Learning Laparoscopic Radical Hysterectomy: Are We Facing an Emerging Situation?

Predictive modeling of postoperative gastrointestinal dysfunction: the role of serum bilirubin, sodium levels, and surgical duration in gynecological cancer care

OBJECTIVE

To elucidate the role of preoperative serum bilirubin and sodium levels, along with the duration of surgery, in predicting postoperative gastrointestinal dysfunction (POGD) following gynecological cancer surgery, informing tailored perioperative strategies.

METHODS

We conducted a retrospective analysis of 281 patients undergoing gynecological cancer surgery between 2018 and 2023. This analysis focused on preoperative serum bilirubin and sodium levels and intraoperative factors (surgical duration) as potential predictors of POGD. Logistic regression models were utilized for analysis, controlling for relevant confounders.

RESULTS

Elevated preoperative serum bilirubin was associated with a reduced risk of POGD (mean level in non-POGD cases: 14.172 ± 4.0701, vs. POGD cases: 9.6429 ± 3.5351; p <  0.001), suggesting a protective role. Lower preoperative sodium levels were identified in the POGD group (136.26 mEq/L [IQR: 135.2-137.63]) compared to the non-POGD group (139.32 mEq/L [IQR: 137.7-140.75]; p <  0.001), highlighting its predictive value. Additionally, longer surgical duration was associated with increased POGD incidence, with POGD cases experiencing surgeries lasting 6.1547 ± 1.9426 hours compared to 4.5959 ± 1.5475 hours in non-POGD cases (p <  0.001).

CONCLUSION

Our findings underscore the importance of serum bilirubin, sodium levels, and surgical duration as significant predictors of POGD in patients undergoing gynecological cancer surgery. These indicators should be integrated into a predictive model, aiding clinicians in identifying high-risk patients, allowing for personalized perioperative care adjustments, potentially mitigating POGD risks.

Beyond Sentinel Lymph Node: Outcomes of Indocyanine Green-Guided Pelvic Lymphadenectomy in Endometrial and Cervical Cancer

BACKGROUND

The aim of our study was to compare the number of lymph nodes removed during indocyanine green (ICG)-guided laparoscopic/robotic pelvic lymphadenectomy with standard systematic lymphadenectomy in endometrial cancer (EC) and cervical cancer (CC).

METHODS

This is a multicenter retrospective comparative study (Clinical Trial ID: NCT04246580; updated on 31 January 2023). Women affected by EC and CC who underwent laparoscopic/robotic systematic pelvic lymphadenectomy, with (cases) or without (controls) the use of ICG tracer injection within the uterine cervix, were included in the study.

RESULTS

The two groups were homogeneous for age (p = 0.08), Body Mass Index, International Federation of Gynaecology and Obstetrics (FIGO) stages (p = 0.41 for EC; p = 0.17 for CC), median estimated blood loss (p = 0.76), median operative time (p = 0.59), and perioperative complications (p = 0.66). Nevertheless, the number of lymph nodes retrieved during surgery was significantly higher (p = 0.005) in the ICG group (n = 18) compared with controls (n = 16).

CONCLUSIONS

The accurate and precise dissection achieved with the use of the ICG-guided procedure was associated with a higher number of lymph nodes removed in the case of systematic pelvic lymphadenectomy for EC and CC.

Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images

Deep learning technology has been used in the medical field to produce devices for clinical practice. Deep learning methods in cytology offer the potential to enhance cancer screening while also providing quantitative, objective, and highly reproducible testing. However, constructing high-accuracy deep learning models necessitates a significant amount of manually labeled data, which takes time. To address this issue, we used the Noisy Student Training technique to create a binary classification deep learning model for cervical cytology screening, which reduces the quantity of labeled data necessary. We used 140 whole-slide images from liquid-based cytology specimens, 50 of which were low-grade squamous intraepithelial lesions, 50 were high-grade squamous intraepithelial lesions, and 40 were negative samples. We extracted 56,996 images from the slides and then used them to train and test the model. We trained the EfficientNet using 2,600 manually labeled images to generate additional pseudo labels for the unlabeled data and then self-trained it within a student-teacher framework. Based on the presence or absence of abnormal cells, the created model was used to classify the images as normal or abnormal. The Grad-CAM approach was used to visualize the image components that contributed to the classification. The model achieved an area under the curve of 0.908, accuracy of 0.873, and F1-score of 0.833 with our test data. We also explored the optimal confidence threshold score and optimal augmentation approaches for low-magnification images. Our model efficiently classified normal and abnormal images at low magnification with high reliability, making it a promising screening tool for cervical cytology.