Quality assessment of surgery for colorectal cancer:Where do we stand?

Quality assurance in surgery has been one of the most important topics of debate among colorectal surgeons in the past decade.It has produced new surgical standards that led in part to the impressive oncological outcomes we see in many units today.Total mesorectal excision,complete mesocolic excision(CME),and the Japanese D3 lymphadenectomy are now benchmark techniques embraced by many surgeons and widely recommended by surgical societies.However,there are still ongoing discrepancies in outcomes largely based on surgeon performance.This is one of the main reasons why many countries have shifted colorectal cancer surgery only to high volume centers.Defining markers of surgical quality is thus a perquisite to ensure that standards and oncological outcomes are met at an institutional level.With the evolution of CME surgery,various quality markers have been described,mostly based on measurements on the surgical specimen and lymph node yield,while others have proposed radiological markers(i.e.arterial stumps)measured on postoperative scans as part of the routine cancer follow-up.There is no ideal marker;however,taken together and assembled into a new score or set of criteria may become a future point of reference for reporting outcomes of colorectal cancer surgery in research studies and defining subspecialization requirements both at an individual and hospital level.

Role of immunotherapy in gastric cancer with liver metastasis

Gastric cancer continues to be a significant issue for public health,marked by its widespread occurrence and high mortality rates,even as the incidence of the disease shows a declining trend.The liver is the primary site for metastatic spread,with the peritoneum,lungs,and bones also being common targets.With the advent of biologic treatments and the introduction of immunotherapy for patients with metastatic conditions,the options to treat metastatic gastric cancer have expanded.This diversified therapeutic approach is designed to enhance patient quality of life and prolong survival,showcasing the progress in treatment modalities for individuals with gastric cancer and liver metastases.

The Use of Artificial Intelligence on Segmental Volumes, Constructed from MRI and CT Images, in the Diagnosis and Staging of Cervical Cancers and Thyroid Cancers: A Study Protocol for a Randomized Controlled Trial

Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accurate and effective therapeutic approach. For accurate diagnosis and staging of cervical and thyroid cancers, we aim to create a diagnostic method, optimized by the algorithms of artificial intelligence and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation computer tomography (CT) scan, magnetic resonance imaging (MRI) of the doctor and improve therapeutic planning. Materials and Methods: The optimization of the computer assisted diagnosis (CAD) method will consist in the development and formation of artificial intelligence models, using algorithms and tools used in segmental volumetric constructions to generate 3D images from MRI/CT. We propose a comparative study of current developments in “DICOM” image processing by volume rendering technique, the use of the transfer function for opacity and color, shades of gray from “DICOM” images projected in a three-dimensional space. We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven to be effective in representing complex data distributions, as we do in this study. Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical and thyroid cancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the CAD method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixture. By using this method in medical practice, we aim to avoid errors, provide precision in diagnosing, staging and establishing the therapeutic plan in cancers of the cervix and thyroid using AI. Conclusion: The use of the CAD method can increase the quality of life by avoiding intra and postoperative complications in surgery, intraoperative orientation and the precise determination of radiation doses and irradiation zone in radiotherapy.

The Cytotoxic Effect of Cecropin A and Cecropin B on the MDA-MB-231 and M14K Tumour Cell Lines

The aim of the present in vitro study was to assess the tumoricidal potential of the following natural peptides belonging to the Cecropin family, namely Cecropin A and B, on a series of tumour cell lines: MDA-MB-231 (breast adenocarcinoma) and M14K (human mesothelioma). The experimental results reveal that the cytotoxic effects of the two peptides depend on their concentration. Their efficiency is significant at 120 μM concentrations and it persists even at 60 μM concentrations. The effects were insignificant at 30 μM concentrations. On the other hand, the cytotoxic potential was not significantly dependant on the type of peptide but more on the type of tumour cell line used. The MDA MB 231 line cells were much more sensitive to the action of Cecropins A and B than the M14K line cells. The prospects brought about by this experimental research consist of the collection of in vitro experimental data on the tumoricidal potential of these natural cytotoxic peptides on tumour cells. This will enable specialists to develop future in vivo experimental models in order to test the antitumor effect of these cytotoxic peptides. The ultimate goal would be the discovery of agents with efficient antitumor properties, i.e. with maximum tumoricidal effects and minimum toxic side effects.

Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

Deep learning (DL) has seen an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image. The purpose of the work converges in determining the importance of each component, describing the specificity and correlations of these elements involved in achieving the precision of interpretation of medical images using DL. The major contribution of this work is primarily to the updated characterisation of the characteristics of the constituent elements of the deep learning process, scientific data, methods of knowledge incorporation, DL models according to the objectives for which they were designed and the presentation of medical applications in accordance with these tasks. Secondly, it describes the specific correlations between the quality, type and volume of data, the deep learning patterns used in the interpretation of diagnostic medical images and their applications in medicine. Finally presents problems and directions of future research. Data quality and volume, annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform image analysis tasks. Moreover, the development of models capable of extracting unattended features and easily incorporated into the architecture of DL networks and the development of techniques to search for a certain network architecture according to the objectives set lead to performance in the interpretation of medical images.

The Use of Artificial Intelligence on Colposcopy Images, in the Diagnosis and Staging of Cervical Precancers: A Study Protocol for a Randomized Controlled Trial

Rationale and Objectives: Accurate diagnosis and staging of cervical precancers is essential for practical medicine in determining the extent of the lesion extension and determines the most correct and effective therapeutic approach. For accurate diagnosis and staging of cervical precancers, we aim to create a diagnostic method optimized by artificial intelligence (AI) algorithms and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation of colposcopy images and improve therapeutic planning. Materials and Methods: The optimization of the method will consist in the development and formation of artificial intelligence models, using complicated convolutional neural networks (CNN) to identify precancers and cancers on colposcopic images. We will use topologies that have performed well in similar image recognition projects, such as Visual Geometry Group Network (VGG16), Inception deep neural network with an architectural design that consists of repeating components referred to as Inception modules (Inception), deeply separable convolutions that significantly reduce the number of parameters (MobileNet) that is a class of Convolutional Neural Network (CNN), Return of investment for machine Learning (ROI), Fully Convolutional Network (U-Net) and Overcomplete Convolutional Network Kite-Net (KiU-Net). Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical precancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the computer assisted diagnostic (CAD) method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixture. By using this method in medical practice, we aim to avoid errors, provide precision in diagnosing, staging and establishing the therapeutic plan in cervical precancers using AI. Conclusion: This diagnostic method, optimized by artificial intelligence algorithms and validated by the clinical trial, which we consider “second opinion”, improves the quality standard in diagnosing, staging and establishing therapeutic conduct in cervical precancer.

Oxaliplatin-induced neuropathy and colo-rectal cancer patient's quality of life:Practical lessons from a prospective cross-sectional,real-world study

BACKGROUND Colon cancer is one the most common forms of cancer in both sexes.Due to important progress in the field of early detection and effective treatment,colon and rectal cancer survivors currently account for 10%of cancer survivors worldwide.However,the effects of anti-cancer treatments,especially oxaliplatinbased chemotherapy,on the quality of life(QoL)have been less evaluated.Although the incidence of severe chemotherapy-induced neuropathy(CIPN)in clinical studies is below 20%,data from real-world studies is scarce,and CIPN is probably under-reported due to patient selection and the patients’fear that reporting side-effects might lead to treatment cessation.AIM To determine the impact of CIPN on QoL in colorectal cancer patients with a recent history of oxaliplatin-based chemotherapy.METHODS We performed a prospective cross-sectional study in two major Romanian oncology tertiary hospitals—the Regional Institute of Oncology Ia?i(Iasi,Romania)and the Fundeni Clinical Oncology Institute(Bucharest,Romania).All consecutive patients with colon or rectal cancer,undergoing Oxaliplatin-based chemotherapy that consented to enroll in the study,were assessed by means of two questionnaires—the EORTC QQ-CR29(quality of life in colon and rectal cancer patients)and the QLQ-CIPN20(assessment of neuropathy).Several demographical,social,clinical and treatment data were also collected.Statistical analysis was performed by means of SPSS v20.The student t test was used to assess the relationship between the QLQ-CIPN20 and QLQ-CR29 results.Kaplan Meyer-curves were used to report 3-year progression-free survival(PFS)in patients that discontinued chemotherapy vs those that completed the recommended course.RESULTS Of the 267 patients that fulfilled the inclusion criteria in the pre-specified time frame,101(37.8%)agreed to participate in the clinical study.At the time of the enrolment in the study,over 50%of the patients had recently interrupted their oxaliplatin-based chemotherapy,most often due to neuropathy.Almost 85%of the responders reported having tingling or numbness in their fingers or hands,symptoms that were associated with pain in over 20%of the cases.When comparing the scores in the two questionnaires,a statistically significant relationship(P<0.001)was found between the presence of neuropathic symptoms and a decreased quality of life.This correlation was consistent when the patients were stratified by sex,disease stage,comorbidities and the presence of stoma or treatment type,suggesting that neuropathy in itself may be a reason for a decreased quality of life.At the 3 year final assessment,median recurrence-free survival in stageⅢpatients was 26.88 mo.When stratified by completion of chemotherapy,median recurrence freesurvival of stageⅢpatients that completed chemotherapy was 28.27 mo vs 24.33 mo in patients that discontinued chemotherapy due to toxicity,a difference that did not reach statistical significance.CONCLUSION CIPN significantly impacts QoL in colorectal cancer patients.CIPN is also the most frequent reason for treatment discontinuation.Physicians should actively assess for CIPN in order to prevent chronic neuropathy.

