Utilization of machine learning methods for prediction of acute and late rectal toxicity due to curative prostate radiotherapy.

OBJECTIVE: Rectal toxicity is one of the primary dose-limiting side effects of prostate cancer radiotherapy, and consequential impairment on quality of life in these patients with long survival is an important problem. In this study, we aimed to evaluate the possibility of predicting rectal toxicity with artificial intelligence model which was including certain dosimetric parameters. MATERIALS AND METHODS: One hundred and thirty-seven patients with a diagnosis of prostate cancer who received curative radiotherapy for prostate +/- pelvic lymphatics were included in the study. The association of the clinical data and dosimetric data between early and late rectal toxicity reported during follow-up was evaluated. The sample size was increased to 274 patients by synthetic data generation method. To determine suitable models, 15 models were studied with machine learning algorithms using Python 2.3, Pycaret library. Random forest classifier was used with to detect active variables. RESULTS: The area under the curve and accuracy were found to be 0.89-0.97 and 95%-99%, respectively, with machine learning algorithms. The sensitivity values for acute and toxicity were found to be 0.95 and 0.99, respectively. CONCLUSION: Early or late rectal toxicity can be predicted with a high probability via dosimetric and physical data and machine learning algorithms of patients who underwent prostate +/- pelvic radiotherapy. The fact that rectal toxicity can be predicted before treatment, which may result in limiting the dose and duration of treatment, makes us think that artificial intelligence can enter our daily practice in a short time in this sense.

Machine learning for predicting colon cancer recurrence.

INTRODUCTION: Colorectal cancer (CRC) is a global public health concern, ranking among the most commonly diagnosed malignancies worldwide. Despite advancements in treatment modalities, the specter of CRC recurrence remains a significant challenge, demanding innovative solutions for early detection and intervention. The integration of machine learning into oncology offers a promising avenue to address this issue, providing data-driven insights and personalized care. METHODS: This retrospective study analyzed data from 396 patients who underwent surgical procedures for colon cancer (CC) between 2010 and 2021. Machine learning algorithms were employed to predict CC recurrence, with a focus on demographic, clinicopathological, and laboratory characteristics. A range of evaluation metrics, including AUC (Area Under the Receiver Operating Characteristic), accuracy, recall, precision, and F1 scores, assessed the performance of machine learning algorithms. RESULTS: Significant risk factors for CC recurrence were identified, including sex, carcinoembryonic antigen (CEA) levels, tumor location, depth, lymphatic and venous invasion, and lymph node involvement. The CatBoost Classifier demonstrated exceptional performance, achieving an AUC of 0.92 and an accuracy of 88 % on the test dataset. Feature importance analysis highlighted the significance of CEA levels, albumin levels, N stage, weight, platelet count, height, neutrophil count, lymphocyte count, and gender in determining recurrence risk. DISCUSSION: The integration of machine learning into healthcare, exemplified by this study's findings, offers a pathway to personalized patient risk stratification and enhanced clinical decision-making. Early identification of individuals at risk of CC recurrence holds the potential for more effective therapeutic interventions and improved patient outcomes. CONCLUSION: Machine learning has the potential to revolutionize our approach to CC recurrence prediction, emphasizing the synergy between medical expertise and cutting-edge technology in the fight against cancer. This study represents a vital step toward precision medicine in CC management, showcasing the transformative power of data-driven insights in oncology.

Artificial intelligence for prediction of endometrial intraepithelial neoplasia and endometrial cancer risks in pre- and postmenopausal women.

