Expanding the application of cancer near-infrared photoimmunotherapy

Delta-like ligand 3 in small cell lung cancer: Potential mechanism and treatment progress

Small cell lung cancer (SCLC) is one of a pathological type of lung cancer, and it is characterized by invasiveness, high malignancy and refractoriness. The mortality rate of SCLC is significantly higher than other types of lung cancer, and the treatment options for SCLC patients are limited. Delta-like ligand 3 (DLL3) is a Notch signaling ligand that plays a role in regulating the proliferation, development and metastasis of SCLC cells. Mnay studies have shown that DLL3 is overexpressed on the surface of SCLC cells, suggesting that DLL3 is a potential target for SCLC patients. A series of drug trials targeting DLL3 are underway. The Phase III clinical trials of Rova-T, a drug targeting DLL3, have not yielded the expected results. However, other drugs that target DLL3, such as AMG119, AMG757 and DLL3-targeted NIR-PIT, bring new ideas for SCLC treatment. Overall, DLL3 remains a valuable target for SCLC.

Inside-the-body light delivery system using endovascular therapy-based light illumination technology

Background

Light-based therapies are promising for treating diseases including cancer, hereditary conditions, and protein-related disorders. However, systems, methods, and devices that deliver light deep inside the body are limited. This study aimed to develop an endovascular therapy-based light illumination technology (ET-BLIT), capable of providing deep light irradiation within the body.

Methods

The ET-BLIT system consists of a catheter with a single lumen as a guidewire and diffuser, with a transparent section at the distal end for thermocouple head attachment. The optical light diffuser alters the emission direction laterally, according to the optical fibre's nose-shape angle. If necessary, after delivering the catheter to the target position in the vessel, the diffuser is inserted into the catheter and placed in the transparent section in the direction of the target lesion.

Findings

ET-BLIT was tested in an animal model. The 690-nm near-infrared (NIR) light penetrated the walls of blood vessels to reach the liver and kidneys without causing temperature increase, vessel damage, or blood component alterations. NIR light transmittance from the diffuser to the detector within the organ or vessel was approximately 30% and 65% for the renal and hepatic arteries, respectively.

Interpretation

ET-BLIT can be potentially used in clinical photo-based medicine, as a far-out technology. ET-BLIT uses a familiar method that can access the whole body, as the basic procedure is comparable to that of endovascular therapy in terms of sequence and technique. Therefore, the use of the ET-BLIT system is promising for many light-based therapies that are currently in the research phase.

Funding

Supported by Programme for Developing Next-generation Researchers (Japan Science and Technology Agency); JSPS KAKENHI (18K15923, 21K07217); JST-CREST (JPMJCR19H2); JST-FOREST-Souhatsu (JPMJFR2017); The Uehara Memorial Foundation; Yasuda Memorial Medical Foundation; Mochida Memorial Foundation for Medical and Pharmaceutical Research; Takeda Science Foundation; The Japan Health Foundation; Takahashi Industrial and Economic Research Foundation; AICHI Health Promotion Foundation; and Princess Takamatsu Cancer Research Fund.

An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method

Prostate cancer disease is one of the common types that cause men's prostate damage all over the world. Prostate-specific membrane antigen (PSMA) expressed by type-II is an extremely attractive style for imaging-based diagnosis of prostate cancer. Clinically, photodynamic therapy (PDT) is used as noninvasive therapy in treatment of several cancers and some other diseases. This paper aims to segment or cluster and analyze pixels of histological and near-infrared (NIR) prostate cancer images acquired by PSMA-targeting PDT low weight molecular agents. Such agents can provide image guidance to resection of the prostate tumors and permit for the subsequent PDT in order to remove remaining or noneradicable cancer cells. The color prostate image segmentation is accomplished using an optimized image segmentation approach. The optimized approach combines the k-means clustering algorithm with elbow method that can give better clustering of pixels through automatically determining the best number of clusters. Clusters' statistics and ratio results of pixels in the segmented images show the applicability of the proposed approach for giving the optimum number of clusters for prostate cancer analysis and diagnosis.