Nanoarchitecture-based photothermal ablation of cancer: A systematic review.

Photothermal therapy (PTT) is an emerging non-invasive method used in cancer treatment. In PTT, near-infrared laser light is absorbed by a chromophore and converted into heat within the tumor tissue. PTT for cancer usually combines a variety of interactive plasmonic nanomaterials with laser irradiation. PTT enjoys PT agents with high conversion efficiency to convert light into heat to destroy malignant tissue. In this review, published studies concerned with the use of nanoparticles (NPs) in PTT were collected by a systematic and comprehensive search of PubMed, Cochrane, Embase, and Scopus databases. Gold, silver and iron NPs were the most frequent choice in PTT. The use of surface modified NPs allowed selective delivery and led to a precise controlled increase in the local temperature. The presence of NPs during PTT can increase the reactive generation of oxygen species, damage the DNA and mitochondria, leading to cancer cell death mainly via apoptosis. Many studies recently used core-shell metal NPs, and the effects of the polymer coating or ligands targeted to specific cellular receptors in order to increase PTT efficiency were often reported. The effective parameters (NP type, size, concentration, coated polymers or attached ligands, exposure conditions, cell line or type, and cell death mechanisms) were investigated individually. With the advances in chemical synthesis technology, NPs with different shapes, sizes, and coatings can be prepared with desirable properties, to achieve multimodal cancer treatment with precision and specificity.

A comprehensive non-invasive framework for automated evaluation of acute renal transplant rejection using DCE-MRI.

The objective was to develop a novel and automated comprehensive framework for the non-invasive identification and classification of kidney non-rejection and acute rejection transplants using 2D dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The proposed approach consists of four steps. First, kidney objects are segmented from the surrounding structures with a geometric deformable model. Second, a non-rigid registration approach is employed to account for any local kidney deformation. In the third step, the cortex of the kidney is extracted in order to determine dynamic agent delivery, since it is the cortex that is primarily affected by the perfusion deficits that underlie the pathophysiology of acute rejection. Finally, we use an analytical function-based model to fit the dynamic contrast agent kinetic curves in order to determine possible rejection candidates. Five features that map the data from the original data space to the feature space are chosen with a k-nearest-neighbor (KNN) classifier to distinguish between acute rejection and non-rejection transplants. Our study includes 50 transplant patients divided into two groups: 27 patients with stable kidney function and the remainder with impaired kidney function. All of the patients underwent DCE-MRI, while the patients in the impaired group also underwent ultrasound-guided fine needle biopsy. We extracted the kidney objects and the renal cortex from DCE-MRI for accurate medical evaluation with an accuracy of 0.97 ± 0.02 and 0.90 ± 0.03, respectively, using the Dice similarity metric. In a cohort of 50 participants, our framework classified all cases correctly (100%) as rejection or non-rejection transplant candidates, which is comparable to the gold standard of biopsy but without the associated deleterious side-effects. Both the 95% confidence interval (CI) statistic and the receiver operating characteristic (ROC) analysis document the ability to separate rejection and non-rejection groups. The average plateau (AP) signal magnitude and the gamma-variate model functional parameter α have the best individual discriminating characteristics.

Modeling of nanotherapeutics delivery based on tumor perfusion.

Heterogeneities in the perfusion of solid tumors prevent optimal delivery of nanotherapeutics. Clinical imaging protocols to obtain patient-specific data have proven difficult to implement. It is challenging to determine which perfusion features hold greater prognostic value and to relate measurements to vessel structure and function. With the advent of systemically administered nanotherapeutics, whose delivery is dependent on overcoming diffusive and convective barriers to transport, such knowledge is increasingly important. We describe a framework for the automated evaluation of vascular perfusion curves measured at the single vessel level. Primary tumor fragments, collected from triple-negative breast cancer patients and grown as xenografts in mice, were injected with fluorescence contrast and monitored using intravital microscopy. The time to arterial peak and venous delay, two features whose probability distributions were measured directly from time-series curves, were analyzed using a Fuzzy C-mean (FCM) supervised classifier in order to rank individual tumors according to their perfusion characteristics. The resulting rankings correlated inversely with experimental nanoparticle accumulation measurements, enabling modeling of nanotherapeutics delivery without requiring any underlying assumptions about tissue structure or function, or heterogeneities contained within. With additional calibration, these methodologies may enable the study of nanotherapeutics delivery strategies in a variety of tumor models.

