Intra and peritumoral PET radiomics analysis to predict the pathological response in breast cancer patients receiving neoadjuvant chemotherapy.

OBJECTIVE: The aim of our study was to evaluate the contribution of 18Fluorine-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) radiomic data obtained from both the tumoral and peritumoral area in predicting pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC). METHODS: Female patients with a diagnosis of invasive ductal carcinoma who received NAC were evaluated retrospectively. The volume of interest (VOI) of the primary tumor (VOI-T) was manually segmented, then a voxel-thick VOI was added around VOI-T to define the peritumoral area (VOI-PT). Morphological, intensity-based, histogram and texture parameters were obtained from VOIs. The patients were divided into two groups as pCR and non-complete pathological response (npCR). A "radiomic model" was created with only radiomic features, and a "patho-radiomic model" was created using radiomic features and immunohistochemical data. RESULTS: Of the 66 patients included in the study, 21 were in the pCR group. The only statistically significant feature from the primary tumor among patients with pCR and npCR was Morphological_Compacity-T (AUC: 0.666). Between response groups, a significant difference was detected in 2 morphological, 1 intensity, 4 texture features from VOI-PT; no correlation was found between Morphological_Compacity-PT and NGTDM_contrast-PT. The obtained radiomic model's sensitivity and accuracy values were calculated as 61.9% and 75.8%, respectively (AUC: 0.786). When HER2 status was added, sensitivity and accuracy values of the patho-radiomic model increased to 85.7% and 81.8%, respectively (AUC: 0.903). CONCLUSIONS: Evaluation of PET peritumoral radiomic features together with the primary tumor, rather than just the primary tumor, provides a better prediction of the pCR to NAC in patients with breast cancer.

A different perspective on PET/CT before treatment in patients with Hodgkin lymphoma: importance of volumetric and dissemination parameters.

The aim of this study is to investigate the role of the combination of volumetric and dissemination parameters obtained from pretreatment 18-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in predicting the interim response and progression status in patients with Hodgkin lymphoma (HL). Pretreatment PET/CT images of HL patients were analyzed with LIFEx software, and volumes of interest (VOIs) were drawn with a fixed SUV 4.0 threshold. MTV, SUVmax, and TLG values were obtained from each VOI. Total MTV (tMTV) was calculated by summing the MTV values in all VOIs, and similarly, total TLG (tTLG) was obtained by summing the TLG values. The distance between the centers of the lesions was noted as Dmax, and the distance between the outermost voxels of the lesions as DmaxVox. tMTV/DmaxVox was calculated by dividing the tMTV value by the DmaxVox value, and tTLG/DmaxVox was calculated by dividing the tTLG value by the DmaxVox value. The correlation of pretreatment PET parameters with response groups (complete/poor) and relapse/progression status (stable/progressive) was statistically evaluated. A total of 52 patients were included in the study. Bulky disease, tMTV, tTLG, and tMTV/DmaxVox values were found to be significantly higher in the poor response group. tMTV > 190.60 ml was found to be the only prognostic factor predicting interim PET response. The tMTV/DmaxVox and tTLG/DmaxVox showed statistically significant differences between the groups with and without progression. tMTV/DmaxVox > 7.70 was found to be the only prognostic factor in predicting relapse/progression. The evaluation of tumor burden and dissemination together in 18F-FDG PET/CT before treatment in patients with HL can help us to predict the results of the patients.

A different perspective on 18F-FDG PET radiomics in colorectal cancer patients: The relationship between intra & peritumoral analysis and pathological findings.

