Prediction of immunochemotherapy response for diffuse large B-cell lymphoma using artificial intelligence digital pathology.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), with their immunochemotherapy response labels.

A comparison between centralized and asynchronous federated learning approaches for survival outcome prediction using clinical and PET data from non-small cell lung cancer patients.

Background And Objective: Survival analysis plays an essential role in the medical field for optimal treatment decision-making. Recently, survival analysis based on the deep learning (DL) approach has been proposed and is demonstrating promising results. However, developing an ideal prediction model requires integrating large datasets across multiple institutions, which poses challenges concerning medical data privacy.

Ensemble Deep Learning Model to Predict Lymphovascular Invasion in Gastric Cancer.

Lymphovascular invasion (LVI) is one of the most important prognostic factors in gastric cancer as it indicates a higher likelihood of lymph node metastasis and poorer overall outcome for the patient. Despite its importance, the detection of LVI(+) in histopathology specimens of gastric cancer can be a challenging task for pathologists as invasion can be subtle and difficult to discern. Herein, we propose a deep learning-based LVI(+) detection method using H&E-stained whole-slide images.

Prognostic Significance Of Sequential 18f-fdg Pet/Ct During Frontline Treatment Of Peripheral T Cell Lymphomas.

Background/aims: The prognostic significance of 18F-fluorodeoxyglucose (FDG)-positron emission tomography-computed tomography (PET/CT) in peripheral T-cell lymphomas (PTCLs) are controversial. We explored the prognostic impact of sequential 18F-FDG PET/CT during frontline chemotherapy of patients with PTCLs.

Methods: In total, 143 patients with newly diagnosed PTCLs were included.

Survival Prediction Using Transformer-Based Categorical Feature Representation in the Treatment of Diffuse Large B-Cell Lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is a common and aggressive subtype of lymphoma, and accurate survival prediction is crucial for treatment decisions. This study aims to develop a robust survival prediction strategy to integrate various risk factors effectively, including clinical risk factors and Deauville scores in positron-emission tomography/computed tomography at different treatment stages using a deep-learning-based approach. We conduct a multi-institutional study on 604 DLBCL patients' clinical data and validate the model on 220 patients from an independent institution.

Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients.

Background: Lung cancer is the leading cause of cancer-related deaths worldwide. The majority of lung cancers are non-small cell lung cancer (NSCLC), accounting for approximately 85% of all lung cancer types. The Cox proportional hazards model (CPH), which is the standard method for survival analysis, has several limitations.

Molecular imaging approaches to facilitate bacteria-mediated cancer therapy.

Bacteria-mediated cancer therapy is a potential therapeutic strategy for cancer that has unique properties, including broad tumor-targeting ability, various administration routes, the flexibility of delivery, and facilitating the host's immune responses. The molecular imaging of bacteria-mediated cancer therapy allows the therapeutically injected bacteria to be visualized and confirms the accurate delivery of the therapeutic bacteria to the target lesion. Several hurdles make bacteria-specific imaging challenging, including the need to discriminate therapeutic bacterial infection from inflammation or other pathologic lesions.

In vivo imaging of invasive aspergillosis with F-fluorodeoxysorbitol positron emission tomography.

Invasive aspergillosis is a critical complication in immunocompromised patients with hematologic malignancies or with viral pneumonia caused by influenza virus or SARS‑CoV‑2. Although early and accurate diagnosis of invasive aspergillosis can maximize clinical outcomes, current diagnostic methods are time-consuming and poorly sensitive. Here, we assess the ability of 2-deoxy-2-F-fluorosorbitol (F-FDS) positron emission tomography (PET) to specifically and noninvasively detect Aspergillus infections.

Precise characterization of a solitary pulmonary nodule using tumor shadow disappearance rate-corrected F-18 FDG PET and enhanced CT.

We aimed to characterize solitary pulmonary nodule (SPN) using imaging parameters for F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) or enhanced CT corrected by tumor shadow disappearance rate (TDR) to reflect the tissue density.We enrolled 51 patients with an SPN who underwent PET/CT and chest CT with enhancement. The FDG uptake of SPN was evaluated using maximum standardized uptake value (SUVmax) on PET/CT.

