Quantified treatment effect at the individual level is more indicative for personalized radical prostatectomy recommendation: implications for prostate cancer treatment using deep learning.

BACKGROUND: There are potential uncertainties and overtreatment existing in radical prostatectomy (RP) for prostate cancer (PCa) patients, thus identifying optimal candidates is quite important. PURPOSE: This study aims to establish a novel causal inference deep learning (DL) model to discern whether a patient can benefit more from RP and to identify heterogeneity in treatment responses among PCa patients. METHODS: We introduce the Self-Normalizing Balanced individual treatment effect for survival data (SNB). Six models were trained to make individualized treatment recommendations for PCa patients. Inverse probability treatment weighting (IPTW) was used to avoid treatment selection bias. RESULTS: 35,236 patients were included. Patients whose actual treatment was consistent with SNB recommendations had better survival outcomes than those who were inconsistent (multivariate hazard ratio (HR): 0.76, 95% confidence interval (CI), 0.64-0.92; IPTW-adjusted HR: 0.77, 95% CI, 0.61-0.95; risk difference (RD): 3.80, 95% CI, 2.48-5.11; IPTW-adjusted RD: 2.17, 95% CI, 0.92-3.35; the difference in restricted mean survival time (dRMST): 3.81, 95% CI, 2.66-4.85; IPTW-adjusted dRMST: 3.23, 95% CI, 2.06-4.45). Keeping other covariates unchanged, patients with 1 ng/mL increase in PSA levels received RP caused 1.77 months increase in the time to 90% mortality, and the similar results could be found in age, Gleason score, tumor size, TNM stages, and metastasis status. CONCLUSIONS: Our highly interpretable and reliable DL model (SNB) may identify patients with PCa who could benefit from RP, outperforming other models and clinical guidelines. Additionally, the DL-based treatment guidelines obtained can provide priori evidence for subsequent studies.

Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer.

PURPOSE: The efficacy of adjuvant chemotherapy in elderly breast cancer patients is currently controversial. This study aims to provide personalized adjuvant chemotherapy recommendations using deep learning (DL). METHODS: Six models with various causal inference approaches were trained to make individualized chemotherapy recommendations. Patients who received actual treatment recommended by DL models were compared with those who did not. Inverse probability treatment weighting (IPTW) was used to reduce bias. Linear regression, IPTW-adjusted risk difference (RD), and SurvSHAP(t) were used to interpret the best model. RESULTS: A total of 5352 elderly breast cancer patients were included. The median (interquartile range) follow-up time was 52 (30-80) months. Among all models, the balanced individual treatment effect for survival data (BITES) performed best. Treatment according to following BITES recommendations was associated with survival benefit, with a multivariate hazard ratio (HR) of 0.78 (95% confidence interval (CI): 0.64-0.94), IPTW-adjusted HR of 0.74 (95% CI: 0.59-0.93), RD of 12.40% (95% CI: 8.01-16.90%), IPTW-adjusted RD of 11.50% (95% CI: 7.16-15.80%), difference in restricted mean survival time (dRMST) of 12.44 (95% CI: 8.28-16.60) months, IPTW-adjusted dRMST of 7.81 (95% CI: 2.93-11.93) months, and p value of the IPTW-adjusted Log-rank test of 0.033. By interpreting BITES, the debiased impact of patient characteristics on adjuvant chemotherapy was quantified, which mainly included breast cancer subtype, tumor size, number of positive lymph nodes, TNM stages, histological grades, and surgical type. CONCLUSION: Our results emphasize the potential of DL models in guiding adjuvant chemotherapy decisions for elderly breast cancer patients.

High serum amyloid A predicts risk of cognitive impairment after lacunar infarction: Development and validation of a nomogram.