OBJECTIVE: We aimed to determine the value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) based primary tumoral and peritumoral radiomics in the prediction of tumor deposits (TDs), tumor budding (TB) and extramural venous invasion (EMVI) of colorectal cancer (CRC). METHODS: Our retrospective study included 77 CRC patients who had preoperative 18F-FDG PET/CT between June 2020 and February 2022. A total of 131 radiomic features were extracted from primary tumors and peritumoral areas on PET/CT fusion images. The relationship between TDs, TB, EMVI and T stage in the postoperative pathology of the tumors and radiomic features was investigated. Features with a correlation coefficient (CC) less than 0.8 were analyzed by logistic regression. The area under curve (AUC) obtained from the receiver operating characteristic analysis was used to measure the model performance. RESULTS: A model was developed from primary tumoral and peritumoral radiomics data to predict T stage (AUC 0.931), and also a predictive model was constructed from primary tumor derived radiomics to predict EMVI (AUC 0.739). Radiomic data derived from the primary tumor was obtained as a predictive prognostic factor in predicting TDs and a peritumoral feature was found to be a prognostic factor in predicting TB. CONCLUSIONS: Intratumoral and peritumoral radiomics derived from 18F-FDG PET/CT are useful for non-invasive early prediction of pathological features that have important implications in the management of CRC.

Machine learning approach using 18 F-FDG PET-based radiomics in differentiation of lung adenocarcinoma with bronchoalveolar distribution and infection.

OBJECTIVE: In this study, we aimed to evaluate the role of 18F-fluorodeoxyglucose PET/computerized tomography ( 18 F-FDG PET/CT)-based radiomic features in the differentiation of infection and malignancy in consolidating pulmonary lesions and to develop a prediction model based on radiomic features. MATERIAL AND METHODS: The images of 106 patients who underwent 18 F-FDG PET/CT of consolidated lesions observed in the lung between January 2015 and July 2020 were evaluated using LIFEx software. The region of interest of the lung lesions was determined and volumetric and textural features were obtained. Clinical and radiomic data were evaluated with machine learning algorithms to build a model. RESULTS: There was a significant difference in all standardized uptake value (SUV) parameters and 26 texture features between the infection and cancer groups. The features with a correlation coefficient of less than 0.7 among the significant features were determined as SUV mean , GLZLM_SZE, GLZLM_LZE, GLZLM_SZLGE and GLZLM_ZLNU. These five features were analyzed in the Waikato Environment for Knowledge Analysis program to create a model that could distinguish infection and cancer groups, and the model performance was found to be the highest with logistic regression (area under curve, 0.813; accuracy, 75.7%). The sensitivity and specificity values of the model in distinguishing cancer patients were calculated as 80.6 and 70.6%, respectively. CONCLUSIONS: In our study, we created prediction models based on radiomic analysis of 18 F-FDG PET/CT images. Texture analysis with machine learning algorithms is a noninvasive method that can be useful in the differentiation of infection and malignancy in consolidating lung lesions in the clinical setting.

Evaluation of survival of the patients with metastatic rectal cancer by staging 18F-FDG PET/CT radiomic and volumetric parameters.

OBJECTIVE: The aim of this study is to predict the prognosis in patients with metastatic rectal cancer (mRC) by obtaining a model with machine learning (ML) algorithms through volumetric and radiomic data obtained from baseline 18-Fluorine Fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) images. METHODS: Sixty-two patients with mRC who underwent 18F-FDG PET/CT imaging for staging between January 2015 and January 2021 were evaluated using LIFEx software. The volume of interest (VOI) of the primary tumor was generated and volumetric and textural features were obtained from this VOI. In addition, metabolic tumor volume (tMTV) and total lesion glycolysis (tTLG) values of tumor foci in the whole body. Clinical and radiomic data were evaluated with ML algorithms to create a model that predicts survival. Significant associations between these features and 1-year and 2-year survival were investigated. RESULTS: Random forest algorithm was the most successful algorithm in predicting 2-year survival (AUC: 0.843, PRC: 0.822, and MCC: 0.583). The model obtained with this algorithm was able to predict 49 patients with 79.03% accuracy. While tMTV and tTLG values were successful in predicting 1-year survival (p: 0.002 and 0.007, respectively), texture characteristics from the primary tumor did not show a significant relationship with 1-year survival. CONCLUSIONS: In addition to the important role of 18F-FDG PET/CT in staging patients with mRC, this study shows that it is possible to predict survival with ML methods, with parameters obtained using texture analysis from the primary tumor and whole body volumetric parameters.

