This study investigated whether tracking changes over time inÌýCYFRA 21-1, aÌýserum biomarkerÌý(a substance measured in the liquid part of the blood) forÌýnon-small cell lung cancer (NSCLC), could improve the evaluation ofÌýpulmonary nodulesÌý(small growths or spots in the lungs). Most previous studies have relied on a single measurement of CYFRA 21-1, which may make it more difficult to distinguish cancerous nodules from benign (noncancerous) lung conditions. The researchers analyzed 132 patients with pulmonary nodules, with the primary analysis focusing on 121 patients who had not yet received treatment. CYFRA 21-1 levels were measured repeatedly over time to examine how they changed. At the start of the study, patients with malignant nodules had higher CYFRA 21-1 levels than those with benign nodules. Although the overall patterns of change over time were not significantly different between the two groups, malignant nodules showed greater variability and larger changes in CYFRA 21-1 levels over time. The researchers also found that a single baseline measurement of CYFRA 21-1 had moderate ability to distinguish malignant from benign nodules. The amount of change in CYFRA 21-1 over time also showed moderate diagnostic performance, with very highÌýspecificity(meaning it rarely identified benign nodules as cancer) but relatively lowÌýsensitivityÌý(meaning it missed many cancers). Overall, the findings suggest that monitoring CYFRA 21-1 over time may provide additional information beyond a single measurement when evaluating pulmonary nodules. However, larger prospective studies are needed to confirm these findings before this approach can be used routinely in clinical practice.

Fig 1.ÌýReceiver Operating Characteristic (ROC) curve for baseline CYFRA 21−1 in the discrimination of benign versus untreated malignant pulmonary nodules.

The area under the curve (AUC) was 0.676 (95% CI: 0.565–0.787). The diagonal grey line represents the reference line of no discrimination. The optimal Youden index threshold yielded a sensitivity of 0.633 and a specificity of 0.714.

When doctors find a pulmonary nodule, which is a small spot in the lung, they often try to estimate the chance that it is cancer before deciding on the next test or treatment. This study looked at how well four commonly used prediction models—the Mayo, Brock, Veterans Affairs (VA), and Peking University (PKU) models—worked in a setting where lung cancer was very common. The researchers reviewed records from 1,518 patients with nodules measuring 6 to 30 mm who were seen at ÎåÒ»²è¹Ý¶ù Medical Center or the VA Tennessee Valley Healthcare System in Nashville between 2002 and 2021. They then compared each model’s predictions with the actual diagnosis using measures of accuracy, including how well the models separated benign from malignant nodules, how well their predictions matched real outcomes, and how sensitive and specific they were at the thresholds used in guidelines. Among these patients, 1,098 nodules, or 72.3%, were cancerous. The Mayo model was the best at distinguishing cancerous from noncancerous nodules, while the Brock and VA models performed similarly overall. The VA model matched outcomes somewhat better than the others, and the PKU model had the weakest ability to separate benign from malignant nodules but the best overall match to actual risk. The main takeaway is that even when models are similarly accurate at a broad level, their usefulness can change a lot depending on how common cancer is in the patient population. Doctors should therefore consider the cancer rate in their own setting when using these models to guide decisions about lung nodules.

Figure 1ÌýFlow chart showing patient inclusion. VUMC = ÎåÒ»²è¹Ý¶ù Medical Center.

TheÌýNCCN Clinical Practice Guidelines in OncologyÌýforÌýnon-small cell lung cancer (NSCLC)Ìýprovide evidence-based recommendations to help doctors manage this common type of lung cancer. These guidelines cover the full course of care, including how the disease is diagnosed, treated initially, monitored over time (surveillance), and managed if it progresses. In this update, the expert panel has revised the list of recommendedÌýtargeted therapies—treatments designed to specifically attack cancer cells with certain genetic features—based on the latest approvals from the U.S. Food and Drug Administration (FDA) and new clinical research findings. This section of the guidelines focuses on patients withÌýadvanced or metastatic NSCLCÌý(cancer that has spread beyond the lungs) whose tumors haveÌýactionable biomarkers, meaning identifiable genetic mutations or molecular traits that can be matched with specific targeted treatments to improve outcomes.