BACKGROUND: The current approach to endometrial cancer screening requires that all patients be able to recognize symptoms, report them, and carry out appropriate interventions. The current approach to endometrial cancer screening could become a problem in the future, especially for Black women and women from minority groups, and could lead to disparities in receiving proper care. Moreover, there is a lack of literature on artificial intelligence in the prediction and diagnosis of endometrial intraepithelial neoplasia and endometrial cancer. OBJECTIVE: This study analyzed different artificial intelligence methods to help in clinical decision-making and the prediction of endometrial intraepithelial neoplasia and endometrial cancer risks in pre- and postmenopausal women. This study aimed to investigate whether artificial intelligence may help to overcome the challenges that statistical and diagnostic tests could not. STUDY DESIGN: This study included 564 patients. The features that were collected included age, menopause status, premenopausal abnormal bleeding and postmenopausal bleeding, obesity, hypertension, diabetes mellitus, smoking, endometrial thickness, and history of breast cancer. Endometrial sampling was performed on all women with postmenopausal bleeding and asymptomatic postmenopausal women with an endometrial thickness of at least 3 mm. Endometrial biopsy was performed on premenopausal women with abnormal uterine bleeding and asymptomatic premenopausal women with suspected endometrial lesions. Python was used to model machine learning algorithms. Random forest, logistic regression, multilayer perceptron, Catboost, Xgboost, and Naive Bayes methods were used for classification. The synthetic minority oversampling technique was used to correct the class imbalance in the training sets. In addition, tuning and boosting were used to increase the performance of the models with a 5-fold cross-validation approach using a training set. Accuracy, sensitivity, specificity, positive predictive value, and F1 score were calculated. RESULTS: The prevalence of endometrial or preuterine cancer was 7.9%. Data from 451 patients were randomly assigned to the training group, and data from another 113 patients were used for internal validation. Of note, 3 of 9 features were selected by the Boruta algorithm for use in the final modeling. Age, body mass index, and endometrial thickness were all associated with a high risk of developing precancerous and cancerous diseases, after fine-tuning for the multilayer computer to have the highest area below the receiver operating characteristic curve (area under the curve, 0.938) to predict a precancerous disease. The accuracy was 0.94 for predicting a precancerous disease. Precision, recall, and F1 scores for the test group were 0.71, 0.50, and 0.59, respectively. CONCLUSION: Our study found that artificial intelligence can be used to identify women at risk of endometrial intraepithelial neoplasia and endometrial cancer. The model is not contingent on menopausal status or symptoms. This may be an advantage over the traditional methodology because many women, especially Black women and women from minority groups, could not recognize them. We have proposed to include patients to provide age and body mass index, and measurement of endometrial thickness by either sonography or artificial intelligence may help improve healthcare for women in rural or minority communities.

The diagnostic value of magnetic resonance imaging-based texture analysis in differentiating enchondroma and chondrosarcoma.

OBJECTIVE: To assess the diagnostic performance of MRI-based texture analysis for differentiating enchondromas and chondrosarcomas, especially on fat-suppressed proton density (FS-PD) images. MATERIALS AND METHODS: The whole tumor volumes of 23 chondrosarcomas and 24 enchondromas were manually segmented on both FS-PD and T1-weighted images. A total of 861 radiomic features were extracted. SelectKBest was used to select the features. The data were randomly split into training (n = 36) and test (n = 10) for T1-weighted and training (n = 37) and test (n = 10) for FS-PD datasets. Fivefold cross-validation was performed. Fifteen machine learning models were created using the training set. The best models for T1-weighted, FS-PD, and T1-weighted + FS-PD images were selected in terms of accuracy and area under the curve (AUC). RESULTS: There were 7 men and 16 women in the chondrosarcoma group (mean ± standard deviation age, 45.65 ± 11.24) and 7 men and 17 women in the enchondroma group (mean ± standard deviation age, 46.17 ± 11.79). Naive Bayes was the best model for accuracy and AUC for T1-weighted images (AUC = 0.76, accuracy = 80%, recall = 80%, precision = 80%, F1 score = 80%). The best model for FS-PD images was the K neighbors classifier for accuracy and AUC (AUC = 1.00, accuracy = 80%, recall = 80%, precision = 100%, F1 score = 89%). The best model for T1-weighted + FS-PD images was logistic regression for accuracy and AUC (AUC = 0.84, accuracy = 80%, recall = 60%, precision = 100%, F1 score = 75%). CONCLUSION: MRI-based machine learning models have promising results in the discrimination of enchondroma and chondrosarcoma based on radiomic features obtained from both FS-PD and T1-weighted images.