The prognostic relationship of 18F-FDG PET/CT metabolic and volumetric parameters in metastatic ALK + NSCLC.

OBJECTIVE: The aim of this study is to determine the role of metabolic and volumetric parameters obtained from 18Fluorine-Fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) imaging on progression-free survival (PFS) and overall survival (OS) in patients with advanced nonsquamous cell lung carcinoma (NSCLC) with anaplastic lymphoma kinase (ALK) rearrangement. METHODS: Pre and post-treatment PET/CT images of the ALK + NSCLC patients between January 2015 and July 2020 were evaluated. The highest standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) values were obtained from pre-tyrosine kinase inhibitor (TKI) basal PET/CT (PETpre) and post-TKI PET/CT (PETpost) images. Total MTV (tMTV) and total TLG (tTLG) values were calculated by summing MTV and TLG values in all tumor foci. The change (Δ) in pSUVmax, pMTV, pTLG, tMTV and tTLG before and after treatment was calculated.The relationship of these parameters with OS and PFS was analyzed. RESULTS: tTLGpre, tMTVpre, pTLGpre, pMTVpre, ∆SUVmax, ∆tMTV and ∆tTLG values were found to be associated with OS; ∆tMTV, ∆tTLG, tTLGpre, tMTVpre, pTLGpre and pMTVpre were associated with PFS. The cutoff values in both predicting OS and PFS were calculated as -31.6 and 391.1 for ∆tMTV and tTLGpre, respectively. In Cox regression analysis, ∆tMTV and stage for OS and ∆tMTV and tTLGpre for PFS were obtained as prognostic factors. CONCLUSIONS: Metabolic and volumetric parameters, especially TLG values in the whole body before treatment and change in whole body MTV value, obtained from PET/CT may be useful in predicting prognosis and determining treatment strategies for patients with advanced ALK + NSCLC.

Volumetric Evaluation of Staging 18F-FDG PET/CT Images in Patients with Esophageal Cancer.

Objectives: The aim of this study was to evaluate the metastatic potential of primary tumor and survival in esophageal cancer (EC) patients by using metabolic tumor volume (MTV) and total lesion glycolysis (TLG) from the staging 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) images. Another aim is to determine a tumor volume-based cut-off value to predict long-term survival. Methods: Medical records of EC patients were retrospectively evaluated. Sixty-two patients with staging 18F-FDG PET/CT and at least five years of follow-up were included in the study. The region of interest to the primary tumor and all metastatic sites was created and MTV and TLG values of the primary tumor (MTVp, TLGp) and total tumor volume (MTVt and TLGt) values were obtained. The relationship between the obtained MTV and TLG values and short-time (one-year) and long time (five-year) survival was investigated. Results: Significant factors on survival were determined as lymph node or distant metastasis (p=0.024, 0.008, respectively) at the staging PET/CT. A significant relationship between volumetric parameters of the primary tumor and total tumor burden (MTVp, TLGp, MTVwb and TLGwb) between survivors and non-survivors for one-year and five-year was detected. In receiver operating characteristics analysis, the most significant volumetric parameter was MTVwb, with area under curve 0.771 in estimated five-year survival. The best cut-off value was detected as 36.1 mL with 78% sensitivity and 75% specificity for MTVwb in determining long-term survivors. Conclusion: Tumor burden in 18F-FDG PET/CT images at the time of staging of patients with EC will contribute to the prediction of long-term survivors.

IMPORTANCE of PRETREATMENT 18F-FDG PET/CT TEXTURE ANALYSIS in PREDICTING EGFR and ALK MUTATION in PATIENTS with NON-SMALL CELL LUNG CANCER.

