Background: The purpose of the present meta‐analysis was to analyze the prevalence of sarcopenia on staging computed tomography (CT) in patients with different malignant solid tumors and in different oncologic settings based on a large sample.Methods: MEDLINE, Cochrane, and SCOPUS literature databases were screened for prevalence of sarcopenia in oncologic patients up to December 2021. Overall, 280 studies met the inclusion criteria. The methodological quality of the involved studies was checked according to the Quality Assessment of Diagnostic Studies instrument. The meta‐analysis was undertaken by using RevMan 5.4 software. DerSimonian and Laird random‐effects models with inverse‐variance weights were used.Results: The included 280 studies comprised 81,814 patients with different tumors. The prevalence of sarcopenia over all included studies was 35.3%. A prevalence of sarcopenia >50% was identified in esophageal cancer, urothelial cancer, cholangiocarcinoma, prostate cancer, sarcomas, and thyroid cancer. In head and neck squamous cell carcinoma, pancreatic cancer, lung cancer, renal cell cancer, and ovarian cancer, the prevalence of sarcopenia varied between 35% and 50%. In colorectal cancer, gastric cancer, hepatocellular cancer, and breast cancer, the prevalence of low skeletal muscle mass was <35%. In curative setting, the prevalence of sarcopenia was 39.6% and in palliative setting, it was 49.2% (P< 0.001).Conclusions: Sarcopenia is a frequent condition in oncology with a prevalence of 35.3%. The prevalence of sarcopenia is higher in palliative setting vs curative setting. The prevalence of sarcopenia is also different in different tumors.

To analyze diffusion-weighted imaging (DWI) findings of meningiomas and to compare them with tumor grade, cell count, and proliferation index and to test a possibility of use of apparent diffusion coefficient (ADC) to differentiate benign from atypical/malignant tumors. Forty-nine meningiomas were analyzed. DWI was done using a multislice single-shot echo-planar imaging sequence. A polygonal region of interest was drawn on ADC maps around the margin of the lesion. In all lesions, minimal ADC values (ADCmin) and mean ADC values (ADCmean) were estimated. Normalized ADC (NADC) was calculated in every case as a ratio ADCmean meningioma/ADCmean white matter. All meningiomas were surgically resected and analyzed histopathologically. The tumor proliferation index was estimated on Ki-67 antigen-stained specimens. Cell density was calculated. Collected data were evaluated by means of descriptive statistics. Analyses of ADC/NADC values were performed by means of two-sided t tests. The mean ADCmean value was higher in grade I meningiomas in comparison to grade II/III tumors (0.96 vs 0.80 × 10(-3) mm(2)s(-1), P = .006). Grade II/III meningiomas showed lower NADC values in comparison to grade I tumors (1.05 vs 1.26, P = .015). There was no significant difference in ADCmin values between grade I and II/III tumors (0.69 vs 0.63 × 10(-3) mm(2)s(-1), P = .539). The estimated cell count varied from 486 to 2091 (mean value, 1158.20 ± 333.74; median value, 1108). There were no significant differences in cell count between grade I and grade II/III tumors (1163.93 vs 1123.86 cells, P = .77). The mean level of the proliferation index was 4.78 ± 5.08%, the range was 1% to 18%, and the median value was 2%. The proliferation index was statistically significant higher in grade II/III meningiomas in comparison to grade I tumors (15.43% vs 3.00%, P = .001). Ki-67 was negatively associated with ADCmean (r = -0.61, P < .001) and NADC (r = -0.60, P < .001). No significant correlations between cell count and ADCmean (r = -0.20, P = .164) or NADC (r = -0.25, P = .079) were found. ADCmin correlated statistically significant with cell count (r = -0.44, P = .002) but not with Ki-67 (r = -0.22, P = .129). Furthermore, the association between ADCmin and cell count was stronger in grade II/III tumors (r = -0.79, P = .036) versus grade I meningiomas (r = -0.41, P = .008). An ADCmean value of less than 0.85 × 10(-3) mm(2)s(-1) was determined as the threshold in differentiating between grade I and grade II/III meningiomas (sensitivity 72.9%, specificity 73.1%, accuracy 73.0%). The positive and negative predictive values were 33.3% and 96.8%, respectively. The same threshold ADCmean value was used in differentiating between tumors with Ki-67 level ≥5% and meningiomas with low proliferation index (Ki-67 <5%). This threshold yielded a sensitivity of 70.6%, a specificity of 81.2%, and an accuracy of 77.6%. The positive and negative predictive values were 66.6% and 83.9%, respectively. Grade II/III tumors had lower ADCmean values than grade I meningiomas. ADCmean correlated negatively with tumor proliferation index and ADCmin with tumor cell count. These associations were different in several meningiomas. ADCmean can be used for distinguishing between benign and atypical/malignant tumors.

