Objective: This prospective comparative study aimed to investigate the applied value of whole body 2deoxy-2[fluorine-18]fluoro-D-glucose positron emission tomography integrated with computed tomography ( 18 F-FDG PET/CT) in comparison to pelvic magnetic resonance imaging (MRI) in early cervical cancer patients.Material and methods: A prospective study was performed on 47 clinically early-stage cervical cancer patients evaluated with positron emission tomography/computed tomography (PET/CT) and MRI before surgery. The final postoperative histopathology report served as the reference standard. Both PET/CT and MRI images were analyzed and correlated with histopathologic findings concerning parametrial and lymph node involvement.Results: Sensitivity, specificity, and negative predictive value (NPV) of PET/CT were 33.3%, 81.8%, and 94.7%, respectively, for parametrium assessment. And the corresponding values of pelvic MRI were 33.3%, 63.6%, and 93.3%, respectively (PET/CT versus MRI, p > 0.05). The positive predictive value (PPV) of PET/CT (11.1%) was higher than MRI (5.9%) for parametrial assessment (p < 0.05). The sensitivity, specificity, PPV, and NPV of PET/CT were 75%, 83.7%, 30%, and 97.3%, respectively, for lymph node assessment. And the corresponding values of MRI were 75%, 81.3%, 27.3%, and 97.2%, respectively (PET/CT versus MRI, p > 0.05). There was no significant difference between MRI and PET/CT concerning stage migration (p = 0.4276). Conclusion:The PET/CT had no additional utility (compared to MRI) in the evaluation of local staging of clinically early cervical carcinoma patients.

Objective: Automated cell nuclei segmentation is vital for the histopathological diagnosis of cancer. However, nuclei segmentation from ``hematoxylin and eosin'' (HE) stained ``whole slide images'' (WSIs) remains a challenge due to noise-induced intensity variations and uneven staining. The goal of this paper is to propose a novel deep learning model for accurately segmenting the nuclei in HE-stained WSIs. Approach: We introduce FEEDNet, a novel encoder-decoder network that uses LSTM units and ``feature enhancement blocks'' (FE-blocks). Our proposed FE-block avoids the loss of location information incurred by pooling layers by concatenating the downsampled version of the original image to preserve pixel intensities. FEEDNet uses an LSTM unit to capture multi-channel representations compactly. Secondly, for datasets that provide class information, we train a multiclass segmentation model, which generates masks corresponding to each class at the output. Using this information, we generate more accurate binary masks than that generated by conventional binary segmentation models. Main results: We have thoroughly evaluated FEEDNet on CoNSeP, Kumar, and CPM-17 datasets. FEEDNet achieves the best value of PQ (panoptic quality) on CoNSeP and CPM-17 datasets and the second best value of PQ on the Kumar dataset. The 32-bit floating-point version of FEEDNet has a model size of 64.90MB. With INT8 quantization, the model size reduces to only 16.51 MB, with a negligible loss in predictive performance on Kumar and CPM-17 datasets and a minor loss on the CoNSeP dataset. Significance: Our proposed idea of generalized class-aware binary segmentation is shown to be accurate on a variety of datasets. FEEDNet has a smaller model size than the previous nuclei segmentation networks, which makes it suitable for execution on memory-constrained edge devices. The state-of-the-art predictive performance of FEEDNet makes it the most preferred network. The source code can be obtained from https://github.com/CandleLabAI/FEEDNet.

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