OBJECTIVE: Identification of anaplastic lymphoma kinase (ALK) and epidermal growth factor receptor (EGFR) mutation types is of great importance before treatment with tyrosine kinase inhibitors (TKIs). Radiomics is a new strategy for noninvasively predicting the genetic status of cancer. We aimed to evaluate the predictive power of 18F-FDG PET/CT-based radiomic features for mutational status before treatment in non-small cell lung cancer (NSCLC) and to develop a predictive model based on radiomic features. METHODS: Images of patients who underwent 18F-FDG PET/CT for initial staging with the diagnosis of NSCLC between January 2015 and July 2020 were evaluated using LIFEx software. The region of interest (ROI) of the primary tumor was established and volumetric and textural features were obtained. Clinical data and radiomic data were evaluated with machine learning (ML) algorithms to create a model. RESULTS: For EGFR mutation prediction, the most successful machine learning algorithm obtained with GLZLM_GLNU and clinical data was Naive Bayes (AUC: 0.751, MCC: 0.347, acc: 71.4%). For ALK rearrangement prediction, the most successful machine learning algorithm obtained with GLCM_correlation, GLZLM_LZHGE and clinical data was evaluated as Naive Bayes (AUC: 0.682, MCC: 0.221, acc: 77.4%). CONCLUSIONS: In our study, we created prediction models based on radiomic analysis of 18F-FDG PET/CT images. Tissue analysis with ML algorithms are non-invasive methods for predicting ALK rearrangement and EGFR mutation status in NSCLC, which may be useful for targeted therapy selection in a clinical setting.

Tracheitis Diagnosed With 68 Ga-PSMA PET/CT in a Patient With COVID-19.

ABSTRACT: A 57-year-old man with newly diagnosed with prostate cancer was admitted to our department for 68 Ga-prostate-specific membrane antigen PET/CT imaging. The patient, who was asymptomatic at the time of imaging, had increased diffuse 68 Ga-prostate-specific membrane antigen uptake in the trachea on PET/CT. No ground-glass density suggestive of pneumonia in both lungs was observed. The patient, whose symptoms developed 2 days after PET/CT imaging, was diagnosed with coronavirus disease 2019 by real-time polymerase chain reaction.

Adult Tiger Man: A Case of Dermatomyositis Associated With Thymoma.

ABSTRACT: The scan demonstrated multiple linear and patchy hypermetabolic lesions in skeletal muscles, mimicking the coat of a tiger, and was referred to as "a tiger man" sign. A 38-year-old man was admitted to our hospital with weakness, shortness of breath, and muscle pain. A CT scan showed a mass in the anterior mediastinum. 18F-FDG PET/CT was performed to evaluate the mass with a preliminary diagnosis of thymoma; heterogeneous 18F-FDG uptake in the mediastinal mass and tiger man findings in skeletal muscles were observed in PET/CT images.

The Evaluation of Preoperative 18F-FDG PET/CT in Patients with Endometrial Cancer and the Correlation Between PET Parameters and Postoperative Pathology Results.

OBJECTIVES: Endometrial cancer (EC) is the most common gynecological malignancy. The 18fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) is used for initial staging, evaluating treatment response, and detecting recurrence. This study aimed to investigate the diagnostic value of preoperative PET/CT in EC staging and determine the volumetric PET parameters that are accurate predictors of histopathological tumor characteristics. METHODS: Preoperative PET/CT data of 66 patients with EC were retrospectively analyzed. Patients were divided into low and high-risk groups according to the European Society for Medical Oncology criteria. The maximum standardized uptake value (SUVmax), SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the primary lesion and pathological lymph nodes were noted. The International Federation of Gynecology and Obstetrics (FIGO) classifications, histopathology, the depth of myometrial invasion (MI), lymph node metastasis (LNM), cervical stromal invasion (CSI), and tumor sizes were noted. RESULTS: The SUVmax, TLG, and MTV values of high and low-risk groups were significantly different. TLG was the most useful parameter in differentiating risk groups. PET/CT had 90% sensitivity, 96.3% specificity, 81.8% positive predictive value, 98.1% negative predictive value, and 95.45% accuracy in assessing LNM. MTV and TLG values in patients with non-endometrioid pathology were higher than those with endometrioid. The SUVmax, MTV, and TLG of patients with deep MI were higher than those with superficial MI. TLG values of patients with CSI were higher than those without CSI. Patients with LNM had higher MTV and TLG values than those without LNM. A significant difference was found in TLG, MTV, and SUVmax values between patients with FIGO stage I-II and patients with FIGO stage III and above. CONCLUSION: SUV and volumetric parameters obtained from PET/CT, especially TLG, are strong predictors of tumor characteristics, such as MI and CSI, FIGO stages, and LNM, and are useful in noninvasively defining the risk groups in the preoperative period.

