Systematic review and meta-analysis of prediction models used in cervical cancer

Prognostic prediction of oxidative stress related hematological biomarkers in locally advanced cervical cancer patients undergoing chemoradiotherapy

OBJECTIVE

This investigation aimed to develop and validate a novel oxidative stress score for prognostic prediction in locally advanced cervical cancer (LACC) patients receiving chemoradiotherapy.

METHODS

A total of 301 LACC patients were enrolled and randomly divided into a training and a validation set. The association between oxidative stress parameters and prognosis was analyzed for oxidative stress score (OSS) establishment. A Cox regression model was conducted for overall survival (OS) and progression-free survival (PFS). A nomogram prediction model was developed using independent prognostic factors from the training set and validated in the validation set.

RESULTS

A novel OSS was established with four oxidative stress parameters, including albumin, total bilirubin, blood urea nitrogen, and lactate dehydrogenase. Multivariate regression analysis identified OSS as an independent prognostic factor for OS (p = 0.001) and PFS (p < 0.001). A predictive nomogram based on the OSS was established and validated. The C-indexes of the nomogram in the training set were 0.772 for OS and 0.781 for PFS, while in the validation set the C-indexes were 0.642 for OS and 0.621 for PFS.

CONCLUSION

This study confirmed that preoperative OSS could serve as a useful independent prognostic factor in LACC patients who received CCRT.

A machine learning-based prediction model of pelvic lymph node metastasis in women with early-stage cervical cancer

OBJECTIVE

To develop a novel machine learning-based preoperative prediction model for pelvic lymph node metastasis (PLNM) in early-stage cervical cancer by combining the clinical findings and preoperative computerized tomography (CT) of the whole abdomen and pelvis.

METHODS

Patients diagnosed with International Federation of Gynecology and Obstetrics stage IA2-IIA1 squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma of the cervix who had primary radical surgery with bilateral pelvic lymphadenectomy from January 1, 2003 to December 31, 2020, were included. Seven supervised machine learning algorithms, including logistic regression, random forest, support vector machine, adaptive boosting, gradient boosting, extreme gradient boosting, and category boosting, were used to evaluate the risk of PLNM.

RESULTS

PLNM was found in 199 (23.9%) of 832 patients included. Younger age, larger tumor size, higher stage, no prior conization, tumor appearance, adenosquamous histology, and vaginal metastasis as well as the CT findings of larger tumor size, parametrial metastasis, pelvic lymph node enlargement, and vaginal metastasis, were significantly associated with PLNM. The models' predictive performance, including accuracy (89.1%-90.6%), area under the receiver operating characteristics curve (86.9%-91.0%), sensitivity (77.4%-82.4%), specificity (92.1%-94.3%), positive predictive value (77.0%-81.7%), and negative predictive value (93.0%-94.4%), appeared satisfactory and comparable among all the algorithms. After optimizing the model's decision threshold to enhance the sensitivity to at least 95%, the 'highly sensitive' model was obtained with a 2.5%-4.4% false-negative rate of PLNM prediction.

CONCLUSION

We developed prediction models for PLNM in early-stage cervical cancer with promising prediction performance in our setting. Further external validation in other populations is needed with potential clinical applications.

Why Is Surgery Still Done after Concurrent Chemoradiotherapy in Locally Advanced Cervical Cancer in Romania?

The incidence and mortality of cervical cancer are high in Romania compared to other European countries, particularly for locally advanced cervical cancer cases, which are predominant at the time of diagnosis. Widely accepted therapeutic guidelines indicate that the treatment for locally advanced cervical cancer consists of concurrent chemoradiotherapy (total dose 85-90 Gy at point A), with surgery not being necessary as it does not lead to improved survival and results in significant additional morbidity. In Romania, the treatment for locally advanced cervical cancer differs, involving lower-dose chemoradiotherapy (total dose 60-65 Gy at point A), followed by surgery, which, under these circumstances, ensures better local control. In this regard, we attempted to evaluate the role and necessity of surgery in Romania, considering that in our study, residual lesions were found in 55.84% of cases on resected specimens, especially in cases with unfavorable histology (adenocarcinoma and adenosquamous carcinoma). This type of surgery was associated with significant morbidity (28.22%) in our study. The recurrence rate was 24.21% for operated-on patients compared to 62% for non-operated-on patients receiving suboptimal concurrent chemotherapy alone. In conclusion, in Romania, surgery will continue to play a predominant role until radiotherapy achieves the desired effectiveness for local control.

Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology

Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.