Background: Post-stroke cognitive impairment (PSCI) after lacunar infarction was worth attention in recent years. An easy-to-use score model to predict the risk of PSCI was rare. This study aimed to explore the association between serum amyloid A (SAA) and cognitive impairment, and it also developed a nomogram for predicting the risk of PSCI in lacunar infarction patients. Methods: A total of 313 patients with lacunar infarction were enrolled in this retrospective study between January 2021 and December 2021. They were divided into a training set and a validation set at 70%:30% randomly. The Chinese version of the Mini-Mental State Examination (MMSE) was performed to identify cognitive impairment 3 months after discharge. Univariate and multivariate logistic regression analyses were used to determine the independent risk factors for PSCI in the training set. A nomogram was developed based on the five variables, and the calibration curve and the receiver operating characteristic (ROC) curve were drawn to assess the predictive ability of the nomogram between the training set and the validation set. The decision curve analysis (DCA) was also conducted in both sets. Results: In total, 52/313 (16.61%) participants were identified with PSCI. The SAA levels in patients with PSCI were significantly higher than non-PSCI patients in the training set (P < 0.001). After multivariate analysis, age, diabetes mellitus, white blood count, cystatin C, and SAA were independent risk predictors of PSCI. The nomogram demonstrated a good discrimination performance between the training set (AUC = 0.860) and the validation set (AUC = 0.811). The DCA showed that the nomogram had a well clinical utility in the two sets. Conclusion: The increased SAA is associated with PSCI in lacunar infarction patients, and the nomogram developed with SAA can increase prognostic information for the early detection of PSCI.

A Case of Surgical Removal of a Giant and Multiple Acquired Plate-Like Osteoma Cutis After Implantation of a Dilator.

Osteoma cutis (OC) is a group of rare skin ossification diseases, most of which are secondary to inflammation, scarring, trauma, or tumors, but a small portion are primary. Plate-like osteoma cutis is rare, especially after puberty. This report documents a case of a 30-year-old female, who presented with multiple stone-hard plates on the forehead and bilateral temples, with no relevant family history, or abnormalities in metabolism. These lesions showed slow progression over the last 11 years. The pathological diagnosis confirmed osteoma cutis. The forehead lesions were treated surgically due to aesthetic problems. In addition, long-term follow-up and observations are still needed to determine progression to deeper levels of tissue.

Keratoacanthoma centrifugum marginatum: An unusual case.

Keratoacanthoma centrifugum marginatum (KCM) of the skin is a rare variant of cutaneous keratoacantoma. KCM was first reported in 1962 andpresents with progressive peripheral expansion , no spontaneous clearing and a bank-shaped outer wall with concurrent central healing. Treatment options include topical and systemic therapies.Surgical intervention is the preferred therapy for solitary KCM. We report on surgery and photodynamic therapy delivered sequentially to treat a giant facial Keratoacanthoma centrifugum marginatum patient. It was safe and effective .

Human Schlafen 5 Inhibits Proliferation and Promotes Apoptosis in Lung Adenocarcinoma via the PTEN/PI3K/AKT/mTOR Pathway.

BACKGROUND: Human Schlafen 5 (SLFN5) is reported to inhibit or promote the proliferation of several specific types of cancer cells by our lab and other researchers. We are curious about its implications in lung adenocarcinoma (LUAC), a malignant tumor with a high incidence rate and high mortality. METHOD: Lentiviral stable transfections of SLFN5-specific shRNA for knockdown and SLFN5 full-length coding sequence for overexpression were performed in LUAC cell for proliferation analysis in vitro and in vivo in nude mice. Clinical LUAC samples were collected for immunohistochemical analysis of SLFN5 protein levels. RESULTS: We found that knockdown of endogenous SLFN5 upregulates cancer cell proliferation while inhibiting apoptosis. Besides, SLFN5 inhibition on proliferation was also observed in a nude mouse xenograft model. In contrast, overexpression of exogenous SLFN5 inhibited cell proliferation in vitro and in vivo and promoted apoptosis. As to the signaling pathway, we found phosphatase and tensin homolog on chromosome 10 (PTEN) was positively regulated by SLFN5, while its downstream signaling pathway AKT/mammalian target of rapamycin (mTOR) was inhibited. Moreover, compared with adjacent normal tissues, SLFN5 protein levels were markedly decreased in lung adenocarcinoma tissues. In conclusion, these suggest that human SLFN5 plays inhibitory roles in LUAC progression through the PTEN/PI3K/AKT/mTOR pathway, providing a potential target for developing drugs for lung cancer therapy in the future.