MRI of cervical cancer with a surgical perspective: staging, prognostic implications and pitfalls

Balcacer, Patricia; Shergill, Arvind K; Litkouhi, Babak

doi:10.1007/s00261-019-01984-7

Cited by 71 publications

(58 citation statements)

References 47 publications

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“…Thanks to its excellent soft tissue contrast, MRI is an excellent imaging method for the local staging of cervical cancer as well during follow-up for the evaluation of treatment success [12]. Particularly compared to CT, MRI is superior regarding the evaluation of stromal invasion and the infiltration of adjacent structures and neighboring organs.…”

Section: Mrimethods Of Choicementioning

confidence: 99%

“…In contrast, the outer fibromuscular stroma has a slightly higher T2 signal and an inhomogeneous, less compact structure (▶ Fig. 1) [12]. Intermediate signal intensity between the inner and the outer fibromuscular stroma is a characteristic finding in cervical cancer (▶ Fig.…”

Section: Examination Techniquementioning

confidence: 99%

“…In order to promote a globally uniform approach, the staging of cervical cancer was consequently based only on methods with widespread availability [11]. This includes examinations under anesthesia, histological confirmation via conization or cervical biopsy, systematic pelvic and if applicable also retroperitoneal lymphadenectomy to rule out lymph node metastases, cystoscopy and proctoscopy, IV pyelogram and chest X-ray [12]. The use of additional imaging like computed tomography (CT), magnetic resonance imaging (MRI) or positron emission tomography (PET) for staging local findings was considered desirable but not mandatory and the acquired findings were not taken into consideration when determining the tumor stage.…”

Section: Cervical Cancermentioning

confidence: 99%

“…2). T2-weighted sequences are therefore best suited for differentiating tumor tissue from healthy cervical stroma and are essential for the evaluation of tumor extent [12,21]. The extent of the tumor in the corpus uteri and in the vagina can be precisely evaluated on the sagittal plane, while the axial plane is particularly suitable for evaluating stromal invasion and infiltration of the parametria [19].…”

Section: Examination Techniquementioning

confidence: 99%

“…If this ring is intact and continuous on MRI, the examination has a high negative predictive value regarding parametrial infiltration (▶ Fig. 1) [12,22].…”

Section: Examination Techniquementioning

confidence: 99%

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Revised FIGO Staging for Cervical Cancer – A New Role for MRI

Merz

Bossart

Bamberg

et al. 2020

Rofo

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Cervical cancer is still the fourth most common malignancy in women worldwide and has a high mortality rate. The prognosis as well as the therapy depends largely on the extent of the tumor at the time of initial diagnosis. This shows the importance of correct staging of cervical cancer. In order to promote a globally uniform approach, staging of cervical cancer in the past was based on widespread examinations such as exam under anesthesia, histology from cervical conization or biopsy, systematic lymphadenectomy, cystoscopy, proctoscopy, i. v.-pyelogram and chest X-ray. However, as the primary tumor stage was often underestimated, the 2018 revised FIGO classification now permits cross-sectional imaging techniques and pathological findings to be incorporated into disease staging or an already existing stage to be adapted based on radiological findings. Thanks to its excellent soft tissue contrast, magnetic resonance imaging (MRI) is the method of choice for local-regional staging of cervical cancer, evaluating the response to treatment, detecting tumor recurrence and for follow-up examinations. It is important that radiologists interpreting pelvic MRI in case of suspected cervical cancer are familiar with the current FIGO staging system. This is the only way to determine the tumor stage as precisely as possible and thus lay the foundation for the success of therapy for patients. The aim of this review is to present the changes of the revised FIGO classification as well as to show the importance of MRI as the method of choice for local-regional tumor staging as a complement to clinical examination. Key Points: Citation Format