Protective effect of theophylline on renal functions in experimental pneumoperitoneum model.

Our objective in this experimental study is to research the effect of the intra-abdominal pressure which rises following pneumoperitoneum and whether Theophylline has a possible protective activity on this situation. In our study, 24 Wistar Albino rats were used. Rats were divided into two groups. The first group was set for only pneumoperitoneum model. The second group was given 15 mg/kg of Theophylline intraperitoneally before setting pneumoperitoneum model. Then urea, creatinine, cystatin-C, tissue and serum total antioxidant capacity, total oxidant capacity and oxidative stress index in two groups were measured and compared with each other. Apoptosis and histopathological conditions in the renal tissues were examined. The differences between the groups were analyzed with the Mann-Whitney U test. Results were considered significant at p < 0.05. No statistically significant difference was determined between tissue and serum averages in two groups in terms of TAS, TOS and OSI values (p > 0.05). The mean value of urea were similar in pneumoperitoneum and pneumoperitoneum + theophylline groups (p = 0.12). The mean cystatin-C value was 2.2 ± 0.3 µg/mL in pneumoperitoneum, 1.74 ± 0.33 µg/mL in pneumoperitoneum + theophylline (p = 0.002). According to our study, lower cystatin-C levels in the group, where Theophylline was given, are suggestive of lower renal injury in this group. However, this opinion is interrogated as there is no difference in terms of tissue and serum TAS, TOS, OSI and urea values between the groups.

Cancer detection rates of different prostate biopsy regimens in patients with renal failure.

We aimed to evaluate the cancer detection rates of 6-, 10-, 12-core biopsy regimens and the optimal biopsy protocol for prostate cancer diagnosis in patients with renal failure. A total of 122 consecutive patients with renal failure underwent biopsy with age-specific prostate-specific antigen (PSA) levels up to 20 ng/mL. The 12-core biopsy technique (sextant biopsy + lateral base, lateral mid-zone, lateral apex, bilaterally) performed to all patients. Pathology results were examined separately for each sextant, 10-core that exclude parasagittal mid-zones from 12-cores (10a), 10-core that exclude apex zones from 12-cores (10b) and 12-core biopsy regimens. Of 122 patients, 37 (30.3%) were positive for prostate cancer. The cancer detection rates for sextant, 10a, 10b and 12 cores were 17.2%, 29%, 23.7% and 30.7%, respectively. Biopsy techniques of 10a, 10b and 12 cores increased the cancer detection rates by 40%, 27.5% and 43.2% among the sextant technique, respectively. Biopsy techniques of 10a and 12 cores increased the cancer detection rates by 17.1% and 21.6% among 10b biopsy technique, respectively. There were no statistical differences between 12 core and 10a core about cancer detection rate. Adding lateral cores to sextant biopsy improves the cancer detection rates. In our study, 12-core biopsy technique increases the cancer detection rate by 5.4% among 10a core but that was not statistically different. On the other hand, 12-core biopsy technique includes all biopsy regimens. We therefore suggest 12-core biopsy or minimum 10-core strategy incorporating six peripheral biopsies with elevated age- specific PSA levels up to 20 ng/mL in patients with renal failure.

The role of phosphodiesterase type-5 inhibitors in treatment of lower urinary tract symptoms associated with benign prostatic hyperplasia.