Prediction of early biochemical response after 177Lu-PSMA radioligand therapy with 68Ga-PSMA PET, a different perspective with quantitative parameters.

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). METHODS: The 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-TVT and TL-PSMAT). The distance between the two furthest lesions (Dmax) 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, Dmax, PSMA-TVT and TL-PSMAT values were obtained at lower levels in the progressed group. In multivariate analysis, only Dmax was found to be a prognostic factor in predicting early biochemical response. CONCLUSION: Dmax 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.

Dual time point imaging of staging PSMA PET/CT quantification; spread and radiomic analyses.

OBJECTIVE: The aims were to evaluate the performance of models that predict Gleason Grade (GG) groups with radiomic data obtained from the prostate gland in dual time 68Ga-Prostate Specific Membrane Antigen (PSMA) Positron Emission Tomography/Computerized Tomography (PET/CT) images for prostate cancer (PCa) staging, and to analyze the contribution of late imaging to the radiomic model and to evaluate the relationship of the distance between tumor foci in the body (Dmax) obtained in early PET images with histopathology and prostate specific antigen (PSA) value. METHODS: Between October 2020 and August 2021, 41 patients who underwent 68Ga-PSMA PET/CT for staging of PCa were retrospectively analyzed. Volumetric and radiomics data were obtained from early and late PSMA PET images. The differences between age, metastasis status, PSA, standard uptake value (SUV), volumetric and radiomics parameters between GG groups were analyzed. Early and late PET radiomic models were created, area under curve (AUC), sensitivity, specificity and accuracy values of the models were obtained. In addition, the correlation of Dmax values with total PSMA-tumor volume (TV), Total lesion (TL)-PSMA and PSA values was evaluated. In metastatic patients, the difference in Dmax between GG groups was analyzed. RESULTS: There was a significant difference between patients with GG ≤ 3 and > 3 in 35 of the early PET radiomic features. In the early PET model, multivariate analyses showed that GLRLM_RLNU and PSA were the most meaningful parameters. The AUC, sensitivity, specificity and accuracy values of the early model in detecting patients with GG > 3 were calculated as 0.902, 76.2%, 84% and 78.1%, respectively. In 36 late PET radiomic features, there was a significant difference between patients with GG ≤ 3 and > 3. In multivariate analyses; SHAPE_compacity and PSA were obtained as the most meaningful parameters. The AUC, sensitivity, specificity and accuracy values of the late model in detecting patients with GG > 3 were calculated as 0.924, 85.7%, 85% and 85.4%. There was a strong correlation between Dmax and PSA values (p < 0.001, rho: 0.793). Dmax showed strong correlation with PSMA-TVtotal and TL-PSMAtotal (p < 0.001, rho: 0.797; p < 0.001, rho: 0.763, respectively). In patients with metastasis, median Dmax values of the GG > 3 group were higher than GG ≤ 3 group; A statistically significant difference was obtained between these two groups (p = 0.023). CONCLUSIONS: Model generated from the late PSMA PET radiomic data had better performance in the current study. Without the use of invasive methods, the heterogeneity and aggressiveness of the primary tumor and the prediction of GG groups may be possible with 68Ga-PSMA PET/CT images obtained for diagnostic purposes especially with late PSMA PET/CT imaging.

Interreader agreement in evaluation of 68Ga-PSMA PET/CT at the time of initial staging: comparison of the three evaluation criteria in the pretreatment risk groups.

