Diagnostic Value of PET/CT in Comparison with Other Conventional Imaging Modalities for the Evaluation of Breast Cancer Recurrence: A Systematic Review of the Literature

Background: Despite developments in surgical treatment, radiation therapy, and chemotherapy protocols, tumor recurrence and metastasis are still major problems in breast cancer management. The aim of the present report was to review and compare the performance of PET/CT with some of the conventional imaging modalities in detection of breast cancer recurrence. Methods: A literature search was performed in PubMed, Europe PMC and ScienceDirect databases with no search restriction for the date of publication but the search was limited to papers published in English. Results: Twenty-two studies including a total of 1378 patients with prior breast cancer and clinical suspicion of recurrence that assessed the sensitivity, specificity, and accuracy of PET/CT and other conventional imaging methods in followed up by treated breast cancer and presented the results in systematic review format. The information extracted from each article included the first author, publication year, number of patients and their characteristics, index test(s), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Conclusions: According to the literature, PET/CT seems to be a more useful modality than current techniques to assess the patients with suspected recurrent and metastatic breast cancer. If PET/CT is not applicable, MRI and also bone scintigraphy could also be performed as alternatives

The Applications of Ultrasound Microbubbles in Molecular Diagnosis and Therapy

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Influence of the contrast agents on treatment planning dose calculations of prostate and rectal cancers

AimThe aim of the present study is to quantify differences in dose calculations caused by using CA and determine if the resulting differences are clinically significant.BackgroundThe influence of contrast agents (CA) on radiation dose calculations must be taken into account in treatment planning.Materials and methodsEleven patients with pelvic cancers were included in this study and two sets of CTs were taken for each patient (without and with CA) in the same position and coordinates. Both sets of images were transferred to the DosiSoft ISOgray treatment planning system for contouring and calculating the dose distribution and monitor units (MUs) with Collapsed Cone and Superposition algorithms, respectively. All plans were generated on pre-contrast CT and subsequently copied to the post-contrast CT. Radiation dose calculations from the two sets of CTs were compared using a paired sample t-test.ResultsThe results showed a statistically insignificant difference between pre- and post-contrast CT treatment plans for target volume and OARs (p[[ce:hsp sp="0.25"/]]>[[ce:hsp sp="0.25"/]]0.05), except bladder organ in the prostate region (p[[ce:hsp sp="0.25"/]

Influence of the contrast agents on dose-volume histograms in radiotherapy treatment planning based on CT-scan

Background: In three-dimensional conformal radiation therapy (3D-CRT), contrast-enhanced CT (CECT) image is commonly used to assist radiation oncologists in diagnosing regions of interest, so that normal and target tissues can be better delineated. CECT causes the temporary increase in the CT number and the corresponding electron density (ρe). Administrated contrast agents (CA) during CT simulation and altering the ρe of structures can be effective on radiation calculations and dose-volume histograms (DVHs) in radiotherapy treatment planning. Therefore, present study was designed and performed to determine the influence of the administrated CA on DVHs. Methods: Current study performed as a self-controlled clinical trial study with before/after method at Imam Reza Hospital, Kermanshah City, during the period from June 2015 till August 2016. Ten patients with pelvic cancer included in this study through simple sampling. Cases with prior reactions to CA, diabetes, renal diseases, and asthma were excluded. Two sets of CT-scans were taken for each patient in the same position and coordinates. Primary study sets contained pre-contrast images and secondary study sets were performed post-contrast. Both sets of CT images were transferred to the treatment planning system (ISOgray® software, Version 4.1.3.23 L, DOSIsoft®, Cachan, France). All treatment plans were generated on pre-contrast and subsequently copied to the post-contrast CT. Quantitative calculations were performed in treatment planning including the difference in ρe before and after CA administration. Results: The prostate (1.27%), the bladder (0.62-0.79%) and the rectum (0.43-0.56%) showed the largest changes in average ρe increase. The results confirm the expected relationship of increasing attenuation, CT number, and ρe with increased tissue density due to the CA. However, the DVHs showed insignificant difference between pre-and post-contrast CTs for 18 MV photon beam. Conclusion: The results showed statistical insignificant difference between with and without CA CTs treatment plan in pelvic field for targets and OARs. These results may serve as a reference to justify the use of CECT data sets for 3D-CRT planning of pelvic region cancers using DosiSoft ISOgray system

