Lymph Node Size on Computed Tomography Images Is a Predictive Indicator for Lymph Node Metastasis in Patients with Colorectal Neuroendocrine Tumors

Rectal and perirectal CT findings in patients with monkeypox virus infection

OBJECTIVE

To analyze the findings of proctitis in patients with laboratory-confirmed Mpox and correlate the patient clinical presentation and laboratory findings.

METHODS

21 patients with PCR-positive Mpox who obtained abdominopelvic CT were retrospectively identified by electronic medical record search. Three radiologists independently evaluated CT images, measuring rectal wall thickness (cm), degree of perirectal fat stranding on a 5-point Likert scale, and size of perirectal lymph nodes (cm, short axis). Mann-Whitney U-test (Wilcoxon rank sum test) was used to assess the association of rectal wall thickness and perirectal fat standing between patients with rectal symptoms and patients without rectal symptoms.

RESULTS

20 of 21 patients presented with perirectal fat stranding, with mean Likert score of 3.0 ± 1.4, indicating moderate perirectal stranding. Mean transverse rectal wall thickness was 1.1 ± 0.5 cm (range 0.3-2.3 cm); it was thicker among patients with HIV (1.2 cm vs 0.7 cm; p = .019). Mean perirectal fat stranding was greater among patients presenting with HIV, and with rectal symptoms, though not significantly so. 17/21 (81%) patients had abnormal mesorectal lymph nodes by at least two of three readers, with mean short-axis measurement 1.0 ± 0.3 cm (range 0.5-1.6 cm). Multiple linear regression showed no significant correlation between rectal thickness and laboratory values or HIV status.

CONCLUSION

Nearly all patients with Mpox who presented with additional symptoms warranting a CT demonstrated proctitis. Degree of proctitis varied greatly within the cohort, with greatest thickening among patients with HIV. Physicians should have a high suspicion for proctitis in patients with suspected Mpox.

Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: a pilot study

BACKGROUND

Preoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metastasis using CT images. In this retrospective study, we investigated the potential value of CT texture features to diagnose LN metastasis using preoperative CT data and patient characteristics by developing quantitative prediction models.

METHODS

A total of 390 CC patients, undergone surgical resection, were enrolled in this monocentric study. 390 histologically validated LNs were collected from patients and randomly separated into training (312 patients, 155 metastatic and 157 normal LNs) and test cohorts (78 patients, 39 metastatic and 39 normal LNs). Six patient characteristics and 146 quantitative CT imaging features were analyzed and key variables were determined using either exhaustive search or least absolute shrinkage algorithm. Two kernel-based support vector machine classifiers (patient-characteristic model and radiomic-derived model), generated with 10-fold cross-validation, were compared with the clinical model that utilizes long-axis diameter for diagnosis of metastatic LN. The performance of the models was evaluated on the test cohort by computing accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC).

RESULTS

The clinical model had an overall diagnostic accuracy of 64.87%; specifically, accuracy of 65.38% and 62.82%, sensitivity of 83.87% and 84.62%, and specificity of 47.13% and 41.03% for training and test cohorts, respectively. The patient-demographic model obtained accuracy of 67.31% and 73.08%, the sensitivity of 62.58% and 69.23%, and specificity of 71.97% and 76.23% for training and test cohorts, respectively. Besides, the radiomic-derived model resulted in an accuracy of 81.09% and 79.49%, sensitivity of 83.87% and 74.36%, and specificity of 78.34% and 84.62% for training and test cohorts, respectively. Furthermore, the diagnostic performance of the radiomic-derived model was significantly higher than clinical and patient-demographic models (p < 0.02) according to the DeLong method.

CONCLUSIONS

The texture of the LNs provided characteristic information about the histological status of the LNs. The radiomic-derived model leveraging LN texture provides better preoperative diagnostic accuracy for the detection of metastatic LNs compared to the clinically accepted diagnostic criteria and patient-demographic model.

Pathological nodal staging score for rectal cancer patients treated with radical surgery with or without neoadjuvant therapy: a postoperative decision tool

Background

Lymph node status can predict the prognosis of patients with rectal cancer treated with surgery. Thus, we sought to establish a standard for the minimum number of lymph nodes (LNs) examined in patients with rectal cancer by evaluating the probability that pathologically negative LNs prove positive during surgery.

Patients and methods

We extracted information of 31,853 patients with stage I–III rectal carcinoma registered between 2004 and 2013 from the Surveillance, Epidemiology, and End Results database and divided them into two groups: the first group was SURG, including patients receiving surgery directly and the other group was NEO, encompassing those underwent neo-adjuvant therapy. Using a beta-binomial model, we developed nodal staging score (NSS) based on pT/ypT stage and the number of LNs retrieved.

Results

In both cohorts, the false-negative rate was estimated to be 16% when 12 LNs were examined, but it dropped to 10% when 20 LNs were evaluated. In the SURG cohort, to rule out 90% possibility of false staging, 3, 7, 28, and 32 LNs would be necessarily examined in patients with pT1–4 disease, respectively. While in the NEO cohort, 4, 7, 12, and 16 LNs would be included for examination in patients with ypT1–4 disease to guarantee an NSS of 90%.

Conclusion

By determining whether a rectal cancer patient with negative LNs was appropriately staged, the NSS model we developed in this study may assist in tailoring postoperative management.