Early Cervical Carcinoma and Fertility-sparing Treatment Options: MR Imaging as a Tool in Patient Selection and a Follow-up Modality
Bibliographic record
Abstract
Because of the widespread use of cytologic screening programs in industrialized nations, cervical carcinoma is being diagnosed in younger patients and at an earlier stage. The traditional therapy for early-stage disease is radical hysterectomy with pelvic lymphadenectomy, which leads to infertility. In the past 20 years, fertility-sparing therapies, such as cervical conization and radical trachelectomy, have emerged and show good oncologic and obstetric outcomes. The selection criteria for vaginal radical trachelectomy include stages IA2 and IB1, a tumor that is smaller than 2 cm, distance from the internal os of at least 1 cm, limited stromal invasion, and no nodal or extracervical extension. Magnetic resonance (MR) imaging accurately depicts these criteria and is a necessary tool in the preoperative evaluation of patients with cervical carcinoma who are eligible for fertility-sparing surgery. The MR imaging report must provide the following pieces of information for adequate surgical planning: tridimensional diameters of the lesion, uterine and cervical lengths, the degree of stromal invasion, distance from the internal os, and the presence of extracervical or nodal involvement. Because patients also undergo follow-up MR imaging, radiologists must be familiar with the postoperative imaging appearance of the cervix. After trachelectomy, the uterovaginal anastomosis may appear end-to-end or with a neoposterior vaginal fornix. Vaginal wall thickening, hematomas, lymphoceles, and hematometra secondary to isthmic stenosis may be seen. The normal postoperative appearance must be differentiated from recurrent disease, which is seen as a mass with intermediate to high signal intensity in the vaginal vault or parametrium on T2-weighted images. Functional imaging, including diffusion-weighted and dynamic contrast-enhanced imaging, may help characterize recurrence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".