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Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy

  • Gynecologic Oncology
  • Published:
Archives of Gynecology and Obstetrics Aims and scope Submit manuscript

Abstract

Purpose

Concurrent cisplatin-based chemotherapy and radiotherapy (CCRT) plus brachytherapy is the standard treatment for locally advanced cervical cancer (LACC). Platinum-based neoadjuvant chemotherapy (NACT) followed by radical hysterectomy is an alternative for patients with stage IB2–IIB disease. Therefore, the correct pre-treatment staging is essential to the proper management of this disease. Pelvic magnetic resonance imaging (MRI) is the gold standard examination but studies about MRI accuracy in the detection of lymph node metastasis (LNM) in LACC patients show conflicting data. Machine learning (ML) is emerging as a promising tool for unraveling complex non-linear relationships between patient attributes that cannot be solved by traditional statistical methods. Here we investigated whether ML might improve the accuracy of MRI in the detection of LNM in LACC patients.

Methods

We analyzed retrospectively LACC patients who underwent NACT and radical hysterectomy from 2015 to 2020. Demographic, clinical and MRI characteristics before and after NACT were collected, as well as information about post-surgery histopathology. Random features elimination wrapper was used to determine an attribute core set. A ML algorithm, namely Extreme Gradient Boosting (XGBoost) was trained and validated with tenfold cross-validation. The performances of the algorithm were assessed.

Results

Our analysis included n.92 patients. FIGO stage was IB2 in n.4/92 (4.3%), IB3 in n.42/92 (45%), IIA1 in n.1/92 (1.1%), IIA2 in n.16/92 (17.4%) and IIB in n.29/92 (31.5%). Despite detected neither at pre-treatment and post-treatment MRI in any patients, LNM occurred in n.16/92 (17%) patients. The attribute core set used to train ML algorithms included grading, histotypes, age, parity, largest diameter of lesion at either pre- and post-treatment MRI, presence/absence of fornix infiltration at pre-treatment MRI and FIGO stage. XGBoost showed a good performance (accuracy 89%, precision 83%, recall 78%, AUROC 0.79).

Conclusions

We developed an accurate model to predict LNM in LACC patients in NACT, based on a ML algorithm requiring few easy-to-collect attributes.

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Data availability

Data are not freely available due to local Ethics Committee privacy issues. Authors will consider data sharing upon specific request to local Ethics Committee.

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Acknowledgements

The authors affiliated to the IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, are responsible for the views expressed in this article, which do not necessarily represent the Institute.

Funding

This research received no external funding. The authors have no relevant financial or non-financial interests to disclose.

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Authors and Affiliations

  1. Gynecologic Oncology Unit, IRCCS Istituto Tumori “Giovanni Paolo II”, Interdisciplinar Department of Medicine, Bari, Italy

    Francesca Arezzo, Erica Silvestris, Anila Kardhashi & Ambrogio Cazzolla

  2. Gynecologic Oncology Unit, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy

    Gennaro Cormio

  3. Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Bari, Italy

    Michele Mongelli & Claudio Lombardi

  4. Department of Emergency and Organ Transplantation, Pathology Section, University of Bari “Aldo Moro”, Bari, Italy

    Gerardo Cazzato

  5. Department of Emergency and Organ Transplantations, Rheumatology Unit, University of Bari “Aldo Moro”, Bari, Italy

    Vincenzo Venerito

  6. Obstetrics and Gynecology Unit, Interdisciplinar Department of Medicine, University of Bari “Aldo Moro”, Bari, Italy

    Gennaro Cormio & Vera Loizzi

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  1. Francesca Arezzo
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Contributions

FA and VL performed the study conception. GC and CL contributed to the study design. Material preparation and data collection were performed by VV, AC and MM. The first draft of the manuscript was written by FA and GC. VL, ES and CL performed the data visualization. The manuscript was reviewed by AK, GC and VL under the supervision of GC. The project was administrated by FA. All authors read and approved the final manuscript.

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Correspondence to Francesca Arezzo.

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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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The study was conducted in accordance with the Declaration of Helsinki, and the protocol was endorsed by the Scientific Board University of Bari, Bari, Italy (protocol code 6398, date of approval 10 June 2020).

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Informed consent was obtained from all subjects involved in the study at baseline consultation.

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Arezzo, F., Cormio, G., Mongelli, M. et al. Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy. Arch Gynecol Obstet 307, 1911–1919 (2023). https://doi.org/10.1007/s00404-022-06824-6

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