Intra-Appendiceal Air at CT: Is It a Useful or a Confusing Sign for the Diagnosis of Acute Appendicitis?

Diagnostic Algorithm Based on Machine Learning to Predict Complicated Appendicitis in Children Using CT, Laboratory, and Clinical Features

To establish a diagnostic algorithm for predicting complicated appendicitis in children based on CT and clinical features.

METHODS

This retrospective study included 315 children (<18 years old) who were diagnosed with acute appendicitis and underwent appendectomy between January 2014 and December 2018. A decision tree algorithm was used to identify important features associated with the condition and to develop a diagnostic algorithm for predicting complicated appendicitis, including CT and clinical findings in the development cohort (n = 198). Complicated appendicitis was defined as gangrenous or perforated appendicitis. The diagnostic algorithm was validated using a temporal cohort (n = 117). The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) from the receiver operating characteristic curve analysis were calculated to evaluate the diagnostic performance of the algorithm.

RESULTS

All patients with periappendiceal abscesses, periappendiceal inflammatory masses, and free air on CT were diagnosed with complicated appendicitis. In addition, intraluminal air, transverse diameter of the appendix, and ascites were identified as important CT findings for predicting complicated appendicitis. C-reactive protein (CRP) level, white blood cell (WBC) count, erythrocyte sedimentation rate (ESR), and body temperature also showed important associations with complicated appendicitis. The AUC, sensitivity, and specificity of the diagnostic algorithm comprising features were 0.91 (95% CI, 0.86-0.95), 91.8% (84.5-96.4), and 90.0% (82.4-95.1) in the development cohort, and 0.7 (0.63-0.84), 85.9% (75.0-93.4), and 58.5% (44.1-71.9) in test cohort, respectively.

CONCLUSION

We propose a diagnostic algorithm based on a decision tree model using CT and clinical findings. This algorithm can be used to differentiate between complicated and noncomplicated appendicitis and to provide an appropriate treatment plan for children with acute appendicitis.

Appendiceal wall thickness and Alvarado score are predictive of acute appendicitis in the patients with equivocal computed tomography findings

Around 8-13% of the patients who underwent CT scan for diagnosis of appendicitis had equivocal CT results. About one-third of these patients had acute appendicitis and this caused diagnostic challenge to the clinicians. This study was conducted to identify clinical and imaging features that were predictive of acute appendicitis in patients who had equivocal CT findings. During January 2015 to June 2021, we retrospectively included 103 consecutive CT scans of adult patients (22 men and 81 women; mean age, 39.1 ± 17.5 years) who had equivocal CT findings of acute appendicitis. Two readers, blinded to the clinical data, independently assessed CT images for the relevant CT findings of appendicitis. Any disagreement between the readers was solved by consensus. The clinical parameters and CT findings were analyzed and compared between the patients who had appendicitis and patients who did not have appendicitis. Thirty-one (30.1%) patients had appendicitis, all of which were non-complicated. The appendiceal wall thickness of ≥ 2 mm and the Alvarado score of ≥ 7 were independent predictors of appendicitis with adjusted odds ratios (ORs) of 2.76 (95% CI, 1.09-7.02) and 1.47 (95% CI, 1.12-1.94), respectively. The maximal appendiceal diameter was higher in the appendicitis group (7.2 ± 1.2 mm vs. 6.5 ± 1.0 mm), but not predictive of appendicitis. The rest of the clinical parameters and CT findings, including mucosal hyperenhancement, periappendiceal fat reticulation, thickening of peritoneal reflection, appendicolith, focal cecal thickening, and content in appendiceal lumen showed no significant difference between two groups. The appendiceal wall thickness and the Alvarado score were able to predict appendicitis in patients who had equivocal CT findings.

Selection and Reporting of Statistical Methods to Assess Reliability of a Diagnostic Test: Conformity to Recommended Methods in a Peer-Reviewed Journal

Objective

To evaluate the frequency and adequacy of statistical analyses in a general radiology journal when reporting a reliability analysis for a diagnostic test.

Materials and Methods

Sixty-three studies of diagnostic test accuracy (DTA) and 36 studies reporting reliability analyses published in the Korean Journal of Radiology between 2012 and 2016 were analyzed. Studies were judged using the methodological guidelines of the Radiological Society of North America-Quantitative Imaging Biomarkers Alliance (RSNA-QIBA), and COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) initiative. DTA studies were evaluated by nine editorial board members of the journal. Reliability studies were evaluated by study reviewers experienced with reliability analysis.

Results

Thirty-one (49.2%) of the 63 DTA studies did not include a reliability analysis when deemed necessary. Among the 36 reliability studies, proper statistical methods were used in all (5/5) studies dealing with dichotomous/nominal data, 46.7% (7/15) of studies dealing with ordinal data, and 95.2% (20/21) of studies dealing with continuous data. Statistical methods were described in sufficient detail regarding weighted kappa in 28.6% (2/7) of studies and regarding the model and assumptions of intraclass correlation coefficient in 35.3% (6/17) and 29.4% (5/17) of studies, respectively. Reliability parameters were used as if they were agreement parameters in 23.1% (3/13) of studies. Reproducibility and repeatability were used incorrectly in 20% (3/15) of studies.

Conclusion

Greater attention to the importance of reporting reliability, thorough description of the related statistical methods, efforts not to neglect agreement parameters, and better use of relevant terminology is necessary.

How to differentiate acute pelvic inflammatory disease from acute appendicitis ? A decision tree based on CT findings

PURPOSE

To construct a decision tree based on CT findings to differentiate acute pelvic inflammatory disease (PID) from acute appendicitis (AA) in women with lower abdominal pain and inflammatory syndrome.

MATERIALS AND METHODS

This retrospective study was approved by our institutional review board and informed consent was waived. Contrast-enhanced CT studies of 109 women with acute PID and 218 age-matched women with AA were retrospectively and independently reviewed by two radiologists to identify CT findings predictive of PID or AA. Surgical and laboratory data were used for the PID and AA reference standard. Appropriate tests were performed to compare PID and AA and a CT decision tree using the classification and regression tree (CART) algorithm was generated.

RESULTS

The median patient age was 28 years (interquartile range, 22-39 years). According to the decision tree, an appendiceal diameter ≥ 7 mm was the most discriminating criterion for differentiating acute PID and AA, followed by a left tubal diameter ≥ 10 mm, with a global accuracy of 98.2 % (95 % CI: 96-99.4).

CONCLUSION

Appendiceal diameter and left tubal thickening are the most discriminating CT criteria for differentiating acute PID from AA.

KEY POINTS

• Appendiceal diameter and marked left tubal thickening allow differentiating PID from AA. • PID should be considered if appendiceal diameter is < 7 mm. • Marked left tubal diameter indicates PID rather than AA when enlarged appendix. • No pathological CT findings were identified in 5 % of PID patients.