Promotional language in radiology publications: increasing use of "excellent", "favorable", "promising", "robust", and "unique".

PURPOSE: To investigate if radiology researchers are increasingly promoting their scientific findings by more frequently using positive words in their publications. MATERIALS AND METHODS: This study included all articles that were published in 14 general radiology journals between 2003 and 2022. The title and abstract of each article were assessed for the presence of positive, negative, neutral, and random words, according to predefined sets of words for each category. Usage of positive, negative, neutral, and random words was calculated for each year and corrected for the total number of articles in each year. Temporal trends between 2002 and 2023 and the relationship between positive word usage and journal impact factor (IF) were assessed. RESULTS: Positive word usage (Mann-Kendall tau of 0.895, P< 0.001) and neutral word usage (Mann-Kendall tau of 0.463, P = 0.005) showed significant upward temporal trends. Negative word usage and random word usage did not show any significant temporal trends. Five positive words showed significantly increased usage over time and were present in more than 1 % of titles/abstracts in at least one year: "excellent" (Mann-Kendall tau of 0.800, P< 0.001), "favorable" (Mann-Kendall tau of 0.547, P< 0.001), "promising" (Mann-Kendall tau of 0.607, P< 0.001), "robust" (Mann-Kendall tau of 0.737, P< 0.001), and "unique" (Mann-Kendall tau of 0.747, P< 0.001). There was no significant association between positive word usage and journal IF. CONCLUSION: Radiology researchers appear to increasingly promote their scientific findings by more frequently using positive words in their publications over the past two decades. KEY POINTS: · Positive word usage in titles/abstracts has strongly increased between 2003-2022. · "Excellent", "favorable", "promising", "robust", and "unique" were most often used. · This trend occurred in all general radiology journals, regardless of impact factor.

A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist.

OBJECTIVES: To present a framework to develop and implement a fast-track artificial intelligence (AI) curriculum into an existing radiology residency program, with the potential to prepare a new generation of AI conscious radiologists. METHODS: The AI-curriculum framework comprises five sequential steps: (1) forming a team of AI experts, (2) assessing the residents' knowledge level and needs, (3) defining learning objectives, (4) matching these objectives with effective teaching strategies, and finally (5) implementing and evaluating the pilot. Following these steps, a multidisciplinary team of AI engineers, radiologists, and radiology residents designed a 3-day program, including didactic lectures, hands-on laboratory sessions, and group discussions with experts to enhance AI understanding. Pre- and post-curriculum surveys were conducted to assess participants' expectations and progress and were analyzed using a Wilcoxon rank-sum test. RESULTS: There was 100% response rate to the pre- and post-curriculum survey (17 and 12 respondents, respectively). Participants' confidence in their knowledge and understanding of AI in radiology significantly increased after completing the program (pre-curriculum means 3.25 ± 1.48 (SD), post-curriculum means 6.5 ± 0.90 (SD), p-value = 0.002). A total of 75% confirmed that the course addressed topics that were applicable to their work in radiology. Lectures on the fundamentals of AI and group discussions with experts were deemed most useful. CONCLUSION: Designing an AI curriculum for radiology residents and implementing it into a radiology residency program is feasible using the framework presented. The 3-day AI curriculum effectively increased participants' perception of knowledge and skills about AI in radiology and can serve as a starting point for further customization. CRITICAL RELEVANCE STATEMENT: The framework provides guidance for developing and implementing an AI curriculum in radiology residency programs, educating residents on the application of AI in radiology and ultimately contributing to future high-quality, safe, and effective patient care. KEY POINTS: • AI education is necessary to prepare a new generation of AI-conscious radiologists. • The AI curriculum increased participants' perception of AI knowledge and skills in radiology. • This five-step framework can assist integrating AI education into radiology residency programs.

Repeatability analysis of ADC histogram metrics of the uterus.

