Machine learning-based rapid diagnosis of human borderline ovarian cancer on second-harmonic generation images

Ovarian cancer identification technology based on deep learning and second harmonic generation imaging

Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer-related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally required to screen for lesions; however, traditional case examinations are time consuming and laborious and require extensive experience and knowledge from pathologists. Therefore, this study proposes a simple, fast, and label-free ovarian cancer diagnosis method that combines second harmonic generation (SHG) imaging and deep learning. Unstained fresh human ovarian tissues were subjected to SHG imaging and accurately characterized using the Pyramid Vision Transformer V2 (PVTv2) model. The results showed that the SHG imaged collagen fibers could quantify ovarian cancer. In addition, the PVTv2 model could accurately differentiate the 3240 SHG images obtained from our imaging collection into benign, normal, and malignant images, with a final accuracy of 98.4%. These results demonstrate the great potential of SHG imaging techniques combined with deep learning models for diagnosing the diseased ovarian tissues.

Extraction of collagen morphological features from second-harmonic generation microscopy images via GLCM and CT analyses: A cross-laboratory study

Second-harmonic generation (SHG) microscopy provides a high-resolution label-free approach for noninvasively detecting collagen organization and its pathological alterations. Up to date, several imaging analysis algorithms for extracting collagen morphological features from SHG images-such as fiber size and length, order and anisotropy-have been developed. However, the dependence of extracted features on experimental setting represents a significant obstacle for translating the methodology in the clinical practice. We tackled this problem by acquiring SHG images of the same kind of collagenous sample in various laboratories using different experimental setups and imaging conditions. The acquired images were analyzed by commonly used algorithms, such as gray-level co-occurrence matrix or curvelet transform; the extracted morphological features were compared, finding that they strongly depend on some experimental parameters, whereas they are almost independent from others. We conclude with useful suggestions for comparing results obtained in different labs using different experimental setups and conditions.

On the quantitative analysis of lamellar collagen arrangement with second-harmonic generation imaging

Second harmonic generation (SHG) allows for the examination of collagen structure in collagenous tissues. Collagen is a fibrous protein found in abundance in the human body, present in bones, cartilage, the skin, and the cornea, among other areas, providing structure, support, and strength. Its structural arrangement is deeply intertwined with its function. For instance, in the cornea, alterations in collagen organization can result in severe visual impairments. Using SHG imaging, various metrics have demonstrated the potential to study collagen organization. The discrimination between healthy, keratoconus, and crosslinked corneas, assessment of injured tendons, or the characterization of breast and ovarian tumorous tissue have been demonstrated. Nevertheless, these metrics have not yet been objectively evaluated or compared. A total of five metrics were identified and implemented from the literature, and an additional approach adapted from texture analysis was proposed. In this study, we analyzed their effectiveness on a ground-truth set of artificially generated fibrous images. Our investigation provides the first comprehensive assessment of the performance of multiple metrics, identifying both the strengths and weaknesses of each approach and providing valuable insights for future applications of SHG imaging in medical diagnostics and research.

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as a promising solution to improve the accuracy of ovarian cancer detection. This systematic review explores the role of DL in improving the diagnostic accuracy for ovarian cancer. The methodology involved the establishment of research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis using medical imaging modalities, as well as tumour differentiation and radiomics. Data extraction, analysis, and synthesis were performed to summarize the characteristics and findings of the selected studies. The review emphasizes the potential of DL in enhancing the diagnosis of ovarian cancer by accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated their effectiveness in categorizing ovarian tissues and achieving comparable diagnostic performance to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the promise of improving patient outcomes, refining treatment approaches, and supporting well-informed decision-making. Nevertheless, additional research and validation are necessary to ensure the dependability and applicability of DL models in everyday clinical settings.

Use and application of organ-on-a-chip platforms in cancer research

Tumors are a major cause of death worldwide, and much effort has been made to develop appropriate anti-tumor therapies. Existing in vitro and in vivo tumor models cannot reflect the critical features of cancer. The development of organ-on-a-chip models has enabled the integration of organoids, microfluidics, tissue engineering, biomaterials research, and microfabrication, offering conditions that mimic tumor physiology. Three-dimensional in vitro human tumor models that have been established as organ-on-a-chip models contain multiple cell types and a structure that is similar to the primary tumor. These models can be applied to various foci of oncology research. Moreover, the high-throughput features of microfluidic organ-on-a-chip models offer new opportunities for achieving large-scale drug screening and developing more personalized treatments. In this review of the literature, we explore the development of organ-on-a-chip technology and discuss its use as an innovative tool in basic and clinical applications and summarize its advancement of cancer research.

Construction of 3D and 2D contrast-enhanced CT radiomics for prediction of CGB3 expression level and clinical prognosis in bladder cancer

Objective

The purpose of this study was to construct a 3D and 2D contrast-enhanced computed tomography (CECT) radiomics model to predict CGB3 levels and assess its prognostic abilities in bladder cancer (Bca) patients.

Methods

Transcriptome data and CECT images of Bca patients were downloaded from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database. Clinical data of 43 cases from TCGA and TCIA were used for radiomics model evaluation. The Volume of interest (VOI) (3D) and region of interest (ROI) (2D) radiomics features were extracted. For the construction of predicting radiomics models, least absolute shrinkage and selection operator regression were used, and the filtered radiomics features were fitted using the logistic regression algorithm (LR). The model's effectiveness was measured using 10-fold cross-validation and the area under the receiver operating characteristic curve (AUC of ROC).