OBJECTIVE: The aim of this study was to assess the interreader agreement in evaluation 68Ga-prostate-specific membrane antigen (PSMA) PET/CT according to three current criteria European association of nuclear medicine, PROMISE with miTNM, and PSMA-RADS in newly diagnosed prostate cancer (PC) patients. METHODS: The images of 101 patients who had been diagnosed with PC and underwent 68Ga-PSMA PET/CT at the time of initial staging were evaluated according to the three interpretation criteria by two nuclear medicine specialists. Local tumor, pelvic lymph node metastasis and distant metastasis were evaluated separately. Abdominal lymph nodes, bone and visceral organ metastases were additionally evaluated as subregions of distant metastatic sites. Patients were evaluated in subgroups Gleason score ≥8 or prostate-specific antigen ≥20 ng/mL as the high-risk group (HR) and prostate-specific antigen ≤ 20 ng/mL and Gleason score <8 as the low-risk group (LR). To measure interreader agreement for each judgment site Cohen's Kappa statistic coefficient (κ) was calculated. RESULTS: All three criteria European association of nuclear medicine, PROMISE with miTNM and PSMA-RADS exhibit substantial and almost perfect agreement between the readers in all sites except for PSMA-RADS in bone and visceral metastasis (κ = 0.495, κ = 0.506, respectively). According to the risk groups, a remarkable difference in interreader agreement for bone metastasis for all three criteria (especially in PSMA-RADS) between the HR and LR patients was detected. CONCLUSIONS: In low-risk patients especially PSMA-RADS criteria leads to increased interreader reporting differences. While evaluating 68Ga-PSMA PET/CT images it should be considered that pretreatment risk levels of PC patients could affect the interreader agreement.

Is SUV Corrected for Lean Body Mass Superior to SUV of Body Weight in 68Ga-PSMA PET/CT?

Objectives: This study aimed to investigate the relationship between the standard uptake value (SUV) of body weight and SUV corrected for lean body mass (SUL) parameters obtained from the prostate gland in gallium-68 (68Ga)-prostate-specific membrane antigen (PSMA) positron emission tomography-computed tomography (PET/CT) with Gleason grade (GG) groups, D'Amico risk groups, and presence of metastases. Methods: Patients with prostate adenocarcinoma who underwent 68Ga-PSMA PET/CT for staging at our center between February 2017 and October 2018 were evaluated retrospectively. Maximum SUV (SUVmax), SUVpeak, SULmax, SULpeak, SUVmean, and SULmean values of the prostate tumor were obtained. The difference in these values between GG groups (≥3, <3) and D'Amico risk (low-moderate/high) groups was evaluated with the Mann-Whitney U test. The area under the curve values of SUV and SUL parameters were compared. In addition, SUVmean and SULmean values were obtained from the right liver lobe, and their correlation with body weight was evaluated. Results: A total of 79 patients were included in the study. Significant differences were found in the prostate SUVmax, SULmax, SUVpeak, SULpeak, SUVmean, and SULmean values between the GG (≥3 and <3) groups and between D'Amico risk (low-moderate and high) groups. However, no significant difference was found in the discriminative power of any SUV or SUL parameter when compared with each other. A significant difference in any SUV and SUL parameters was found in patients with and without metastasis. Neither liver SUVmean value nor SULmean value correlated with the body weight. Conclusion: The superiority of SUL values obtained from 68Ga-PSMA PET to SUV was not determined in our study. SUV parameters can also be used for quantitative analysis in 68Ga-PSMA PET.

A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods.

OBJECTIVE: This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline 18F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to predict survival and histopathology. METHODS: The initial staging 18F-FDG PET/CT images obtained on newly diagnosed EC patients between January 2008 and June 2019 were evaluated using LIFEx software. A region of interest (ROI) of the primary tumor was created and volumetric and textural features were obtained. A significant relationship between these features and pathological subtypes, 1-year, and 5-year survival was investigated. Due to the nonhomogeneity of the data, nonparametric test (The Mann-Whitney U test) was used for each feature, in pairwise comparisons of independent variables. A p value of < 0.05 was considered significant. Receiver operating curve (ROC) analysis was performed for features with p < 0.05. Correlation between the significant features was evaluated with Spearman correlation test; features with correlation coefficient < 0.8 were evaluated with several ML algorithms. RESULTS: In predicting survival in a 1-year follow-up J48 was obtained as the most successful algorithm (AUC: 0.581, PRC: 0.565, MCC: 0.258, acc: 64.29%). 5-year survival results were more promising than 1-year survival results with (AUC: 0.820, PRC: 0.860, MCC: 271, acc: 81.36%) by logistic regression. It is revealed that the most successful algorithm was naive bayes (AUC: 0.680 PRC: 0.776, MCC: 0.298, acc: 82.66%) in the histopathological discrimination. CONCLUSION: Texture analysis with ML algorithms could be predictive of overall survival and discriminating histopathological subtypes of EC.