Synthesis and characterization of actively HER-2 Targeted Fe3O4@Au nanoparticles for molecular radiosensitization of breast cancer.

Introduction: The present study was done to assess the effect of molecularly-targeted core/shell of iron oxide/gold nanoparticles (Fe3O4@AuNPs) on tumor radiosensitization of SKBr-3 breast cancer cells. Methods: Human epidermal growth factor receptor-2 (HER-2)-targeted Fe3O4@AuNPs were synthesized by conjugating trastuzumab (TZ, Herceptin) to PEGylated (PEG)-Fe3O4@AuNPs (41.5 nm). First, the Fe3O4@Au core-shell NPs were decorated with PEG-SH to synthesize PEG-Fe3O4@AuNPs. Then, the TZ was reacted to OPSS-PEG-SVA to conjugate with the PEG-Fe3O4@AuNPs. As a result, structure, size and morphology of the developed NPs were assessed using Fourier-transform infrared (FT-IR) spectroscopy, dynamic light scattering (DLS) and transmission electron microscopy (TEM), and ultraviolet-visible spectroscopy. The SKBr-3 cells were treated with different concentrations of TZ, Fe3O4@Au, and TZ-PEG-Fe3O4@AuNPs for irradiation at doses of 2, 4, and 8 Gy (from X-ray energy of 6 and 18 MV). Cytotoxicity was assessed by MTT assay, BrdU assay, and flow cytometry. Results: Results showed that the targeted TZ-PEG-Fe3O4@AuNPs significantly improved cell uptake. The cytotoxic effects of all the studied groups were increased in a higher concentration, radiation dose and energy-dependent manner. A combination of TZ, Fe3O4@Au, and TZ-PEG-Fe3O4@AuNPs with radiation reduced cell viability by 1.35 (P=0.021), 1.95 (P=0.024), and 1.15 (P=0.013) in comparison with 8 Gy dose of 18 MV radiation alone, respectively. These amounts were obtained as 1.27, 1.58, and 1.10 for 8 Gy dose of 6 MV irradiation, respectively. Conclusion: Radiosensitization of breast cancer to mega-voltage radiation therapy with TZ-PEG-Fe3O4@AuNPs was successfully obtained through an optimized therapeutic approach for molecular targeting of HER-2.

Quantifying epithelial-mesenchymal heterogeneity and EMT scoring in tumor samples via tyramide signal amplification (TSA).

Tumor heterogeneity presents an ongoing challenge to disease progression and treatment in many solid tumor types. Understanding the roots of intra-tumoral heterogeneity and how it may relate to the high incidence of metastasis is critical in overcoming disease relapse and chemoresistance. The epithelial-to-mesenchymal transition is a dynamic cellular program that is co-opted by cancer cells to enhance, among others, migratory and invasive cell traits. It is a key contributor to heterogeneity, chemo-resistance, and metastasis in many carcinoma-types, with the intermediate or hybrid EMT state playing a critical role due to its increased tumor-initiating potential. A critical component in utilizing this knowledge in patient treatment is to first detect and score the impact of EMT in a patient sample. Here, we provide a detailed protocol to detect EMT states and quantify the resulting epithelial-mesenchymal heterogeneity within tumors using a novel multiplexed immunostaining approach and analysis method. This protocol and concept can easily be adapted using custom panels of markers to explore other sources of tumoral heterogeneity in addition to EMT.

Bladder cancer prognosis using deep neural networks and histopathology images.

Background: Recent studies indicate that bladder cancer is among the top 10 most common cancers in the world (Saginala et al. 2022). Bladder cancer frequently reoccurs, and prognostic judgments may vary among clinicians. As a favorable prognosis may help to inform less aggressive treatment plans, classification of histopathology slides is essential for the accurate prognosis and effective treatment of bladder cancer patients. Developing automated and accurate histopathology image analysis methods can help pathologists determine the prognosis of patients with bladder cancer. Materials and methods: In this study, we introduced Bladder4Net, a deep learning pipeline, to classify whole-slide histopathology images of bladder cancer into two classes: low-risk (combination of PUNLMP and low-grade tumors) and high-risk (combination of high-grade and invasive tumors). This pipeline consists of four convolutional neural network (CNN)-based classifiers to address the difficulties of identifying PUNLMP and invasive classes. We evaluated our pipeline on 182 independent whole-slide images from the New Hampshire Bladder Cancer Study (NHBCS) (Karagas et al., 1998; Sverrisson et al., 2014; Sverrisson et al., 2014) collected from 1994 to 2004 and 378 external digitized slides from The Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/tcga). Results: The weighted average F1-score of our approach was 0.91 (95% confidence interval (CI): 0.86-0.94) on the NHBCS dataset and 0.99 (95% CI: 0.97-1.00) on the TCGA dataset. Additionally, we computed Kaplan-Meier survival curves for patients who were predicted as high risk versus those predicted as low risk. For the NHBCS test set, patients predicted as high risk had worse overall survival than those predicted as low risk, with a log-rank p-value of 0.004. Conclusions: If validated through prospective trials, our model could be used in clinical settings to improve patient care.