Result

CGB3 was a differential expressed prognosis-related gene and involved in the immune response process of plasma cells and T cell gamma delta. The high levels of CGB3 are a risk element for overall survival (OS). The AUCs of VOI and ROI radiomics models in the training set were 0.841 and 0.776, while in the validation set were 0.815 and 0.754, respectively. The Delong test revealed that the AUCs of the two models were not statistically different, and both models had good predictive performance.

Conclusion

The CGB3 expression level is an important prognosis factor for Bca patients. Both 3D and 2D CECT radiomics are effective in predicting CGB3 expression levels.

Current Understanding on Why Ovarian Cancer Is Resistant to Immune Checkpoint Inhibitors

The standard treatment of ovarian cancer (OC) patients, including debulking surgery and first-line chemotherapy, is unsatisfactory because of recurrent episodes in the majority (~70%) of patients with advanced OC. Clinical trials have shown only a modest (10-15%) response of OC individuals to treatment based on immune checkpoint inhibitors (ICIs). The resistance of OC to therapy is caused by various factors, including OC heterogeneity, low density of tumor-infiltrating lymphocytes (TILs), non-cellular and cellular interactions in the tumor microenvironment (TME), as well as a network of microRNA regulating immune checkpoint pathways. Moreover, ICIs are the most efficient in tumors that are marked by high microsatellite instability and high tumor mutation burden, which is rare among OC patients. The great challenge in ICI implementation is connected with distinguishing hyper-, pseudo-, and real progression of the disease. The understanding of the immunological, molecular, and genetic mechanisms of OC resistance is crucial to selecting the group of OC individuals in whom personalized treatment would be beneficial. In this review, we summarize current knowledge about the selected factors inducing OC resistance and discuss the future directions of ICI-based immunotherapy development for OC patients.

A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women

Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further.

Nonlinear microscopy and deep learning classification for mammary gland microenvironment studies

Tumors, their microenvironment, and the mechanisms by which collagen morphology changes throughout cancer progression have recently been a topic of interest. Second harmonic generation (SHG) and polarization second harmonic (P-SHG) microscopy are label-free, hallmark methods that can highlight this alteration in the extracellular matrix (ECM). This article uses automated sample scanning SHG and P-SHG microscopy to investigate ECM deposition associated with tumors residing in the mammary gland. We show two different analysis approaches using the acquired images to distinguish collagen fibrillar orientation changes in the ECM. Lastly, we apply a supervised deep-learning model to classify naïve and tumor-bearing mammary gland SHG images. We benchmark the trained model using transfer learning with the well-known MobileNetV2 architecture. By fine-tuning the different parameters of these models, we show a trained deep-learning model that suits such a small dataset with 73% accuracy.

CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer

OBJECTIVE

We aimed to evaluate the prognostic value of C-C motif chemokine receptor type 5 (CCR5) expression level for patients with ovarian cancer and to establish a radiomics model that can predict CCR5 expression level using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database.

METHODS

A total of 343 cases of ovarian cancer from the TCGA were used for the gene-based prognostic analysis. Fifty seven cases had preoperative computed tomography (CT) images stored in TCIA with genomic data in TCGA were used for radiomics feature extraction and model construction. 89 cases with both TCGA and TCIA clinical data were used for radiomics model evaluation. After feature extraction, a radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. A prognostic scoring system incorporating radiomics signature based on CCR5 expression level and clinicopathologic risk factors was proposed for survival prediction.

RESULTS

CCR5 was identified as a differentially expressed prognosis-related gene in tumor and normal sample, which were involved in the regulation of immune response and tumor invasion and metastasis. Four optimal radiomics features were selected to predict overall survival. The performance of the radiomics model for predicting the CCR5 expression level with 10-fold cross- validation achieved Area Under Curve (AUCs) of 0.770 and of 0.726, respectively, in the training and validation sets. A predictive nomogram was generated based on the total risk score of each patient, the AUCs of the time-dependent receiver operating characteristic (ROC) curve of the model was 0.8, 0.673 and 0.792 for 1-year, 3-year and 5-year, respectively. Along with clinical features, important imaging biomarkers could improve the overall survival accuracy of the prediction model.

CONCLUSION

The expression levels of CCR5 can affect the prognosis of patients with ovarian cancer. CT-based radiomics could serve as a new tool for prognosis prediction.

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

Organ on Chip Technology to Model Cancer Growth and Metastasis

Organ on chip (OOC) has emerged as a major technological breakthrough and distinct model system revolutionizing biomedical research and drug discovery by recapitulating the crucial structural and functional complexity of human organs in vitro. OOC are rapidly emerging as powerful tools for oncology research. Indeed, Cancer on chip (COC) can ideally reproduce certain key aspects of the tumor microenvironment (TME), such as biochemical gradients and niche factors, dynamic cell-cell and cell-matrix interactions, and complex tissue structures composed of tumor and stromal cells. Here, we review the state of the art in COC models with a focus on the microphysiological systems that host multicellular 3D tissue engineering models and can help elucidate the complex biology of TME and cancer growth and progression. Finally, some examples of microengineered tumor models integrated with multi-organ microdevices to study disease progression in different tissues will be presented.