Vulva and lower limb cancer: Results of inguinal lymph node staging on 81 cases

Background: The objective of this work completed at the Cancer Institute in Dakar is to report the results of inguinal lymph node dissection in cancers of the lower limb and of the lower genital tract. Methods: This is a retrospective study over a 10-year period. The parameters being studied are histological type, lymph node involvement, postoperative morbidity, recurrence, and survival. Results: 81 patients received surgery over a period of 7 years. The average age of our patients was 61. The sex ratio is 0.74 with 34 men and 47 women. There were 70 cases of cancer of the lower limbs (86%) and 11 cases of cancer of the vulva (14%). The most common histological type was squamous cell carcinoma (SCC) with 41 cases (51%). Clinical inguinal involvement was noted in 58 patients (72.5%) with palpable lymph nodes. All vulvar cancer patients developed histologically positive nodes. Melanoma patients were more susceptible to developing positive nodes. In sarcoma there were more matches between clinical and histological positive nodes. No vascular and nerve damage was reported. The average length of hospitalization was 5 days, with 3 days being the shortest stay, and 40 days the longest stay. Local complications consisted of suture releases in 9 cases, and 6 surgical necrosis of the wound. A seroma was found with an average duration of 35 days in 69 patients (85%). Postoperative deaths occurred in 5 cases (6%), 1 after a renal failure, 1 due to thromboembolic disease, 1 due to sepsis, and 2 deaths occurred after patients experienced respiratory distress. Conclusion: After five years of follow-up care, no patient presented chronic sequelae after inguinal dissection, 7 patients (8.75%) had local recurrence, and 4 patients (7.7%) had lymph node metastases. We recorded 33 cancer-related deaths (41%). Chronic complications, including lymphedema are underestimated and require better assessment methods for prevention and treatment.

Deuterium depletion inhibits lung cancer cell growth and migration in vitro and results in severalfold increase of median survival time of non-small cell lung cancer patients receiving conventional therapy

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for 84% of all lung cancer diagnoses. In advanced NSCLC, including adenocarcinoma and squamous cell carcinoma, median survival time (MST) rarely exceeds 10-12 months. Reduced deuterium (D) concentration in water of tissue culture media and in drinking water for humans has shown a strong anticancer effect in previous investigations. In the present study, 1 parts per million (ppm) decrease of D-concentration every 8 hours resulted in reduced growth rate of the A459 lung cancer cell line in vitro, and the cell migration was also dose-dependently reduced. Retrospective study of 183 NSCLC patients consuming commercially available deuterium-depleted water (DDW) revealed a severalfold increase of MST, which was 149 months for 19 patients and 40 months for 110 patients, who started DDW-consumption at early or advanced stage, respectively. Interestingly, MST showed a significant difference by gender (107 months in females and 41.2 months in males). Application of DDW in combination with surgery plus other conventional therapies (68 patients) gave 149 months MST, while for DDW combined with chemotherapy only (48 patients) MST was 43.7 months. The present results support earlier data that integration of D-depletion to conventional therapies increases the efficacy of therapy, reduces relapse rate and increases MST.

Advances in artificial neural networks as a disease prediction tool

Throughout the last decade, utilization of machine learning has seen a sharp rise in fields such as computing, transportation, engineering, and medicine. Artificial neural networks (ANNs) have demonstrated increased application due to their versatility and ability to learn from large datasets. The emergence of electronic health records has propelled healthcare into an era of personalized medicine largely aided by computers. This review summarizes the current state of ANNs as a predictive tool in medicine and the downfalls of reliance on a self-adjusting computer network to make healthcare decisions. Medical ANN studies can be grouped into three categories - diagnosis, classification, and prediction, with diagnostic studies currently dominating the field. However, recent trends show prediction studies may soon outnumber the remaining categories. ANN prediction studies dominate in fields such as cardiovascular disease, neurologic disease, and osteoporosis. Neural networks consistently show higher predictive accuracy than industry standards. But several pitfalls are preventing mainstream adoption. Clinicians often rely on situational pearls to make complex healthcare decisions, ANNs often do not account for intuitive variables during their analysis. Instead, ANNs rely on incomplete patient data and ‘black box’ computing to make decisions that are not completely transparent to the end-user. This has led to ‘runaway’ networks that may ultimately make inaccurate and harmful decisions. This review emphasizes the extensive potential of machine learning in medicine and the obstacles that must be overcome to utilize its full potential.