Correlations of 68Ga-PSMA PET/CT in the initial staging of prostate cancer patients.

OBJECTIVE: The aim of this study was to evaluate the correlations between the different risk groups of prostate cancer (PCa) regarding the presence of metastasis and the gallium-68 prostate specific membrane antigen (68Ga-PSMA) uptake patterns in the prostate gland. MATERIALS AND METHODS: One hundred thirty nine patients with newly diagnosed, untreated PCa who underwent 68Ga-PSMA PET/CT imaging for staging between July 2017 and March 2019 were enrolled in this retrospective study. Maximum standardized uptake values (SUVmax) were determined by manually placing the region of interest to the primary tumor in the prostate gland. Patients were divided into groups according to their prostate-specific antigen (PSA) values, International Society of Urological Pathology (ISUP) grade groups, Gleason Scores (GS), D'Amico risk stratification criteria and the presence of metastasis. Mann Whitney U test was used in the comparison of two groups of data. In multivariate analysis, logistic regression was used to determine independent predictors for the presence of metastasis. RESULTS: There were statistically significant differences between D'Amico risk groups in terms of prostate SUVmax levels. The SUVmax levels of the patients in the high risk group were significantly higher than the SUVmax levels of the patients in the low-medium risk groups (P<0.001). Maximum standardized uptake value levels of the patients with PSA level 20ng/mL and above were significantly higher than those with PSA level below 20ng/mL (P<0.001). The metastatic rate of patients with 68Ga-PSMA uptake on two lobes of the prostate gland was significantly higher (42.6%) than the metastatic rate of patients with 68Ga-PSMA uptake on only one lobe (7.9%) (P<0.001).The median SUVmax of tumours in patients with metastasis was statistically significantly higher than in patients with no metastasis. In multivariate analysis; bilobar involvement, PSA value 20ng/mL, prostate SUVmax value 8.6 and GS 8 were determined as independent predictors for the presence of metastasis. CONCLUSION: The strong correlation between PSA values and/or Gleason score/Grade and SUVmax values suggests that the SUVmax value of the prostate gland might be a valuable determinant in risk classifications.

Evaluation of 68Ga-PSMA PET/CT with volumetric parameters for staging of prostate cancer patients.

BACKGROUND: The aim of this study was to investigate the relationship between volumetric data obtained from staging 68Ga-prostate-specific membrane antigen (PSMA) PET computerized tomography (CT) images with prostate-specific antigen (PSA), risk groups, Gleason Grade (GG) groups and presence of metastasis. METHODS: We performed a retrospective analysis of 68Ga-PSMA PET-CT images from 88 patients undergoing initial staging of prostate adenocarcinoma between January 2015 and September 2018. Images were evaluated in LIFEx software; PSMA involvement above the background activity in prostate gland, lymph node and other distant metastases was plotted with 40% SUVmax threshold, SUVmax, PSMA tumor volume (PSMA-TV) and total lesion PSMA (TL-PSMA) values were obtained. RESULTS: In all patients, there was a moderate correlation between PSA and PSMA-tumor volume whole-body (PSMA-TVwb) (P < 0.001, r = 0.580) and a high correlation between total lesion-PSMAwb (TL-PSMAwb) (P < 0.001, r = 0.636). Prostate PSMA-TV (PSMA-TVp) and TL-PSMA (PSMA-TVp) values were different in local and locally advanced/metastatic patients (P = 0.020 and 0.006, respectively). PSMA-TVp and TL-PSMAp values were significantly different in low-moderate and high-risk patients (P = 0.003 and <0.001, respectively), and in patients with and without metastasis (P = 0.008 and <0.001, respectively). PSMA-TVp, PSMA-TVwb, TL-PSMAp and TL-PSMAwb values were significantly different in patients with GG ≤3 and >3 (P = 0.030, 0.002, <0.001 and <0.001, respectively). CONCLUSION: Pretreatment 68Ga-PSMA PET/CT volumetric parameters provides unique data to use in the clinical decision-making process of patients with adenocarcinoma of the prostate.