To identify potential risk factors for the development of jaw osteoradionecrosis (ORN) after 3D-conformal radiotherapy (3D-CRT) and intensity-modulated radiotherapy (IMRT) among patients with newly diagnosed head and neck cancer. This study included 776 patients who underwent 3D-CRT or IMRT for head and neck cancer at the Department of Radiotherapy at the University Hospital Halle-Wittenberg between 2003 and 2013. Sex, dental status prior to radiotherapy, tumor site, bone surgery during tumor resection, concomitant chemotherapy, and the development of advanced ORN were documented for each patient. ORN was classified as grade 3, 4, or 5 according to the Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer classification or grade 3 or 4 according to the late effects in normal tissues/subjective, objective, management, and analytic scale. The cumulative incidence of ORN was estimated. Cox regression analysis was used to identify prognostic risk factors for the development of ORN. Fifty-one patients developed advanced ORN (relative frequency 6.6 %, cumulative incidence 12.4 %). The highest risk was found in patients who had undergone primary bone surgery during tumor resection (hazard ratio [HR] = 5.87; 95 % confidence interval [CI]: 3.09–11.19) and in patients with tumors located in the oral cavity (HR = 4.69; 95 % CI: 1.33–16.52). Sex, dentition (dentulous vs. edentulous), and chemotherapy had no clinically relevant influence. In contrast to most previous studies, we noted a low cumulative incidence of advanced ORN. Patients with tumors located in the oral cavity and those who undergo bone surgery during tumor resection prior to RT may be considered a high-risk group for the development of ORN.

Background: To date, there are inconsistent data about relationships between diffusion-weighted imaging (DWI) and tumor grading/microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Our purpose was to systematize the reported results regarding the role of DWI in prediction of tumor grading/MVI in HCC. Method: MEDLINE library, Scopus, and Embase data bases were screened up to December 2019. Overall, 29 studies with 2,715 tumors were included into the analysis. There were 20 studies regarding DWI and tumor grading, 8 studies about DWI and MVI, and 1 study investigated DWI, tumor grading, and MVI in HCC. Results: In 21 studies (1,799 tumors), mean apparent diffusion coefficient (ADC) values (ADCmean) were used for distinguishing HCCs. ADCmean of G1–3 lesions overlapped significantly. In 4 studies (461 lesions), minimum ADC (ADCmin) was used. ADCmin values in G1/2 lesions were over 0.80 × 10−3 mm2/s and in G3 tumors below 0.80 × 10−3 mm2/s. In 4 studies (241 tumors), true diffusion (D) was reported. A significant overlapping of D values between G1, G2, and G3 groups was found. ADCmean and MVI were analyzed in 9 studies (1,059 HCCs). ADCmean values of MIV+/MVI− lesions overlapped significantly. ADCmin was used in 4 studies (672 lesions). ADCmin values of MVI+ tumors were in the area under 1.00 × 10−3 mm2/s. In 3 studies (227 tumors), D was used. Also, D values of MVI+ lesions were predominantly in the area under 1.00 × 10−3 mm2/s. Conclusion: ADCmin reflects tumor grading, and ADCmin and D predict MVI in HCC. Therefore, these DWI parameters should be estimated for every HCC lesion for pretreatment tumor stratification. ADCmean cannot predict tumor grading/MVI in HCC.

Background: The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions. Methods: MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength. The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions. Results: The acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10^− 3 mm²/s and the mean value of the benign lesions was 1.5 × 10^− 3 mm²/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10^− 3 mm²/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area). Conclusion: An ADC threshold of 1.00 × 10^− 3 mm²/s can be recommended for distinguishing breast cancers from benign lesions.

Numerous studies have analyzed associations between apparent diffusion coefficient (ADC) and histopathological features such as Ki-67 proliferation index in breast cancer (BC), with mixed results. The purpose of this study was to perform a multicenter analysis to determine relationships between ADC and expression of Ki-67 and tumor grade in BC. For this study, data from six centers were acquired. The sample comprises 870 patients (all female; mean age, 52.6 ± 10.8 years). In every case, breast magnetic resonance imaging with diffusion-weighted imaging was performed. The comparison of ADC values in groups was performed by Mann-Whitney U test where the p values are adjusted for multiple testing (Bonferroni correction). The association between ADC and Ki-67 values was calculated by Spearman’s rank correlation coefficient. Sensitivity, specificity, negative and positive predictive values, accuracy, and AUC were calculated for the diagnostic procedures. ADC thresholds were chosen to maximize the Youden index. Overall, data of 870 patients were acquired for this study. The mean ADC value of the tumors was 0.98 ± 0.22 × 10− 3 mm2 s− 1. ROC analysis showed that it is impossible to differentiate high/moderate grade tumors from grade 1 lesions using ADC values. Youden index identified a threshold ADC value of 1.03 with a sensitivity of 56.2% and specificity of 67.9%. The positive predictive value was 18.2%, and the negative predictive value was 92.4%. The level of the Ki-67 proliferation index was available for 845 patients. The mean value was 12.33 ± 21.77%. ADC correlated with weak statistical significant with expression of Ki-67 (p = − 0.202, p < 0.001). ROC analysis was performed to distinguish tumors with high proliferative potential from tumors with low expression of Ki-67 using ADC values. Youden index identified a threshold ADC value of 0.91 (sensitivity 64%, specificity 50%, positive predictive value 67.7%, negative predictive value 45.0%). ADC cannot be used as a surrogate marker for proliferation activity and/or for tumor grade in breast cancer.