Phenotypic heterogeneity driven by plasticity of the intermediate EMT state governs disease progression and metastasis in breast cancer.

The epithelial-to-mesenchymal transition (EMT) is frequently co-opted by cancer cells to enhance migratory and invasive cell traits. It is a key contributor to heterogeneity, chemoresistance, and metastasis in many carcinoma types, where the intermediate EMT state plays a critical tumor-initiating role. We isolate multiple distinct single-cell clones from the SUM149PT human breast cell line spanning the EMT spectrum having diverse migratory, tumor-initiating, and metastatic qualities, including three unique intermediates. Using a multiomics approach, we identify CBFß as a key regulator of metastatic ability in the intermediate state. To quantify epithelial-mesenchymal heterogeneity within tumors, we develop an advanced multiplexed immunostaining approach using SUM149-derived orthotopic tumors and find that the EMT state and epithelial-mesenchymal heterogeneity are predictive of overall survival in a cohort of stage III breast cancer. Our model reveals previously unidentified insights into the complex EMT spectrum and its regulatory networks, as well as the contributions of epithelial-mesenchymal plasticity (EMP) in tumor heterogeneity in breast cancer.

Main Approaches to Enhance Radiosensitization in Cancer Cells by Nanoparticles: A Systematic Review.

In recent years, high atomic number nanoparticles (NPs) have emerged as promising radio-enhancer agents for cancer radiation therapy due to their unique properties. Multi-disciplinary studies have demonstrated the potential of NPs-based radio-sensitizers to improve cancer therapy and tumor control at cellular and molecular levels. However, studies have shown that the dose enhancement effect of the NPs depends on the beam energy, NPs type, NPs size, NPs concentration, cell lines, and NPs delivery system. It has been believed that radiation dose enhancement of NPs is due to the three main mechanisms, but the results of some simulation studies failed to comply well with the experimental findings. Thus, this study aimed to quantitatively evaluate the physical, chemical, and biological factors of the NPs. An organized search of PubMed/Medline, Embase, ProQuest, Scopus, Cochrane and Google Scholar was performed. In total, 77 articles were thoroughly reviewed and analyzed. The studies investigated 44 different cell lines through 70 in-vitro and 4 in-vivo studies. A total of 32 different types of single or core-shell NPs in different sizes and concentrations have been used in the studies.

Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides.

Importance: Histologic classification of colorectal polyps plays a critical role in screening for colorectal cancer and care of affected patients. An accurate and automated algorithm for the classification of colorectal polyps on digitized histopathologic slides could benefit practitioners and patients. Objective: To evaluate the performance and generalizability of a deep neural network for colorectal polyp classification on histopathologic slide images using a multi-institutional data set. Design, Setting, and Participants: This prognostic study used histopathologic slides collected from January 1, 2016, to June 31, 2016, from Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, with 326 slides used for training, 157 slides for an internal data set, and 25 for a validation set. For the external data set, 238 slides for 179 distinct patients were obtained from 24 institutions across 13 US states. Data analysis was performed from April 9 to November 23, 2019. Main Outcomes and Measures: Accuracy, sensitivity, and specificity of the model to classify 4 major colorectal polyp types: tubular adenoma, tubulovillous or villous adenoma, hyperplastic polyp, and sessile serrated adenoma. Performance was compared with that of local pathologists' at the point of care identified from corresponding pathology laboratories. Results: For the internal evaluation on the 157 slides with ground truth labels from 5 pathologists, the deep neural network had a mean accuracy of 93.5% (95% CI, 89.6%-97.4%) compared with local pathologists' accuracy of 91.4% (95% CI, 87.0%-95.8%). On the external test set of 238 slides with ground truth labels from 5 pathologists, the deep neural network achieved an accuracy of 87.0% (95% CI, 82.7%-91.3%), which was comparable with local pathologists' accuracy of 86.6% (95% CI, 82.3%-90.9%). Conclusions and Relevance: The findings suggest that this model may assist pathologists by improving the diagnostic efficiency, reproducibility, and accuracy of colorectal cancer screenings.

Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides.

Importance: Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results. These approaches, however, require a laborious annotation process and are fragmented. Objective: To evaluate a novel deep learning method that uses tissue-level annotations for high-resolution histological image analysis for Barrett esophagus (BE) and esophageal adenocarcinoma detection. Design, Setting, and Participants: This diagnostic study collected deidentified high-resolution histological images (N = 379) for training a new model composed of a convolutional neural network and a grid-based attention network. Histological images of patients who underwent endoscopic esophagus and gastroesophageal junction mucosal biopsy between January 1, 2016, and December 31, 2018, at Dartmouth-Hitchcock Medical Center (Lebanon, New Hampshire) were collected. Main Outcomes and Measures: The model was evaluated on an independent testing set of 123 histological images with 4 classes: normal, BE-no-dysplasia, BE-with-dysplasia, and adenocarcinoma. Performance of this model was measured and compared with that of the current state-of-the-art sliding window approach using the following standard machine learning metrics: accuracy, recall, precision, and F1 score. Results: Of the independent testing set of 123 histological images, 30 (24.4%) were in the BE-no-dysplasia class, 14 (11.4%) in the BE-with-dysplasia class, 21 (17.1%) in the adenocarcinoma class, and 58 (47.2%) in the normal class. Classification accuracies of the proposed model were 0.85 (95% CI, 0.81-0.90) for the BE-no-dysplasia class, 0.89 (95% CI, 0.84-0.92) for the BE-with-dysplasia class, and 0.88 (95% CI, 0.84-0.92) for the adenocarcinoma class. The proposed model achieved a mean accuracy of 0.83 (95% CI, 0.80-0.86) and marginally outperformed the sliding window approach on the same testing set. The F1 scores of the attention-based model were at least 8% higher for each class compared with the sliding window approach: 0.68 (95% CI, 0.61-0.75) vs 0.61 (95% CI, 0.53-0.68) for the normal class, 0.72 (95% CI, 0.63-0.80) vs 0.58 (95% CI, 0.45-0.69) for the BE-no-dysplasia class, 0.30 (95% CI, 0.11-0.48) vs 0.22 (95% CI, 0.11-0.33) for the BE-with-dysplasia class, and 0.67 (95% CI, 0.54-0.77) vs 0.58 (95% CI, 0.44-0.70) for the adenocarcinoma class. However, this outperformance was not statistically significant. Conclusions and Relevance: Results of this study suggest that the proposed attention-based deep neural network framework for BE and esophageal adenocarcinoma detection is important because it is based solely on tissue-level annotations, unlike existing methods that are based on regions of interest. This new model is expected to open avenues for applying deep learning to digital pathology.

Prediction of lung cancer patient survival via supervised machine learning classification techniques.

Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time with the ultimate goal to inform patient care decisions, and that the performance of these techniques with this particular dataset may be on par with that of classical methods.

A multi-scale non-linear vessel enhancement technique.

We present an enhancement method based on nonlinear diffusion filter and statistical intensity approaches for smoothing and extracting 3-D vascular system from Magnetic Resonance Angiography (MRA) data. Our method distinguishes and enhances the vessels from the other embedded tissues. The Expectation Maximization (EM) technique is employed with non-linear diffusion in order to find the optimal contrast for enhancing vessels; therefore, smoothing while dimming the embedded tissues around the vessels and brightening the vessels. The non-linear diffusion filter smooths the homogeneous regions while preserving edges. The EM technique finds the optimal statistical parameters based on the probability distribution of the classes to discriminate the tissues in the image. Our enhancement technique has been applied to 4 3-D MRA-TOF datasets consisting of around 300 images and has been compared to the regularized Perona and Malik filter. Our experimental results show that the proposed method enhances the image, keeping only the vessels while eliminating the signal from other tissues. In comparison, the conventional non-linear diffusion filter keeps unwanted tissues in addition to the vessels.