Giant androgen-producing adrenocortical carcinoma with atrial flutter: A case report and review of the literature

BACKGROUND Adrenocortical carcinoma(ACC),the second most aggressive malignant tumor,lacks epidemiological data worldwide;therefore,every new case can improve the understanding of the pathology and treatment of this malignancy.CASE SUMMARY We present the case of a 66-year-old Caucasian woman with a giant androgenproducing ACC(21 cm×17 cm×12 cm;2100 g),without metastases,which unusually presented with an acute onset of atrial flutter and congestive heart failure.The cardiac complications observed in our case support the hypothesis that androgen excess in women is a cardiovascular risk factor.Androgen excess in women can be a rare cause of reversible dilated cardiomyopathy,therefore a comprehensive approach to the patient is essential to improve the recognition of androgen-secreting ACC.The atrial flutter was remitted after initiation of drug treatment during admission.The severe heart failure was totally remitted at 6 mo after radical open surgery to remove the giant ACC.CONCLUSION Radical open surgery to remove a giant androgen-producing ACC was the firstline treatment to cure the excess of androgen,which determined the total remission of cardiac complications at 6 mo after surgery in the women of this case report.

Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.

Age,blood tests and comorbidities and AIMS65 risk scores outperform Glasgow-Blatchford and pre-endoscopic Rockall score in patients with upper gastrointestinal bleeding

BACKGROUND Upper gastrointestinal(GI)bleeding is a life-threatening condition with high mortality rates.AIM To compare the performance of pre-endoscopic risk scores in predicting the following primary outcomes:In-hospital mortality,intervention(endoscopic or surgical)and length of admission(≥7 d).METHODS We performed a retrospective analysis of 363 patients presenting with upper GI bleeding from December 2020 to January 2021.We calculated and compared the area under the receiver operating characteristics curves(AUROCs)of Glasgow-Blatchford score(GBS),pre-endoscopic Rockall score(PERS),albumin,international normalized ratio,altered mental status,systolic blood pressure,age older than 65(AIMS65)and age,blood tests and comorbidities(ABC),including their optimal cut-off in variceal and non-variceal upper GI bleeding cohorts.We subsequently analyzed through a logistic binary regression model,if addition of lactate increased the score performance.RESULTS All scores had discriminative ability in predicting in-hospital mortality irrespective of study group.AIMS65 score had the best performance in the variceal bleeding group(AUROC=0.772;P<0.001),and ABC score(AUROC=0.775;P<0.001)in the non-variceal bleeding group.However,ABC score,at a cut-off value of 5.5,was the best predictor(AUROC=0.770,P=0.001)of inhospital mortality in both populations.PERS score was a good predictor for endoscopic treatment(AUC=0.604;P=0.046)in the variceal population,while GBS score,(AUROC=0.722;P=0.024),outperformed the other scores in predicting surgical intervention.Addition of lactate to AIMS65 score,increases by 5-fold the probability of in-hospital mortality(P<0.05)and by 12-fold if added to GBS score(P<0.003).No score proved to be a good predictor for length of admission.CONCLUSION ABC score is the most accurate in predicting in-hospital mortality in both mixed and non-variceal bleeding population.PERS and GBS should be used to determine need for endoscopic and surgical intervention,respectively.Lactate can be used as an additional tool to risk scores for predicting inhospital mortality.