Phosphodiesterase type-5 (PDE-5) inhibitors are approved as the first line of therapy for the treatment of erectile dysfunction. However, different studies have been performed to study the use of these agents in other areas of urology. There are many studies related to the use of PDE-5 inhibitors as a monotherapy or combination therapy with alpha-blockers for lower urinary tract symptoms (LUTS) associated with benign prostatic hyperplasia (BPH). It has been shown that contractions induced by various agents or electrical field stimulation in organ bath models of prostatic tissue can be reversed by PDE-5 inhibitors. Age, body mass index and the severity of LUTS are important factors for the selection of patients suitable for this treatment. It has also been reported that the use of PDE-5 inhibitors can relieve the chronic pelvic ischemia and endothelial dysfunction associated with metabolic syndrome. Most of the side effects observed with PDE-5 inhibitors are minimal and tolerable. The use of PDE-5 inhibitors is absolutely contraindicated in patients taking nitrate preparations. A significant interaction has not been observed even when a patient is taking several antihypertensive agents concurrently. Co-administration of alpha-blockers and PDE-5 inhibitors may result in orthostatic hypotension; therefore, patients should be stable on α-blocker therapy before the initiation of the combination therapy, and the initial PDE-5 inhibitor dose should be the lowest possible. In this review, our aim was to evaluate the role of PDE-5 inhibitors in the treatment of LUTS associated with BPH by analyzing the current literature.

Dermoid cyst in bony pelvis that coexists with pheochromocytoma: report of a case and review of the literature.

A 42-year-old man presented with right lower quadrant abdominal pain and dysuria. The bladder was displaced to the right side of the pelvis in excretory urography. Abdominal CT revealed a mass in right adrenal gland, measuring 8 cm in diameter. There was also a cystic mass; filling left half of the bony pelvis and displacing bladder to the right, measuring 14.5 x 10, 5 x 7 cm. The patient underwent right adrenalectomy and pelvic mass excision. Pathologic examination showed that the adrenal mass was pheochromocytoma and pelvic mass was dermoid cyst. This case is the first one in literature that an intrapelvic dermoid cyst is not derived from an organ coexists with pheochromocytoma.

Prevention of shock wave-induced renal oxidative stress by melatonin: an experimental study.

Our aim was to evaluate the effects of the potent endogenous free radical scavenger melatonin on extracorporeal-shock-wave lithotripsy (ESWL) induced renal impairment. The study was performed using 30 rabbits which were divided into two groups. Both groups were exposed to 3,000 shock waves at 18 Kv. The animals in the first group were treated with melatonin for 8 days. Controls and melatonin treated rabbits were killed a week after ESWL. MDA, uric acid and white cell counts were used as markers of oxidative stress. The mean levels of uric acid and white cell counts were significantly lower in the melatonin treated group than in the controls. The mean level of MDA was also significantly lower in the melatonin treated group compared to the controls. Our results show that melatonin may exhibit a protective effect on free radical mediated oxidative damage induced by ESWL in rabbit kidney.

Antegrade extraperitoneal approach to radical cystectomy and ileal neobladder.

BACKGROUND: We describe a new operative technique for the surgical treatment of bladder cancer. METHODS: Male patients with invasive bladder cancer were managed by radical cystoprostatectomy using a technically different approach than the conventional method. The main feature of this method includes a small vertical incision between pubis and umbilicus, keeping the abdominal peritoneum closed during the surgery. Reperitonealization is done at completion to isolate the urinary anastomoses from the bowel anastomoses. RESULTS: Seventy-six consecutive patients with bladder cancer underwent this operation. The mean operation time was 4 h 30 min and the mean hospital stay was 11 days. No mortality was seen in the early postoperative period. Only one patient developed serious bowel distension. Two patients developed pneumonia. Wound infection was seen in two patients. Two patients experienced hydronephrosis in the late period of follow-up. Four patients developed pelvic lymphocele. There was no evidence of postoperative electrolyte loss in any of the patients. Three patients developed abdominal hernia and this was corrected with surgical treatment. Urodynamic evaluation of 15 patients showed a low capacity reservoir. All patients were continent during the daytime. CONCLUSION: This technique keeps the abdominal peritoneum closed during radical cystoprostatectomy, preventing the patients from complications, such as infection, water and electrolyte imbalances. The technique also decreases the recovery time.