Intensity Modulated Radiotherapy of Two Simultaneous Neoplasms - Cervical Carcinoma and Breast Carcinoma: A Case Report with a Review of the Literature
Bibliographic record
Abstract
Concomitant expression of two neoplasms- cervical carcinoma (CC) and breast carcinoma (BC) is a relatively rare pathology. The manifestation of synchronous primary neoplasms is a challenge for the treating team as it puts a number of questions about the healing strategy.
 We present a 57-year-old patient after total laparohisterectomy with lymphatic pelvic dissection on the local advanced CC/IIIB clinical stage. Intensity-modulated radiotherapy (IMRT) in the small pelvis and the upper 2/3 of the vaginal cuff with daily dose (DD) 1.8 Gy up to total dose (TD) 50.4 Gy combined with Cisplatin (50 mg/m2) once a week, was conducted. After 4 months from the diagnosis and complex treatment of CC, PET/CT establishes a second neoplasm-invasive ductal carcinoma in the left mammary gland. After the breast-conserving surgery of BC, we are currently conducting Deep Inspiration Breath-Hold (DIBH) Radiation Technique on the left breast with DD 2 Gy up to TD 50 Gy. After 1 month of pelvic RT completion, RT on the paraaortal lymph nodes with DD 1.8 Gy up to TD 50 Gy should be conducted.
 The discussion focuses on the simultaneous expression of two or more neoplasms, their relationship with genetic and other unfavorable predisposing factors, as well as the expected survival after the complex treatment of the two invasive carcinomas, involving IMRT.
 For the treatment of multiple malignancies, each case must be considered individually, ideally by a multidisciplinary team. If it is necessary to apply radiotherapy, the use of high-tech radiotherapeutic apparatus with the ability to perform modern radiotherapy techniques such as IMRT is required.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".