Case Report
Management of Stage IIA2 Cervical Cancer in the Third Trimester: A Case Report and Literature Review
Issue:
Volume 5, Issue 1, March 2026
Pages:
1-5
Received:
4 January 2026
Accepted:
15 January 2026
Published:
30 January 2026
Abstract: Background: Cervical cancer remains one of the most frequently diagnosed gynecological malignancies during pregnancy, presenting a complex clinical dilemma when detected in the second trimester. The management of Stage IIA2 disease, defined by a bulky tumor size of ≥ 4cm without parametrial invasion, is particularly challenging. Standard interventions such as concurrent chemoradiation or immediate radical hysterectomy are incompatible with the preservation of the fetus. Consequently, Neoadjuvant Chemotherapy (NACT) has emerged as a therapeutic strategy to arrest tumor progression and delay delivery until fetal maturity is achieved. Case Presentation: We report the case of a 38-year-old multiparous female (G3P2) who was diagnosed with Stage IIA2 cervical cancer at 25 weeks of gestation. Following a multidisciplinary consultation, the patient was treated with NACT using a Paclitaxel and Carboplatin regimen to control the disease while allowing the fetus to mature. The patient completed four cycles of chemotherapy and was admitted to the obstetrics department at 37 weeks and 1 day of gestation. Pre-operative Magnetic Resonance Imaging (MRI) revealed a residual cervical mass measuring 3.1 x 4.1 x 2.8cm with invasion extending to the upper third of the vagina. Crucially, imaging confirmed the absence of parametrial invasion or pelvic lymphadenopathy. An elective Cesarean section was performed. The procedure resulted in the delivery of a healthy male neonate weighing 2700 grams, with Apgar scores of 8 at 1 minute and 9 at 5 minutes. The maternal postoperative course was uneventful, and the patient was subsequently transferred for definitive oncological management. Conclusion: This case illustrates that the administration of NACT is a viable and effective management strategy for Stage IIA2 cervical cancer diagnosed during the second trimester. This approach facilitates the prolongation of pregnancy to term, thereby minimizing neonatal morbidity associated with preterm birth, without compromising maternal oncological outcomes.
Abstract: Background: Cervical cancer remains one of the most frequently diagnosed gynecological malignancies during pregnancy, presenting a complex clinical dilemma when detected in the second trimester. The management of Stage IIA2 disease, defined by a bulky tumor size of ≥ 4cm without parametrial invasion, is particularly challenging. Standard interventi...
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Research Article
Imagined Speech Classification Using EEG and CBAM-CNN Model
Meenakshi Bisla*
,
Radhey Shyam Anand
Issue:
Volume 5, Issue 1, March 2026
Pages:
6-14
Received:
25 February 2026
Accepted:
25 March 2026
Published:
15 April 2026
DOI:
10.11648/j.ijmcr.20260501.12
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Views:
Abstract: This work introduces a novel Convolutional Block Attention Module (CBAM) along with Convolutional Neural Network (CNN) architecture for the recognition of imagined speech electroencephalography (EEG) data. The real-time data is recorded in Biomedical Signal Processing Laboratory, IIT Roorkee. The Pearson correlation (P) method is utilized to gain understanding of neural activity during speech imagination. The proposed CBAM model leverages both spatial and channel attention, allowing the model to selectively focus on the most distinguishing regions. The incorporation of the CBAM mechanism enhances feature representation by adaptively emphasizing the most informative spatial and channel-wise EEG components, thereby improving both model performance and interpretability. This model also utilizes binary and multiclass classification of imagined speech from correlation-based EEG feature images. The subject independent and subject-dependent classification accuracies obtained from P feature images range from 52.72±7.1% to 68.20±5.3% and 67.47±5.8% to 88.09±4.2% respectively. The results suggest that correlation-based feature representation effectively captures the underlying neural dynamics associated with imagined speech. Comparative analysis with existing state-of-the-art methods indicates that the proposed model achieves improved classification accuracy and generalization, validating its effectiveness for EEG-based imagined speech decoding. These findings indicate the potential of the proposed approach for reliable and scalable brain–computer interface (BCI) applications in real-world scenarios.
Abstract: This work introduces a novel Convolutional Block Attention Module (CBAM) along with Convolutional Neural Network (CNN) architecture for the recognition of imagined speech electroencephalography (EEG) data. The real-time data is recorded in Biomedical Signal Processing Laboratory, IIT Roorkee. The Pearson correlation (P) method is utilized to gain u...
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