BACKGROUND: Recently, histogram analysis based on voxel-wise apparent diffusion coefficient (ADC) value distribution has been increasingly performed. However, few studies have been reported regarding its repeatability. PURPOSE: To evaluate the repeatability of ADC histogram metrics of the uterus in clinical magnetic resonance imaging (MRI). MATERIAL AND METHODS: Thirty-three female patients who underwent pelvic MRI including diffusion-weighted imaging (DWI) were prospectively included after providing informed consent. Two sequential DWI acquisitions with identical parameters and position were obtained. Regions of interest (ROIs) for histologically confirmed uterine lesions (five cervical and three endometrial cancers, and one endometrial hyperplasia) and normal appearing tissues (21 endometrium and 33 myometrium) were assigned on the first DWI dataset and then pasted onto the second DWI dataset. ADC histogram metrics within the ROIs were calculated and repeatability was evaluated by calculating within-subject coefficient of variance (%) (wCV (%)) and Bland-Altman plot (%). RESULTS: ADC 10%, 25%, median, 75%, 90%, maximum, mean, and entropy showed high repeatability (wCV (%) < 7, 95% limit of agreement in Bland-Altman plot (%) < ±20), followed by ADC minimum (wCV (%) = 8.12, 95% limit of agreement in Bland-Altman plot (%) < ±30). However, ADC skewness and kurtosis showed very low repeatability in all evaluations. CONCLUSION: ADC histogram metrics like ADC 10%, 25%, median, 75%, 90%, maximum, mean, and entropy are robust biomarkers and could be applicable to clinical use. However, ADC skewness and kurtosis lack robustness. Radiologists should keep these characteristics and limitations in mind when interpreting quantitative DWI.

Improved Visualization of Middle Ear Cholesteatoma with Computed Diffusion-weighted Imaging.

Computed DWI (cDWI) is a mathematical technique that calculates arbitrary higher b value images from at least two different lower b values. In addition, the removal of high intensity noise with image processing on cDWI could improve cholesteatoma-background contrast-to-noise ratio (CNR). In the present study, noise reduction was performed by the cut-off values of apparent diffusion coefficient (ADC) less than 0 and 0.4 × 10-3 s/mm2. The cholesteatoma to non-cholesteatoma CNR was increased using a noise reduction algorithm for clinical setting.

Utility of computed diffusion-weighted MRI for predicting aggressiveness of prostate cancer.

PURPOSE: To investigate the value of computed (c) diffusion-weighted imaging (DWI) in assessing prostate cancer aggressiveness. MATERIALS AND METHODS: Fifty-five patients with peripheral zone prostate cancer who underwent prebiopsy 1.5T magnetic resonance imaging (including native DWI at b-values of 0 and 1000 s/mm2 ) were included. cDWI signal intensities of peripheral zone prostate cancer and nonmalignant prostate tissue were measured. Association between changes in monoexponentially calculated cDWI signals according to different b-values and primary Gleason grades were assessed. RESULTS: The cDWI signal intensity of prostate cancer was lower at b = 0 s/mm2 and higher at b = 1000 s/mm2 compared to nonmalignant prostate tissue. The b-value at which the signal intensities of prostate cancer and nonmalignant prostate tissue were equal was defined as the "iso-b-value." On multivariate analysis, only the iso-b-value was a significant predictor of primary Gleason grade 4/5 cancer (P = 0.001). The area under the curve (AUC) of the iso-b-value for diagnosing primary Gleason grade 4/5 cancer was 0.94, and significantly higher than that of the tumor apparent diffusion coefficient (ADC) value with an AUC of 0.68 (P < 0.001). CONCLUSION: cDWI with iso-b-value-based semiquantitative analysis was found to be useful for predicting the aggressiveness of prostate cancer and may potentially outperform tumor ADC measurements in this setting. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:490-496.

Fabella Fractures after Total Knee Arthroplasty with Correction of Valgus Malalignment.

The incidence of fabella fractures is considered to be extremely low. This report presents two patients with femorotibial osteoarthritis and considerable preoperative valgus malalignment, who developed a fracture of the fabella (as demonstrated by radiography) after total knee arthroplasty with intraoperative correction of the valgus malalignment. Special attention should be paid to the fabella for not missing a fabella fracture in these patients.

Role of imaging in predicting response to neoadjuvant chemotherapy in gastric cancer.

With the proven overall benefit of neoadjuvant chemotherapy in patients with locally advanced gastric cancer, there has come a need to discriminate responders from non-responders. In this article, the current role of anatomical and molecular imaging in the prediction of response to neoadjuvant therapy in gastric cancer is outlined and future prospects are discussed.

Imaging in assessing lymph node status in gastric cancer.