Applying postoperative radioiodine therapy before 3 months seems to decrease ablation success in patients with differentiated thyroid carcinoma.

OBJECTIVES: Radioiodine can be applied for remnant ablation in low and low to intermediate-risk patients with differentiated thyroid cancer (DTC). A controversy still exists about the application time interval of radioiodine following total thyroidectomy. In this study, we investigated the effect of radioiodine (RAI) therapy timing on the success rates of the ablation. METHODS: We retrospectively reviewed the data of DTC patients who underwent total thyroidectomy and were treated with radioiodine remnant ablation during 2013-2017. Because the objective of this study was to determine the success of ablation according to the postoperative RAI therapy timing, any patients with a pathologic uptake outside the thyroid bed as well as high-risk patients determined before and at RAI therapy were excluded from the study. Finally, 503 patients with low and low to intermediate-risk groups were included in the study. Successful ablation was defined as no visible focal uptake on the neck on I-131 whole body scan with stimulated thyroglobulin (Tg) level of < 1 ng/mL and a normal or undetectable antithyroglobulin antibody (ATG). The time interval from total thyroidectomy to RAI therapy (titRAI) was calculated as months for each patient. RESULTS: A total of 115 (22.9%) patients were in the low to intermediate-risk group whereas most of the patients were at the low-risk group according to the American Thyroid Association (ATA) 2015. Successful ablation was observed in 388 (77.1%) patients. The titRAI was ≤ 3 months in 151 (30.0%) patients and > 3 months in 352 (70.0%) patients. The ratio of successful ablation was statistically higher in patients with a titRAI > 3 months (81.2% of patients) than in patients with ≤ 3 months (67.5% of patients) (χ2 11.247, p 0.001). The rate of successful ablation was 20.3% higher in patients treated after 3 months. There was no statistical difference when titRAI cut off was reduced to 2 months (p > 0.5). CONCLUSION: Investigated the effect of radioiodine therapy initiated before 3 months after total thyroidectomy and it seems to decrease ablation success.

Street design scenarios using vegetation for sustainable thermal comfort in Erzurum, Turkey.

Urbanization models that do not comply with the planning criteria are affecting human lives. In urban areas, street trees have positive contributions to the ecosystem and human thermal comfort. In this study, the thermal comfort of the main streets that connect people to each other and provide access and transportation has been thermally explored. Cumhuriyet Street, which is one of the vibrant streets in Erzurum, was selected as a case study scenario in the winter and summer periods in 2018 by using the ENVI-met V. 4.4.2 winter model. A different green scenario is proposed, and the best thermal comfort scenario in both seasons is determined. The results show that, in the summer period, the air temperature of the greener street scenario is about 1.0 °C cooler than the existing condition and about 2.0 °C warmer in the winter period. Physiological equivalent temperature (PET) value was better in narrow canyon streets in winter months, but in wide canyon streets in summer months. The green scenarios of wide canyon streets positively affect the outdoor thermal comfort in both seasons. These results clearly imply that green streets are an appropriate strategy for city streets that suffer from discomfort levels in cold winter and hot summer periods. It has been concluded that it is possible to increase thermal comfort through improvement in the open space in street and more suitable plant preferences for livable urbanization. Planning streets in a new city characterized by summer and winter seasons should take into consideration an accurate decision for providing a thermal comfort level and healthy urbanization.