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Flavonoids have many biological properties, such as anticancer activity. Chalcones, one of their subunits, attribute their biological activity to their enone part. The presence of dinitrophenyl group in bischalcone because of its radiosensitivity property is important. Radiosensitivity property reduces radiation time in cancer patients and reduces damage to their healthy tissues. In this regard, 2,4-dinitrophenyl bischalcones were synthesized. The presence of 2,4-dinitrobenzaldehyde as a fixed component in synthesis pathway, leads to a reduction in yield of synthesis by common catalysts. Therefore, in this study, for bis-chalone synthesis, we used Graphene Oxide/Fe3O4/L-Proline nanocomposite as a green recoverable bifunctional organocatalyst. This catalyst was recovered simply by applying an external magnet and reused for eight runs. In this research, chalcones and asymmetric bis-chalcones have been synthesized with diverse substitutes in high yields (78–97%). Also, short reaction times (10–82 min), and simple experimental procedures with easy work-up are advantages of the introduced procedure. The synthesized compounds were characterized by melting point and analytical techniques. The chemical structures of synthesized compounds were confirmed by means of IR, 1HNMR, and 13CNMR. [ABSTRACT FROM AUTHOR]

Aim: The diagnosis accuracy of computed tomography (CT) systems and the reliability of calculated Hounsfield Units (HUs) are critical in tumor detection and cancer patients’ treatment planning. This study evaluated the effects of scan parameters (Kilovoltage peak or kVp, milli-Ampere-second or mAS reconstruction kernels and algorithms, reconstruction field of view, and slice thickness) on image quality, HUs, and the calculated dose in the treatment planning system (TPS). Materials and Methods: A quality dose verification phantom was scanned several times by a 16‑slice Siemens CT scanner. The DOSIsoft ISO gray TPS was applied for dose calculations. The SPSS.24 software was used to analyze the results and the P-value <0.05 was considered significant. Results: Reconstruction kernels and algorithms significantly affected noise, signal‑to‑noise ratio (SNR), and contrast‑to‑noise ratio (CNR). The noise increased and CNR decreased by raising the sharpness of reconstruction kernels. SNR and CNR had considerable increments at iterative reconstruction compared with the filtered back‑projection algorithm. The noise decreased by raising mAS in soft tissues. Also, KVp had a significant effect on HUs. TPS-‑calculated dose variations were less than 2% for mediastinum and backbone and less than 8% for rib. Conclusions: Although HU variation depends on image acquisition parameters across a clinically feasible range, its dosimetric impact on the calculated dose in TPS can be neglected. Hence, it can be concluded that the optimized values of scan parameters can be applied to obtain the maximum diagnostic accuracy and calculate HUs more precisely without affecting the calculated dose in the treatment planning of cancer patients. [ABSTRACT FROM AUTHOR]

Purpose: Accurately segmenting head and neck cancer (HNC) tumors in medical images is crucial for effective treatment planning. However, current methods for HNC segmentation are limited in their accuracy and efficiency. The present study aimed to design a model for segmenting HNC tumors in three-dimensional (3D) positron emission tomography (PET) images using Non-Local Means (NLM) and morphological operations. Material and Methods: The proposed model was tested using data from the HECKTOR challenge public dataset, which included 408 patient images with HNC tumors. NLM was utilized for image noise reduction and preservation of critical image information. Following pre-processing, morphological operations were used to assess the similarity of intensity and edge information within the images. The Dice score, Intersection Over Union (IoU), and accuracy were used to evaluate the manual and predicted segmentation results. Results: The proposed model achieved an average Dice score of 81.47 ± 3.15, IoU of 80 ± 4.5, and accuracy of 94.03 ± 4.44, demonstrating its effectiveness in segmenting HNC tumors in PET images. Conclusions: The proposed algorithm provides the capability to produce patient-specific tumor segmentation without manual interaction, addressing the limitations of current methods for HNC segmentation. The model has the potential to improve treatment planning and aid in the development of personalized medicine. Additionally, this model can be extended to effectively segment other organs from limited annotated medical images. [ABSTRACT FROM AUTHOR]

Background: Medical images of cancer patients are usually evaluated qualitatively by clinical specialists which makes the accuracy of the diagnosis subjective and related to the skills of clinicians. Quantitative methods based on the textural feature analysis may be useful to facilitate such evaluations. This study aimed to analyze the gray level co-occurrence matrix (GLCM)-based texture features extracted from T1-axial magnetic resonance (MR) images of glioblastoma multiform (GBM) patients to determine the distinctive features specific to treatment response or disease progression., Methods: 20 GLCM-based texture features, in addition to mean, standard deviation, entropy, RMS, kurtosis, and skewness were extracted from step I MR images (obtained 72 h after surgery) and step II MR images (obtained three months later). Responded and not responded patients to treatment were classified manually based on the radiological evaluation of step II images. Extracted texture features from Step I and Step II images were analyzed to determine the distinctive features for each group of responsive or progressive diseases. MATLAB 2020 was applied to feature extraction. SPSS version 26 was used for the statistical analysis. P value < 0.05 was considered statistically significant., Results: Despite no statistically significant differences between Step I texture features for two considered groups, almost all step II extracted GLCM-based texture features in addition to entropy M and skewness were significantly different between responsive and progressive disease groups., Conclusions: GLCM-based texture features extracted from MR images of GBM patients can be used with automatic algorithms for the expeditious prediction or interpretation of response to the treatment quantitatively besides qualitative evaluations., Competing Interests: There are no conflicts of interest., (Copyright: © 2023 Journal of Medical Signals & Sensors.)