Bacteria and bacterial derivatives as delivery carriers for immunotherapy.

There is growing interest in the role of microorganisms in human health and disease, with evidence showing that new types of biotherapy using engineered bacterial therapeutics, including bacterial derivatives, can address specific mechanisms of disease. The complex interactions between microorganisms and metabolic/immunologic pathways underlie many diseases with unmet medical needs, suggesting that targeting these interactions may improve patient treatment. Using tools from synthetic biology and chemical engineering, non-pathogenic bacteria or bacterial products can be programmed and designed to sense and respond to environmental signals to deliver therapeutic effectors.

Effects of Multiple Filters on Liver Tumor Segmentation From CT Images.

Segmentation of liver tumors from Computerized Tomography (CT) images remains a challenge due to the natural variation in tumor shape and structure as well as the noise in CT images. A key assumption is that the performance of liver tumor segmentation depends on the characteristics of multiple features extracted from multiple filters. In this paper, we design an enhanced approach based on a two-class (liver, tumor) convolutional neural network that discriminates tumor as well as liver from CT images.

Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm.

One essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT). Many recent studies have focused on several organs such as the lung, heart, esophagus, trachea, liver, aorta, kidney, and prostate. However, among the above organs, the esophagus is one of the most difficult organs to segment because of its small size, ambiguous boundary, and very low contrast in CT images.

Change of Therapeutic Response Classification According to Recombinant Human Thyrotropin-Stimulated Thyroglobulin Measured at Different Time Points in Papillary Thyroid Carcinoma.

Purpose: We investigated whether response classification after total thyroidectomy and radioactive iodine (RAI) therapy could be affected by serum levels of recombinant human thyrotropin (rhTSH)-stimulated thyroglobulin (Tg) measured at different time points in a follow-up of patients with papillary thyroid carcinoma (PTC).

Methods: A total of 147 PTC patients underwent serum Tg measurement for response assessment 6 to 24 months after the first RAI therapy. Serum Tg levels were measured at 24 h (D1Tg) and 48-72 h (D2-3Tg) after the 2 injection of rhTSH.

Correction to: Recent Progress in the Molecular Imaging of Tumor-Treating Bacteria.

[This corrects the article DOI: 10.1007/s13139-021-00689-4.].

Survival time prediction by integrating cox proportional hazards network and distribution function network.

Background: The Cox proportional hazards model is commonly used to predict hazard ratio, which is the risk or probability of occurrence of an event of interest. However, the Cox proportional hazard model cannot directly generate an individual survival time. To do this, the survival analysis in the Cox model converts the hazard ratio to survival times through distributions such as the exponential, Weibull, Gompertz or log-normal distributions.

Recent Progress in the Molecular Imaging of Tumor-Treating Bacteria.

Bacterial cancer therapy (BCT) approaches have been extensively investigated because bacteria can show unique features of strong tropism for cancer, proliferation inside tumors, and antitumor immunity, while bacteria are also possible agents for drug delivery. Despite the rapidly increasing number of preclinical studies using BCT to overcome the limitations of conventional cancer treatments, very few BCT studies have advanced to clinical trials. In patients undergoing BCT, the precise localization and quantification of bacterial density in different body locations is important; however, most clinical trials have used subjective clinical signs and invasive sampling to confirm bacterial colonization.

Differences in diagnostic impact of dual-tracer PET/computed tomography according to the extrahepatic metastatic site in patients with hepatocellular carcinoma.

Objectives: We compared the diagnostic performance of C-11 acetate and F-18 fluorodeoxyglucose (FDG) PET/computed tomography (CT) for the detection of extrahepatic metastasis in patients with hepatocellular carcinoma (HCC) and evaluated whether the improvement in the diagnostic performance of dual tracer PET/CT differs by the metastatic site.

