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Abstract Spontaneous regression of non‐small cell lung cancer is relatively rare. Here, we present a very rare case of spontaneous regression of lung cancer which occurred in a patient with basaloid squamous cell lung cancer. To the best of our knowledge, this is the first report of such a case. A 76‐year old man was referred to our hospital with nodules in the right upper lobe determined by chest computed tomography. The nodules spontaneously regressed during follow‐up. Two years later, the tumor had regrown and the patient subsequently underwent surgery. The pathological findings showed basaloid squamous cell carcinoma. Stimulation of the immune system was considered to be the cause of the spontaneous regression and CD‐8 positive and CD‐4 positive lymphocytes might play an important role.

In an effort to tackle environmental problems, sustainable policies that have a low environmental impact on the local community are being implemented in many countries, including Japan. One approach is the smart community, which is a policy to evaluate the cost of the entire community, considering each interaction while dividing society into seven fields: power, gas, water, railway, industry, business, and home models. In this study, we evaluate and optimize the home model. We develop an optimization model for the operation plan of energy storage equipment in a residential building as part of a smart community. The purpose of this model is to improve electricity costs from the demand profile and provide stable power supply from the supply profile. Therefore, this model controls the operation of energy storage equipment and also performs load leveling. It is intended to reduce the total power consumption during peak hours by operating the energy storage equipment at night, when the usage of other electrical appliances is low. A stochastic programming model is formulated using scenarios that represent the uncertainty of power and heat demand. This formulation assumes continuous operation in a residential building. We demonstrate the usefulness of the stochastic programming model by comparing it with the deterministic model. As an economic assessment, we compare the daily beneficial expense of the existing model with our new model of daily operations by following the improvement factors. Our model not only lowers peak total power consumption but also achieves load leveling. The total operating time of energy storage equipment is also reduced.

We consider the design of distribution network using power flow controller. In power delivery systems, the use of dispersed generation and security control to improve network utilization requires the optimal use of system control devices. The installation of loop controller allows the distribution system to operate in a loop configuration, achieving effective management of voltage and power flow. In the investment planning process, it is important to identify the optimal location and installed capacity of the equipment such that all operational constraints are satisfied. This paper presents a method for identifying the optimal location and capacity with the minimum installation cost. Our novel approach uses an economic model that considers the fixed costs. A slope scaling procedure is presented, and its efficiency is demonstrated using numerical experiments.

We consider a capacity expansion problem for a railway network under uncertainty. In our approach, integer and stochastic programming provide a basic framework. We develop a multistage stochastic programming model in which some of the variables are restricted to integer values. Given the distribution of the number of customers in a scenario, the problem of minimizing the expected value of the total investment cost is considered. The problem is reformulated as a problem with first stage integer variables and continuous second stage variables. An L-shaped algorithm is proposed to solve this problem.

Robotic surgery with carbon dioxide (CO2) insufflation to the thorax is frequently performed to gain a better operative field of view, although its intraoperative complications have not yet been discussed in detail. We treated two patients with difficult ventilation caused by distal migration of a double-lumen endotracheal tube (DLT) during robotic thymectomy. In the first case, migration of the DLT during one-lung ventilation (OLV) occurred after CO2 insufflation to the bilateral thoraxes was started. Oxygenation rapidly deteriorated because dependent lung expansion was restricted by CO2 insufflation. In the second case, migration of the DLT during OLV occurred while CO2 insufflation to a unilateral thorax and mediastinum was performed. In both cases, once migration of the DLT during OLV occurred with CO2 insufflation, readjusting the DLT became very difficult because our manipulation of bronchofiberscopy was prevented by the robot arms located above the patient’s head and because deformation of the trachea/bronchus induced by CO2 insufflation caused a poor image of the bronchofiberscopic view. Thus, during robotic-assisted thoracoscopic surgery with CO2 insufflation, since there is a potential risk of difficult ventilation with a DLT and since readjustment of the DLT is very difficult, discontinuing CO2 insufflation and switching to double-lung ventilation are needed in such a situation.

