Introduction

Epidemiology

Screening

Incidental versus screen-detected pulmonary nodule management

Clinical manifestations

Diagnosis and staging

Brief update on the eighth edition tumor-node-metastasis classification of lung cancer

Treatment

Prognosis

Summary

References

INTRODUCTION

Lung cancer is the world’s leading cause of cancer death. This is largely because it is initially asymptomatic and typically discovered at advanced stages. Screening for lung cancer by low-dose computed tomography (LDCT) has recently been shown to have a mortality benefit, and implementation of this practice is growing. Once suspected, lung cancer must be diagnosed and staged, and there are recent guidelines to aid in this process. Treatment is determined by subtype and stage of cancer and there are several personalized therapies that did not exist just a few years ago. This review provides a broad outline of this disease, helping clinicians identify such patients and familiarizing them with lung cancer care so they are better equipped to guide their patients along this challenging journey.

SUMMARY

Lung cancer remains a highly lethal disease. The implementation of widespread lung cancer screening holds promise for the future. Given the nonspecific clinical presentation, clinicians should consider the diagnosis in any former or current smoker who presents with worrisome symptoms. If lung cancer is suspected, referral to a pulmonologist is strongly recommended. There are various minimally invasive diagnostic and staging modalities currently available and there has been tremendous advancement in our understanding of lung cancer biology leading to various new treatment options.

مقاله سال 2017

اهداف: عوارض دیابت و به طور ویژه زخم پای دیابتی (DFU) ، با کیفیت زندگی مرتبط با سلامت (HRQoL) پایین همراه است. ما ارزیابی کردیم که چگونه عوارض دیابت بر بهبود HRQoL در طی دوره درمان DFU تاثیر می گذارد.

روشها: 1088بیمار که به منظور درمان زخم پای دیابتی در مرا کز مشارکت در مطالعه یورودیال حضور پیدا کرده بودند، به صورت آینده نگر برای مدت یک سال مورد پیگیری قرار گرفتند. HRQoL هم در ابتدای حضور در مطالعه و هم پس از بهبودی یا در پایان پیگیری ، با استفاده از EQ-5D:یک ابزار استاندارد شامل چهار حیطه و پنج شاخص خلاصه ،انداره گیری شد. تاثیر همزمانی بیماری دیابت بر دوره HRQoL برای هر یک از نتایج EQ-5D با استفاده از تحلیل رگرسیون خطی چند گانه، مناسب برای ویژگی های پایه ای ، مورد ارزیابی قرار گرفت.

نتایج:HRQoL در کلیه برآ یند های EQ-5D در طی دوره درمان برای افرادی با زخم پای دیابتی که درمان شده بودند، بهبود یافت. تفاوت آماری معنی دار کم در ارتقاء HRQoL بین بیماران با و بدون بیماری همزمان یک مزیت افزایش یافته برای بیماران با بیماری همراه در DFUs که درمان نشده بودند یا به کندی درمان شده بودند نشان داد.

نتیجه گیری: بیماری همزمان نمی تواند مانع بهبود HRQoL در درمان بیماران DFU گردد. بر عکس گاهی HRQoL خیلی بیشتر در بیمارانی با بیماری واضح همزمان همراه با التیام سخت زخم ها ، بهبود می یابد.

مقدمه
تعداد افرادی که مبتلا به دیابت ملیتوس هستند به طور فزاینده ای در سرار جهان رو به افزایش است، در اروپا برآورد شیوع آن 10% در بین افراد 20 تا 70 ساله تا سال 2030 می باشد. در نتیجه ، بروز عوارض مزمن دیابت به همراه افزایش هزینه های مرتبط به بستری شدن و مدیریت آنها در بیمارستان رو به افزایش است. درحال حاضر عوارض مزمن دیابت منجربه پدیدار شدن یک همه گیری با زخم پای دیابتی به عنوان یکی از ترسناک ترین عوارض شده است. علاوه بر این DFUs با بیماری زایی و مرگ و میر بالا همراه می باشد. میزان بقای بیماران با یک DFU کمتر از بیمارنی است که از سرطان پروستات یا سرطان پستان و رنج می برند و مشابه بیماران مبتلا به سرطان کولون یا سرطان ریه می باشد.

مقاله سال 2016

Worldwide, approximately one death in eight is due to cancer. The estimated total number of cancer deaths worldwide in 2008 was 7.6 million, and the growth and aging of the population mean that this number is expected to reach 13.2 million in 2030 (American Cancer Society, 2011). It is predicted that around 585,720 people will die of cancer in the United States in 2014, a rate of about 1,600 per day (Siegel, Jiemin, Zou, & Jemal, 2014). Since billions of dollars are spent on research, treatment, prevention, and other cancer-related costs, accurate predictions ofthe numbers of cancer deaths are important for effective planning, resource allocation, and communication (Tiwari et al., 2004). Accurate predictions of the numbers of cancer deaths are beneficial for the public sector because they enable more precise allocations of health and welfare budgets. If the predicted number of deaths does not match the actual number of cancer deaths, then the planned budgets could be either too high or inefficient, leading to difficulties for governments. Accurate predictions are also critical in the private sector. For example, accurate forecasts of the numbers of cancer deaths allow insurance firms to predict the need for cancer insurance.

For these reasons, there has been a considerable amount of research in recent years relating to the forecasting of the numbers of cancer deaths using aggregate-level data. Chen et al. (2012) compared the levels of accuracyof five time series models for four-year-ahead projections of the numbers of cancer deaths. The models were as follows: (1) the state space model, (2) the Bayesian state space model (Schmidt & Pereira, 2011), (3) two versions of the joinpoint regression model (Kim, Fay, Feuer, & Midthune, 2000), (4) the Nordpred model (Møller et al., 2003), and (5) a vector autoregressive model using the Hilbert–Huang transform (Huang et al., 1998). Chen et al. (2012) showed that the joinpoint model with the modified Bayesian information criterion had the smallest error among the various models tested. Tiwari et al. (2004) improved the existing state space model and revealed that the average squared deviations across cancer sites for the new model were substantially lower than those of other benchmark models. However, so far, such models have used only aggregatelevel mortality data. On the other hand, Verdecchia, De Angelis, and Capocaccia (2002) proposed the prevalence, incidence, and analysis model (PIAMOD) for predicting numbers of deaths using incidence data. The model first fitted the incidence based on age, period, and cohort, then derived the numbers of deaths.

Some studies have used survival analysis to focus on the individual hazard or survival rate. The most prominent model in survival analysis is the proportional hazards regression model, proposed by Cox (1972). Since then, the Cox model has been used widely for specifying a linear relationship between hazard or survival rates and covariates in a variety of fields, such as engineering, economics, and sociology. In biometrics, there have been studies of methods for modeling an individual’s lifetime (Hakulinen & Tenkanen, 1987; Prentice & Kalbfleisch, 1979), which have generally involved the use of parametric models. One of the most popular of these is the Weibull model (Cox, 1972). Royston and Parmar (2002) developed flexible parametric models based initially on the assumption of a proportional hazards scaling of covariate effects. This class of models was based on a transformation of the survival function using a link function.