Colorectal cancer is the third most common cancer worldwide with a high mortality rate at the advanced stages. However, colorectal cancer is not a single type of tumor; its pathogenesis depends on the anatomical location of the tumor and differs between right side and left side of the colon. Tumors in the proximal colon (right side) and distal colon (left side) exhibit different molecular characteristics and histology. In the right-sided tumors, mutations in the DNA mismatch repair pathway are commonly observed; and these tumors generally have a flat histology. In the left-sided tumors, chromosomal instability pathway-related mutations, such as KRAS, APC, PIK3CA, p53 mutations are observed and these tumors demonstrate polypoid-like morphology. Therapy responses are totally different between these tumor entities. Left-sided colorectal cancer (LCRC) patients benefit more from adjuvant chemotherapies such as 5-fluorouracil (5-FU)-based regimes, and targeted therapies such as anti- epidermal growth factor receptor (EGFR) therapy, and have a better prognosis. Right-sided colorectal cancer (RCRC) patients do not respond well to conventional chemotherapies, but demonstrate more promising results with immunotherapies because these tumors have high antigenic load. For the development of effective therapy regimes and better treatment options, it is essential to evaluate right-sided and left-sided tumors as separate entities, and design the therapy regime considering the differences between these tumors.

Objective: Using CT texture analysis and machine learning methods, this study aims to distinguish the lesions imaged via 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/CT as metastatic and completely responded in patients with known bone metastasis and who were previously treated. Methods: We retrospectively reviewed the 68Ga-PSMA PET/CT images of 75 patients after treatment, who were previously diagnosed with prostate cancer and had known bone metastasis. A texture analysis was performed on the metastatic lesions showing PSMA expression and completely responded sclerotic lesions without PSMA expression through CT images. Textural features were compared in two groups. Thus, the distinction of metastasis/completely responded lesions and the most effective parameters in this issue were determined by using various methods [decision tree, discriminant analysis, support vector machine (SVM), k-nearest neighbor (KNN), ensemble classifier] in machine learning. Results: In 28 of the 35 texture analysis findings, there was a statistically significant difference between the two groups. The Weighted KNN method had the highest accuracy and area under the curve, has been chosen as the best model. The weighted KNN algorithm was succeeded to differentiate sclerotic lesion from metastasis or completely responded lesions with 0.76 area under the curve. GLZLM_SZHGE and histogram-based kurtosis were found to be the most important parameters in differentiating metastatic and completely responded sclerotic lesions. Conclusions: Metastatic lesions and completely responded sclerosis areas in CT images, as determined by 68Ga-PSMA PET, could be distinguished with good accuracy using texture analysis and machine learning (Weighted KNN algorithm) in prostate cancer. Advances in knowledge: Our findings suggest that, with the use of newly emerging software, CT imaging can contribute to identifying the metastatic lesions in prostate cancer.

Purpose The aim of this study was to evaluate the ability of 18 F-FDG PET/CT texture analysis to predict the exact pathological outcome of thyroid incidentalomas. Methods 18 F-FDG PET/CT images between March 2010 and September 2018 were retrospectively reviewed in patients with focal 18 F-FDG uptake in the thyroid gland and who underwent fine needle aspiration biopsy from this area. The focal uptake in the thyroid gland was drawn in 3D with 40% SUVmax threshold. Features were extracted from volume of interest (VOI) using the LIFEx package. The features obtained were compared in benign and malignant groups, and statistically significant variables were evaluated by receiver operating curve (ROC) analysis. The correlation between the variables with area under curve (AUC) value over 0.7 was examined; variables with correlation coefficient less than 0.6 were evaluated with machine learning algorithms.Results Sixty patients (70% train set, 30% test set) were included in the study. In univariate analysis, a statistically significant difference was observed in 6 conventional parameters, 5 first-, and 16 second-order features between benign and malignant groups in train set (p < 0.05). The feature with the highest benign-malignant discriminating power was GLRLM RLNU (AUC:0.827). AUC value of SUVmax was calculated as 0.758. GLRLM RLNU and SUVmax were evaluated to build a model to predict the exact pathology outcome. Random forest algorithm showed the best accuracy and AUC (78.6% and 0.849, respectively). Conclusion In the differentiation of benign-malignant thyroid incidentalomas, GLRLM RLNU and SUVmax combination may be more useful than SUVmax to predict the outcome.