Thyroid function following radiation therapy in breast cancer patients: risk of radiation-induced hypothyroidism

Background: Radiation exposure to the thyroid gland seems unavoidable in breast cancer (BC) patients receiving radiation therapy (RT) to the supraclavicular (SC) region. Hence, this study aimed to evaluate the effects of SC region RT on thyroid function and the prevalence of radiation-induced hypothyroidism (RIHT) in BC patients at regular intervals post-treatment. Materials and methods: Twenty-one patients with BC were enrolled in this analytical cross-sectional study by simple and convenient sampling, from March 2019 to March 2020. Thyroid function and the prevalence of RIHT were evaluated and compared by measuring the serum of thyroid-stimulating hormone (TSH) and free thyroxine hormone (fT4) levels before radiation therapy (pre-RT) and 3 and 6 months after radiation therapy (post-RT). The patients underwent 3 dimensional conformal. radiation therapy (3D CRT) of breast/chest wall, axillary, and supraclavicular lymph nodes with 50 Gy/25 fractions/5 weeks. The collected data were analyzed using SPSS software (version 20). Results: Serum levels of TSH increased at 3 and 6 months post-RT, this increase was not statistically significant (p > 0.05). Nevertheless, serum levels of fT4 were significantly elevated at 3 and 6 months post-RT (p < 0.01). A correlation was observed between the follow-up period and the incidence of RIHT, where it was 0% at 3 months and 9.5% at 6 months post-RT. RIHT was not significantly associated with any factors, including patient's age, type of surgery, thyroid gland dose, and thyroid gland volume. Conclusions: It seems that SC region RT does not have a significant adverse effect on the thyroid function among BC patients at 3 and 6 months post-treatment. Hence, a long-term follow-up with a larger sample size is suggested

Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI

Abstract Background The purpose of this study is to investigate the use of radiomics and deep features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading prostate cancer. We propose a novel approach called multi-flavored feature extraction or tensor, which combines four mpMRI images using eight different fusion techniques to create 52 images or datasets for each patient. We evaluate the effectiveness of this approach in grading prostate cancer and compare it to traditional methods. Methods We used the PROSTATEx-2 dataset consisting of 111 patients’ images from T2W-transverse, T2W-sagittal, DWI, and ADC images. We used eight fusion techniques to merge T2W, DWI, and ADC images, namely Laplacian Pyramid, Ratio of the low-pass pyramid, Discrete Wavelet Transform, Dual-Tree Complex Wavelet Transform, Curvelet Transform, Wavelet Fusion, Weighted Fusion, and Principal Component Analysis. Prostate cancer images were manually segmented, and radiomics features were extracted using the Pyradiomics library in Python. We also used an Autoencoder for deep feature extraction. We used five different feature sets to train the classifiers: all radiomics features, all deep features, radiomics features linked with PCA, deep features linked with PCA, and a combination of radiomics and deep features. We processed the data, including balancing, standardization, PCA, correlation, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Finally, we used nine classifiers to classify different Gleason grades. Results Our results show that the SVM classifier with deep features linked with PCA achieved the most promising results, with an AUC of 0.94 and a balanced accuracy of 0.79. Logistic regression performed best when using only the deep features, with an AUC of 0.93 and balanced accuracy of 0.76. Gaussian Naive Bayes had lower performance compared to other classifiers, while KNN achieved high performance using deep features linked with PCA. Random Forest performed well with the combination of deep features and radiomics features, achieving an AUC of 0.94 and balanced accuracy of 0.76. The Voting classifiers showed higher performance when using only the deep features, with Voting 2 achieving the highest performance, with an AUC of 0.95 and balanced accuracy of 0.78. Conclusion Our study concludes that the proposed multi-flavored feature extraction or tensor approach using radiomics and deep features can be an effective method for grading prostate cancer. Our findings suggest that deep features may be more effective than radiomics features alone in accurately classifying prostate cancer