BACKGROUND: Accurate assessment of lymph node status is of crucial importance for appropriate treatment planning and determining prognosis in patients with gastric cancer. The aim of this study was to systematically review the current role of imaging in assessing lymph node (LN) status in gastric cancer. METHODS: A systematic literature search was performed in the PubMed/MEDLINE and Embase databases. The methodological quality and diagnostic performance of the included studies was assessed. RESULTS: Six abdominal ultrasonography (AUS) studies, 30 endoscopic ultrasonography (EUS) studies, 10 multidetectorrow computed tomography (MDCT) studies, 3 conventional magnetic resonance imaging (MRI) studies, 4 (18)F-fluoro-2-deoxyglucose positron emission tomography (FDG-PET) studies, and 1 FDG-PET/CT fusion study were included. In general, the included studies had moderate methodological quality. The sensitivity and specificity of AUS varied between 12.2% and 80.0% (median, 39.9%) and 56.3% and 100% (median, 81.8%). The sensitivity and specificity of EUS varied between 16.7% and 95.3% (median, 70.8%) and 48.4% and 100% (median, 84.6%). The sensitivity and specificity of MDCT varied between 62.5% and 91.9% (median, 80.0%) and 50.0% and 87.9% (median, 77.8%). The sensitivity and specificity of MRI varied between 54.6% and 85.3% (median, 68.8%) and 50.0% and 100% (median, 75.0%). The sensitivity and specificity of FDG-PET varied between 33.3% and 64.6% (median, 34.3%) and 85.7% and 97.0% (median, 93.2%). The sensitivity and specificity of the FDG-PET/CT fusion study were 54.7% and 92.2%. For all the imaging modalities, there were no significant differences between the mean sensitivities and specificities of high- and low-quality studies. CONCLUSION: AUS, EUS, MDCT, conventional MRI, and FDG-PET cannot reliably be used to confirm or exclude the presence of LN metastasis. The performance of highresolution PET/CT fusion and functional MRI techniques still has to be determined.

Predicting lymph node status in early gastric cancer.

Accurate prediction of lymph node (LN) status is of crucial importance for appropriate treatment planning in patients with early gastric cancer (EGC). However, there is no definitive consensus yet on which patient and/or tumor characteristics are associated with LN metastasis. A systematic search for studies investigating the relationship between patient and/or tumor characteristics and LN metastasis in EGC was performed in PubMed/MEDLINE. Patient and/or tumor characteristics associated with LN metastasis were identified by meta-analyzing results of individual studies. Forty-five studies were included. Variables significantly associated with LN metastasis in gastric cancer limited to the mucosa were: age younger than 57 years, tumor location in the middle part of the stomach, larger tumor size, macroscopically depressed tumor type, tumor ulcerations, undifferentiated tumors, diffuse tumor type according to the Lauren classification, lymphatic tumor invasion, tumors with a proliferating cell nuclear antigen (PCNA) labeling index of more than 25%, and matrix metalloproteinase-9-positive tumors. Variables significantly associated with LN metastasis in gastric cancer limited to the submucosa were: female sex, tumor location in the lower part of the stomach, larger tumor size, undifferentiated tumors, increasing depth of submucosal invasion, lymphatic tumor invasion, vascular tumor invasion, increased submucosal vascularity, tumors with a PCNA labeling index of more than 25%, tumors with a gastric mucin phenotype, and vascular endothelial growth factor-C-positive tumors. We identified several variables associated with LN metastasis in EGC. These variables should be included in future research, in order to assess which of these variables remain as significant predictors of LN metastasis.

Imaging in local staging of gastric cancer: a systematic review.

PURPOSE: Endoscopic ultrasound (EUS) has been established as the diagnostic modality of choice in local (T) staging of gastric cancer. Multidetector row computed tomography (MDCT) and magnetic resonance imaging (MRI) are promising alternatives. The aim of this study was to systematically review the literature regarding the performance of each of these imaging modalities. METHODS: A systematic search for relevant studies was performed in the PubMed/MEDLINE and EMBASE databases. Two reviewers independently assessed the methodological quality of each study. Local staging performance of included studies was calculated. RESULTS: Twenty-two EUS studies, five MDCT studies, one combined EUS and MDCT study, and three MRI studies met the inclusion criteria. The studies were of moderate methodological quality. Diagnostic accuracy of overall T staging for EUS, MDCT, and MRI varied between 65% to 92.1%, 77.1% to 88.9%, and 71.4% to 82.6%, respectively. Sensitivity for assessing serosal involvement for EUS, MDCT, and MRI varied between 77.8% to 100%, 82.8% to 100%, and 89.5% to 93.1%, respectively. Specificity for assessing serosal involvement for EUS, MDCT, and MRI varied between 67.9% to 100%, 80% to 96.8%, and 91.4% to 100%, respectively. CONCLUSION: EUS, MDCT, and MRI achieve similar results in terms of diagnostic accuracy in T staging and in assessing serosal involvement. Most experience has been gained with EUS. Few MDCT studies and even fewer MRI studies are available. Thus, EUS remains the first-choice imaging modality in preoperative T staging of gastric cancer.