Methods: Fifty-eight patients who had extrahepatic metastasis on either C-11 acetate or F-18 FDG PET/CT were enrolled, and 193 metastatic lesions were analyzed in this retrospective study. The metastatic lesions were categorized based on six sites of involvement.

Prognostic impact of F-FDG PET/CT in patients with multiple myeloma presenting with renal impairment.

Renal insufficiency (RI) is a frequent manifestation of multiple myeloma (MM) at time of diagnosis but there is no reliable prognostic factor for patients with MM presenting with RI. This study investigated the prognostic impact of F-fluorodeoxyglucose (F-FDG) positron emission tomography/computed tomography (PET/CT) in patients with MM with RI at diagnosis. The records of 209 patients with MM between June 2011 and November 2018 were retrospectively analyzed.

Clinical Impact of F-18 FDG PET-CT on Biopsy Site Selection in Patients with Suspected Bone Metastasis of Unknown Primary Site.

Purpose: We investigated the clinical role of F-18 fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET-CT) in the identification of the primary site and the selection of the optimal biopsy site in patients with suspected bone metastasis of unknown primary site.

Methods: The patients with suspected bone metastasis who underwent PET-CT for evaluation of primary site were enrolled in this study. The primary sites were identified by the histopathologic or imaging studies and were classified according to the FDG uptake positivity of the primary site.

Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board.

Background: IBM Watson for Oncology (WFO) is a cognitive computing system helping physicians quickly identify key information in a patient's medical record, surface relevant evidence, and explore treatment options. This study assessed the possibility of using WFO for clinical treatment in lung cancer patients.

Methods: We evaluated the level of agreement between WFO and multidisciplinary team (MDT) for lung cancer.

Ultrasensitive detection of malignant melanoma using PET molecular imaging probes.

Malignant melanoma has one of the highest mortality rates of any cancer because of its aggressive nature and high metastatic potential. Clinical staging of the disease at the time of diagnosis is very important for the prognosis and outcome of melanoma treatment. In this study, we designed and synthesized the F-labeled pyridine-based benzamide derivatives -(2-(dimethylamino)ethyl)-5-[F]fluoropicolinamide ([F]DMPY2) and -(2-(dimethylamino)ethyl)-6-[F]fluoronicotinamide ([F]DMPY3) to detect primary and metastatic melanoma at an early stage and evaluated their performance in this task.

Combined role of lymph node ratio and serum thyroglobulin levels in predicting prognosis of papillary thyroid carcinoma.

Objectives: The aim of this study was to evaluate a prognostic value of the extent of metastatic lymph node combined with TSH-stimulated serum thyroglobulin (sTg) measured just before radioactive iodine (RAI) therapy in patients with papillary thyroid cancer (PTC).

Methods: The retrospective study included 468 patients with PTC who underwent total thyroidectomy with neck dissection and postoperative RAI therapy. The extent of metastatic lymph node was evaluated with the lymph node ratio (LNR) which was defined as the number of metastatic lymph nodes out of the number of total removed lymph nodes.

Imaging of tumor colonization by using F-FDS PET.

Tumor-targeting bacteria have been actively investigated as a new therapeutic tool for solid tumors. However, imaging of tumor-targeting bacteria has not been fully established. F-fluorodeoxysorbitol (FDS) positron emission tomography (PET) is known to be capable of imaging Gram-negative Enterobacteriaceae infection.

Optimization of Predictive Performance for the Therapeutic Response Using Iodine Scan-Corrected Serum Thyroglobulin in Patients with Differentiated Thyroid Carcinoma.

We investigated whether the performance of serum thyroglobulin (Tg) for response prediction could be improved based on the iodine uptake pattern on the post-therapeutic I-131 whole body scan (RxWBS) and the degree of thyroid tissue damage with radioactive iodine (RAI) therapy. A total of 319 patients with differentiated thyroid carcinoma who underwent total thyroidectomy and RAI therapy were included. Based on the presence/absence of focal uptake at the anterior midline of the neck above the thyroidectomy bed on RxWBS, patients were classified into positive and negative uptake groups.