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Prostate large cell neuroendocrine tumor is a rare disease. In this case, metastatic areas showing FDG uptake, somatostatin receptor positivity, and PSMA expression are shown in 18 F-FDG PET/CT, 68 Ga-DOTATATE PET/CT, and 68 Ga-PSMA PET/CT in a 70-year-old man with the diagnosis of prostate large cell neuroendocrine carcinoma.

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Objective: The aim of this study was to compare images from Tc-99m MDP bone scan (BS) and Ga-68 PSMA PET/CT of patients with prostate cancer in terms of bone metastases. Methods: Overall, 34 patients exhibited a mean age of 66 ± 9.5 (50-88) years, mean PSA of 51 ± 159ng/ml (0-912), and mean Gleason score of 8 (6-9). BS and Ga-68 PSMA PET/CT were applied to 34 patients within 30 days, and the results were evaluated, retrospectively. In both tests, radiopharmaceutical uptake in bones were compared. Results: In 7 patients (20.5%), uptake was not significant on BS and Ga-68 PSMA PET / CT images, which is related to metastasis. In 14 (41%) patients, bone metastases were observed in both examinations. However, more metastatic lesions were observed in the Ga-68 PSMA PET/CT of 3 patients and in the bone scintigraphy of 2 patients. PSMA expression was not observed on Ga-68 PSMA PET / CT in 13 (38%) patients with increased activity in bone scintigraphy. Two (6%) of these patients were thought to be metastatic, 2 (6%) were suspicious for metastasis, and 9 (26%) had no metastasis. When a lesion-based evaluation was performed, a total of 480 activities were evaluated: increased activity uptake was found in 305 BS, and 427 PSMA expression activity was detected. Furthermore, 435 of these activities were evaluated as metastatic. Conclusion: Ga-68 PSMA PET/CT provides an additional contribution to the BS evaluation of activity areas because of the presence of PSMA expression and anatomical lesions. In 6% of the patients, activity on BS and metastatic appearance in CT images were observed and the presence of lesions in the absence of PSMA was determined. This suggests that bone metastases without PSMA expression may also be present.

Objective In this study, our aim was to evaluate the relationship of the quantitative data obtained from pretreatment 68Ga prostate-specific membrane antigen (PSMA) PET-computerized tomography (PET/CT) with treatment response of the patients with the diagnosis of metastatic castrationresistant prostate cancer (mCRPC) who received 177Lu-PSMA radioligand therapy (RLT). MethodsThe patients who were given three or four cycles of 177Lu-PSMA RLT between January 2016 and June 2018 were evaluated retrospectively. Volumetric data; PSMA tumor volume (TV) and total lesion (TL) PSMA, were obtained from 68Ga-PSMA PET/CT for whole (PSMA-TV T and TL-PSMA T ). The distance between the two furthest lesions (D max ) was calculated. Posttreatment early prostate-specific antigen (PSA) values on the fourteenth day after treatment were obtained. According to the PSA responses, the patients were divided into two groups as progressed and nonprogressed. In univariate analysis, the relationship of PET quantitative data with biochemical response groups was evaluated with Mann-Whitney U test. Logistic regression was used in multivariate analysis.Results A total of 38 patients were included in the study. In univariate analysis, D max , PSMA-TV T and TL-PSMA T values were obtained at lower levels in the progressed group. In multivariate analysis, only D max was found to be a prognostic factor in predicting early biochemical response. Conclusion D max is the most prognostic parameter in predicting the early biochemical response in patients with mCRPC; high total tumor volume and burden are also parameters that give us an idea about the response to treatment. The success rate will be higher if 177Lu-PSMA RLT treatment is planned for patients with higher tumor volume and spread.