Diagnostic Accuracy of Magnetic Resonance Imaging in Carcinoma of Cervix Taking Histopathology as Gold Standard
Bibliographic record
Abstract
The crucial factor that plays important role in diagnosis and prompt treatment management of cervical cancer is staging. To stage cervical carcinoma Magnetic Resonance Imaging (MRI) is considered to be the most accurate and gold standard diagnostic tool. Objective: The aim of the present study was to elaborate the diagnostic accuracy of MRI in correlation with Histopathological Examination (HPE). Methods: The 53 patients diagnosed with cervical carcinoma attending the gynecology department of hospital from May 2021 to April 2022 were included in the study. Those patients who had undergone the abdomen and pelvis MRI fulfilled the inclusion criteria. The MRI and histopathological examination not only help in staging of cancer but also consider valuable in tracking tumor location, size and extension. The retroperitoneal lymphadenopathy, and involvement of the tumor to the adjacent areas was also evaluated by the study. Staging of all patient was done according to the International Federation of Gynecology and Obstetrics FIGO standards. Findings of MRI and HPE were assessed. For statistical evaluation of data, the SPSS version 22.0 was used. For quantitative variables the values were represented as mean with standard deviations. Results: The 54.46±9.29 years was the calculated mean age. Squamous cell carcinoma was diagnosed in almost 46 patients (87.5% cases). Stage IB carcinoma was diagnosed in almost 47.91% cases. Pelvic lymphadenopathy was observed in 8.34% cases, while metastasis of pelvic nodal lymph was observed in 4.16% cases on the HPE. Conclusion: Malignant diseases require early and accurate tool for their diagnosis. For identification of cancer stage and better planning of treatment of cervical carcinoma the highly non-invasive modality MRI can be used. With the advents in MR imaging, it is considered as gold standard diagnostic tool with better sensitivity, specificity and high accuracy for cervical carcinoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".