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Section: Mrimethods Of Choicementioning

confidence: 99%

Section: Examination Techniquementioning

confidence: 99%

Section: Cervical Cancermentioning

confidence: 99%

Section: Examination Techniquementioning

confidence: 99%

“…If this ring is intact and continuous on MRI, the examination has a high negative predictive value regarding parametrial infiltration (▶ Fig. 1) [12,22].…”

Section: Examination Techniquementioning

confidence: 99%

See 3 more Smart Citations

Revised FIGO Staging for Cervical Cancer – A New Role for MRI

Merz

Bossart

Bamberg

et al. 2020

Rofo

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Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer

Wang

Zhang

et al. 2020

JAMA Netw Open

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IMPORTANCE Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary surgical intervention and benefit treatment planning. OBJECTIVE To develop a deep learning model using preoperative magnetic resonance imaging for prediction of lymph node metastasis in cervical cancer. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study developed an end-to-end deep learning model to identify lymph node metastasis in cervical cancer using magnetic resonance imaging (MRI). A total of 894 patients with stage IB to IIB cervical cancer who underwent radical hysterectomy and pelvic lymphadenectomy were reviewed. All patients underwent radical hysterectomy and pelvic lymphadenectomy, received pelvic MRI within 2 weeks before the operations, had no concurrent cancers, and received no preoperative treatment. To achieve the optimal model, the diagnostic value of 3 MRI sequences was compared, and the outcomes in the intratumoral and peritumoral regions were explored. To mine tumor information from both image and clinicopathologic levels, a hybrid model was built and its prognostic value was assessed by Kaplan-Meier analysis. The deep learning model and hybrid model were developed on a primary cohort consisting of 338 patients (218 patients from Sun Yat

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Construction of refined staging classification systems integrating FIGO/T‐categories and corpus uterine invasion for non‐metastatic cervical cancer

et al. 2023

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BackgroundTo investigate the prognostic value of corpus uterine invasion (CUI) in cervical cancer (CC), and determine the necessity to incorporate it for staging.MethodsA total of 809 cases of biopsy‐proven, non‐metastatic CC were identified from an academic cancer center. Recursive partitioning analysis (RPA) method was used to develop the refined staging systems with respect to overall survival (OS). Internal validation was performed by using calibration curve with 1000 bootstrap resampling. Performances of the RPA‐refined stages were compared against the conventional FIGO 2018 and 9th edition TNM‐stage classifications by the receiver operating characteristic curve (ROC) and decision curve analysis (DCA).ResultsWe identified that CUI was independently prognostic for death and relapse in our cohort. RPA modeling using a two‐tiered stratification by CUI (positive and negative) and FIGO/T‐categories divided CC into three risk groupings (FIGO I′‐III'/T1′‐3′), with 5‐year OS of 90.8%, 82.1%, and 68.5% for proposed FIGO stage I′–III', respectively (p ≤ 0.003 for all pairwise comparisons), and 89.7%, 78.8%, and 68.0% for proposed T1′‐3′, respectively (p < 0.001 for all pairwise comparisons). The RPA‐refined staging systems were well validated with RPA‐predicted OS rates showed optimal agreement with actual observed survivals. Additionally, the RPA‐refined stages outperformed the conventional FIGO/TNM‐stage with significantly higher accuracy of survival prediction (AUC: RPA‐FIGO vs. FIGO, 0.663 [95% CI 0.629–0.695] vs. 0.638 [0.604–0.671], p = 0.047; RPA‐T vs. T, 0.661 [0.627–0.694] vs. 0.627 [0.592–0.660], p = 0.036).ConclusionCUI affects the survival outcomes in patients with CC. Disease extended to corpus uterine should be classified as stage III/T3.

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MRI of cervical cancer with a surgical perspective: staging, prognostic implications and pitfalls

Cited by 71 publications

References 47 publications

Revised FIGO Staging for Cervical Cancer – A New Role for MRI

Revised FIGO Staging for Cervical Cancer – A New Role for MRI

Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer

Construction of refined staging classification systems integrating FIGO/T‐categories and corpus uterine invasion for non‐metastatic cervical cancer

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