We consider the allocation of a running time supplement to a railway timetable. Previously, Vekas et al. examined the optimal way to allocate the running time supplement. The uncertain disturbances in a railway were modeled using random variables. In their model, it was assumed that there was an upper limit to the total supplement, but its allocation was not restricted. In this paper, we suggest an improvement to the previous model and present a new stochastic programming model in which there is a constraint on the running time supplement allocated to each trip to minimize the expected delay. Then a solution algorithm to solve the problem is developed. In the previous model, allocation of the running time supplement was biased because it was not allocated to all trips. We balance the amounts of supplements for trips by adding upper and lower bounds. The uctuations of the supplements for trips become small, and the probability of a delay decreases using our new model. Then the calculation times using the L-shaped algorithm and the former method solving a deterministic equivalent of large-scale problems are compared. It is shown that the large-scale problems can be solved effectively by using the L-shaped method.

OBJECTIVES: Pulmonary ground-glass nodules (GGNs) are occasionally diagnosed as invasive adenocarcinomas. This study aimed to evaluate the clinicopathological features of patients with pulmonary GGNs to identify factors predictive of pathological invasion. METHODS: We retrospectively evaluated 101 pulmonary GGNs resected between July 2006 and November 2013 and pathologically classified them as adenocarcinoma in situ (AIS; n = 47), minimally invasive adenocarcinoma (MIA; n = 30), or invasive adenocarcinoma (I-ADC; n = 24). The age, sex, smoking history, tumor size, and computed tomography (CT) attenuation of the 3 groups were compared. Receiver operating characteristic (ROC) curve analyses were performed to identify factors that could predict the presence of pathologically invasive adenocarcinomas. RESULTS: Tumor size was significantly larger in the MIA and I-ADC groups than in the AIS group. CT attenuation was significantly greater in the I-ADC group than in the AIS and MIA groups. In ROC curve analyses, the sensitivity and specificity of tumor size (cutoff, 11 mm) were 95.8% and 46.8%, respectively, and those for CT attenuation (cutoff, -680 HU) were 95.8% and 35.1%, respectively; the areas under the curve (AUC) were 0.75 and 0.77, respectively. A combination of tumor size and CT attenuation (cutoffs of 11 mm and -680 HU for tumor size and CT attenuation, respectively) yielded in a sensitivity and specificity of 91.7% and 71.4%, respectively, with an AUC of 0.82. CONCLUSIONS: Tumor size and CT attenuation were predictive factors of pathological invasiveness for pulmonary GGNs. Use of a combination of tumor size and CT attenuation facilitated more accurate prediction of invasive adenocarcinoma than the use of these factors independently.

Aims: In Japan, cancer is the most prevalent cause of death; the number of patients suffering from cancer is increasing. Hence, there is an increased burden on pathologists to make diagnoses. To reduce pathologists′ burden, researchers have developed methods of auto-pathological diagnosis. However, virtual slides, which are created when glass slides are digitally scanned, saved in a unique format, and it is difficult for researchers to work on the virtual slides for developing their own image processing method. This paper presents the support system for pathologists and researchers who use auto-pathological diagnosis (P-SSD). Main purpose of P-SSD was to support both of pathologists and researchers. P-SSD consists of several sub-functions that make it easy not only for pathologists to screen pathological images, double-check their diagnoses, and reduce unimportant image data but also for researchers to develop and apply their original image-processing techniques to pathological images. Methods: We originally developed P-SSD to support both pathologists and researchers developing auto-pathological diagnoses systems. Current version of P-SSD consists of five main functions as follows: (i) Loading virtual slides, (ii) making a supervised database, (iii) learning image features, (iv) detecting cancerous areas, (v) displaying results of detection. Results: P-SSD reduces computer memory size random access memory utilization and the processing time required to divide the virtual slides into the smaller-size images compared with other similar software. The maximum observed reduction in computer memory size and reduction in processing time is 97% and 99.94%, respectively. Conclusions: Unlike other vendor-developed software, P-SSD has interoperability and is capable of handling virtual slides in several formats. Therefore, P-SSD can support both of pathologists and researchers, and has many potential applications in both pathological diagnosis and research area.