher expression of CD90 and CD133 while showing a significantly lower expression of TPM1 in malignant samples compared to benign ones. CD90 was significantly higher in grade-3, stage-3, and N3 (p<0.05), with no significant difference concerning positive and negative H. pylori samples. CD133 percentage and H-score were significantly higher in grade-2 and stage-4 tumors than in other grades and stages, while being insignificantly higher in N3 and H. pylori-positive cases. TPM1 expression levels were significantly downregulated in GC and H. pylori-positive cases (p<0.05). TPM1 downregulation was associated with grade progression, increased depth of invasion, and tumor node metastasis. CD90, CD133, and TPM1 immunohistochemical expression in the gastric biopsy are related firmly to grades and stages of GC as well as H. pylori infection, so they could be of prognostic value. Further studies on a larger sample size are recommended.

Enhanced grasshopper optimization‐based selection of ultrasound and elastography features for breast lesion classification

AbstractBreast cancer (BC) is the second leading cause of death in women worldwide, caused by the uncontrolled proliferation of malignant cells in the breast. It is very tough to differentiate cancer from non‐cancer cells because of their shape and structure. Therefore, early diagnosis and classification of breast tumors as benign is a hot research topic. However, existing methods for early diagnosis have shortcomings like high computational cost and low classification accuracy. To trounce those limitations, this paper presented a computer‐aided diagnosis (CAD) system employing metaheuristic algorithms and machine learning classifiers. The proposed system uses fused features of ultrasound (US) and elastography images to enhance classification accuracy. Firstly, both US and elastography images are separately preprocessed using an enhanced wavelet thresholding method to diminish speckle noise. Secondly, a new segmentation algorithm is proposed to extract the lesion region from the preprocessed US image. Then the contour of the segmented lesion is mapped on an elastography image to obtain the corresponding region of interest (ROI), and texture and morphology features are separately extracted and concatenated. Subsequently, the enhanced grasshopper optimization algorithm (EGOA) is adopted to select salient features and reduce the dimension. Finally, based on the selected features, two machine learning classifiers, linear discriminant analysis (LDA) and support vector machine (SVM), are employed to classify the lesions into benign and malignant. The effectiveness of the proposed method is validated on 520 tumor images, including both benign and malignant samples. The suggested performance has been assessed by computing the confusion matrix and receiver operating characteristic curve. The developed method, EGOA‐SVM, outperforms the other classifiers by reaching a higher classification rate of 99.4%, specificity of 100%, the sensitivity of 99.2%, and Matthew's coefficient of 0.979. The introduced method can improve the radiologist's performance in categorizing breast lesions with a good classification rate. It could be a promising tool for clinical use.

A New Detection Method of Oral and Oropharyngeal Squamous Cell Carcinoma Based on Multivariate Analysis of Surface Enhanced Raman Spectra of Salivary Exosomes.

Raman spectroscopy recently proved a tremendous capacity to identify disease-specific markers in various (bio)samples being a non-invasive, rapid, and reliable method for cancer detection. In this study, we first aimed to record vibrational spectra of salivary exosomes isolated from oral and oropharyngeal squamous cell carcinoma patients and healthy controls using surface enhancement Raman spectroscopy (SERS). Then, we assessed this method's capacity to discriminate between malignant and non-malignant samples by means of principal component-linear discriminant analysis (PC-LDA) and we used area under the receiver operating characteristics with illustration as the area under the curve to measure the power of salivary exosomes SERS spectra analysis to identify cancer presence. The vibrational spectra were collected on a solid plasmonic substrate developed in our group, synthesized using tangential flow filtered and concentrated silver nanoparticles, capable of generating very reproducible spectra for a whole range of bioanalytes. SERS examination identified interesting variations in the vibrational bands assigned to thiocyanate, proteins, and nucleic acids between the saliva of cancer and control groups. Chemometric analysis indicated discrimination sensitivity between the two groups up to 79.3%. The sensitivity is influenced by the spectral interval used for the multivariate analysis, being lower (75.9%) when the full-range spectra were used.