In vitro radiotherapy and chemotherapy alter migration of brain cancer cells before cell death.

Although radiotherapy and most cancer drugs target the proliferation of cancer cells, it is metastasis, the complex process by which cancer cells spread from the primary tumor to other tissues and organs of the body where they form new tumors, that leads to over 90% of all cancer deaths. Thus, there is an urgent need for anti-metastasis strategies alongside chemotherapy and radiotherapy. An important step in the metastatic cascade is migration. It is the first step in metastasis via local invasion. Here we address the question whether ionizing radiation and/or chemotherapy might inadvertently promote metastasis and/or invasiveness by enhancing cell migration. We used a standard laboratory irradiator, Faxitron CellRad, to irradiate both non-cancer (HCN2 neurons) and cancer cells (T98G glioblastoma) with 2 Gy, 10 Gy and 20 Gy of X-rays. Paclitaxel (5 µM) was used for chemotherapy. We then measured the attachment and migration of the cells using an electric cell substrate impedance sensing device. Both the irradiated HCN2 cells and T98G cells showed significantly (p < 0.01) enhanced migration compared to non-irradiated cells, within the first 20-40 h following irradiation with 20 Gy. Our results suggest that cell migration should be a therapeutic target in anti-metastasis/anti-invasion strategies for improved radiotherapy and chemotherapy outcomes.

Deep-learning-based model observer for a lung nodule detection task in computed tomography.

Purpose: Task-based image quality assessment using model observers (MOs) is an effective approach to radiation dose and scanning protocol optimization in computed tomography (CT) imaging, once the correlation between MOs and radiologists can be established in well-defined clinically relevant tasks. Conventional MO studies were typically simplified to detection, classification, or localization tasks using tissue-mimicking phantoms, as traditional MOs cannot be readily used in complex anatomical background. However, anatomical variability can affect human diagnostic performance. Approach: To address this challenge, we developed a deep-learning-based MO (DL-MO) for localization tasks and validated in a lung nodule detection task, using previously validated projection-based lesion-/noise-insertion techniques. The DL-MO performance was compared with 4 radiologist readers over 12 experimental conditions, involving varying radiation dose levels, nodule sizes, nodule types, and reconstruction types. Each condition consisted of 100 trials (i.e., 30 images per trial) generated from a patient cohort of 50 cases. DL-MO was trained using small image volume-of-interests extracted across the entire volume of training cases. For each testing trial, the nodule searching of DL-MO was confined to a 3-mm thick volume to improve computational efficiency, and radiologist readers were tasked to review the entire volume. Results: A strong correlation between DL-MO and human readers was observed (Pearson's correlation coefficient: 0.980 with a 95% confidence interval of [0.924, 0.994]). The averaged performance bias between DL-MO and human readers was 0.57%. Conclusion: The experimental results indicated the potential of using the proposed DL-MO for diagnostic image quality assessment in realistic chest CT tasks.