This PDF file contains the front matter associated with SPIE Proceedings Volume 11961 including the Title Page, Copyright information, and Table of Contents.

Metaphase chromosome karyotyping plays an important role in the diagnosis of certain cancers and some genetic diseases by detecting chromosome abnormalities. For this technique, high magnification objective lens is used to ensure the chromosome’s band pattern sharpness, but the small field of view (FOV) of the lens makes the imaging of chromosomes very tedious and time consuming. The purpose of this study is to verify the use of the Fourier ptychography microscopy (FPM) system in high-resolution karyotyping. Based on our former study, we further expanded the theoretical NA of the FPM system to 1.11 with a 20×/0.4 NA objective lens and higher illumination angles. To evaluate the resolving power of the FPM system, a 1951 USAF resolution target was imaged to create the modulation transfer function (MTF) curves. The performance of the FPM system was also assessed by imaging chromosomes acquired from blood and bone marrow pathological samples. The results were compared with a conventional 100×/1.45 NA oil immersion objective lens. The MTF curves demonstrate that the contrast of the FPM system is inferior but close to the 100× objective lens (1.45 NA). As compared to the images acquired by the 100×/1.45 NA oil immersion objective lens, the chromosome images recovered by the FPM system contain all the band patterns, despite the loss of some fine details. This study initially verified that the high NA FPM system can guarantee the sharpness of chromosome band patterns as the conventional high magnification oil immersion objective lens, while enabling a large FOV without the utilization of oil immersion medium.

Pancreatic tumors and melanoma are two types of cancers with high mortality. Monitoring the tumor microenvironment during growth plays an important role in studying the property of tumors. To study the progression of pancreatic tumors and melanoma in vivo, we propose to use optical coherence tomography (OCT) to monitor the tumor structure and angiogenesis in mouse in a longitudinal manner. Our results demonstrate that OCT is a promising tool to longitudinally monitor the change and progression of structure, tissue distribution, and microvasculature in pancreatic and melanoma tumor models. OCT can serve as a promising modality to provide structural and microvascular information for future anti-cancer treatment and drug development.

Objective: To investigate the underlying mechanism of hyperthermia-induced inhibition of breast cancer cells, we analyzed the influence of hyperthermia on the stimulator of interferon genes (STING) signaling pathway in 4T1 cells. Method: 4T1 cells were heated in the water bath at 37°C, 40°C, 42°C, 45°C, 47°C and 50°C for 30 minutes respectively. Additionally, 4T1 cells were randomly divided into four groups and cultured under the conventional condition for 0h, 2h, 4h, 8h after treated with water bath at 42°C and 45°C. Western blot was used to detect the expression level of STING, interferon regulatory factor 3 (IRF3) protein expression and their phosphorylation, and ELISA was used to detect IFN-β secreted by 4T1 cells at different temperatures. Results: Under 37°C to 50°C, with the increase of temperature, the phosphorylation ratio of STING and IRF3 decreased (P<0.05, P<0.0001), and there is no significant change of IFN-β. After treated in the water bath at 42°C and 45°C, with the extension of following conventional culture time, the expression of STING protein in 4T1 cells did not change significantly while the expression of IRF3 protein raised (P<0.05). Conclusion: From 37°C to 50 °C, the anti-tumor immune effect of breast cancer during hyperthermia is not achieved by STING signaling pathway in 4T1 cells, but the uncomplete STING signaling pathway in 4T1 cells might improve heat tolerance by its decreased phosphorylation level.

Pancreatic cancer is one of the deadliest types of cancer due to its ability to metastasize throughout the body. According to the Surveillance, Epidemiology, and End Results Program (SEER), the overall 5-year relative survival rate for pancreatic cancer is 10.8% but once the cancer metastasizes, this survival rate reduces to 3%. Despite recent advances in cancer research, all the current cancer therapies have severe limitations in treating pancreatic cancer. One major challenge in pancreatic cancer research is the lack of an accurate model of the pancreatic tumor and its microenvironment. The common two-dimensional (2D) models are ineffective in mimicking solid tumors. Therefore, there is a need for a better in vitro model to accurately mimic the characteristics of solid pancreatic tumor and to improve assessments for cancer therapies. Here, we generated multicellular tumor spheroids from an aggressive pancreatic cancer cell line, Panc02-H7, and monitored its growth. We also investigated the effects of photothermal therapy through irradiation of a near-infrared laser, which is used to induce immunogenic cell death (ICD) on the tumor spheroids. This three-dimensional (3D) tumor model mimics pancreatic tumors in vitro and this study can generate translatable results and, consequently, guide clinical studies.

The study aims to develop a novel computer-aided diagnosis (CAD) scheme for mammographic breast mass classification using semi-supervised learning. Although supervised deep learning has achieved huge success across various medical image analysis tasks, its success relies on large amounts of high-quality annotations, which can be challenging to acquire in practice. To overcome this limitation, we propose employing a semi-supervised method, i.e., virtual adversarial training (VAT), to leverage and learn useful information underlying in unlabeled data for better classification of breast masses. Accordingly, our VAT-based models have two types of losses, namely supervised and virtual adversarial losses. The former loss acts as in supervised classification, while the latter loss aims at enhancing the model’s robustness against virtual adversarial perturbation, thus improving model generalizability. To evaluate the performance of our VAT-based CAD scheme, we retrospectively assembled a total of 1024 breast mass images, with equal number of benign and malignant masses. A large CNN and a small CNN were used in this investigation, and both were trained with and without the adversarial loss. When the labeled ratios were 40% and 80%, VAT-based CNNs delivered the highest classification accuracy of 0.740±0.015 and 0.760±0.015, respectively. The experimental results suggest that the VAT-based CAD scheme can effectively utilize meaningful knowledge from unlabeled data to better classify mammographic breast mass images.