Figure 1.

NSCL-19. NCCN Clinical Practice Guidelines in Oncology for Non–Small Cell Lung Cancer, Version 4.2026.

]]> /valiant/2025/09/26/early-adipose-tissue-wasting-in-a-preclinical-model-of-human-lung-cancer-cachexia/ Fri, 26 Sep 2025 19:55:59 +0000 /valiant/?p=5132 Snoke, Deena B., van der Velden, Jos L.J.L., Bellafleur, Emma R., Dearborn, Jacob S., Lenahan, Sean M., Beal, Alexandra E., Aboushousha, Reem, Heininger, Skyler C.J., Ather, Jennifer L., & Mank, Madeleine M. (2025). Cell Reports, 44(9), 116278.

This study focuses on cancer cachexia (CC), a condition where patients lose skeletal muscle and fat, which makes them less responsive to treatments and increases the risk of death. There are currently no approved treatments for CC, partly because animal models often do not accurately reflect human disease. To create a more relevant model for lung cancer–related CC, researchers developed mice with a specific genetic mutation in lung epithelial cells (KrasG12D/+, called G12D mice). These mice gradually develop CC and show tissue, cellular, genetic, and metabolic changes similar to those seen in humans with lung CC. One early sign in these mice is fat loss, a feature also observed in many CC models and in lung cancer patients. The study found that factors released by tumors trigger fat breakdown, which drives the loss of adipose tissue, and that this fat loss is not directly proportional to tumor size. Overall, G12D mice replicate important aspects of human lung CC and reveal that early tumor-driven changes in fat metabolism play a key role in the development of cachexia.

Figure 1

ÌýCharacterization of lung epithelial cell-specific, inducibleÌýKras^G12D/+Ìý(G12D) mice at 12 weeks post-induction

Large community health studies are valuable tools for understanding lung cancer, helping researchers explore risk factors and build models to predict who might develop the disease. To make the most of this data, a reliable method is needed to identify cases of lung cancer and lung spots known as pulmonary nodules, and to link various types of health information collected over time from electronic health records (EHRs). In this study, researchers used medical coding systems, including SNOMED and ICD codes, to create rules for identifying patients with lung cancer or pulmonary nodules in EHR data. They also applied clinical expertise to determine appropriate timeframes for gathering related health and imaging data. Using this approach, they curated three patient groups, or cohorts, with pulmonary nodules and repeated imaging records from ÎåÒ»²è¹Ý¶ù Medical Center.

The method proved highly accurate, correctly identifying lung cancer in 93% of cases (sensitivity) and correctly identifying those without lung cancer in 99.6% of cases (specificity). It also showed high reliability in predicting who truly had or didn’t have lung cancer, based on the data. This study presents an effective and scalable strategy for organizing long-term, multi-type health data about individuals at risk for lung cancer, using routinely collected information from medical records.

Figure 1. Archives linking EHRs to imaging allowed for the selection of subjects via ICD rules. Scans that were low quality and data that did not fall within observation windows were excluded. VU-SPN: subjects with no cancer history prior to an SPN code. VU-LI-SPN: subjects in VU-SPN with imaging. VU-LI-Incidence: subjects with imaging.

There is growing interest in how long cancer has existed and why malignant tumors, especially in soft tissues, seem rare in ancient human remains. To explore this, researchers carefully examined 45 whole-body CT scans of ancient Egyptian mummies to look for bone and soft tissue tumors. They found evidence of malignant bone disease (likely cancer spread to the bones) in 1 out of 45 cases (2%). In addition, 5 out of 45 mummies (11%) showed soft tissue masses that were likely cancerous. These soft tissue tumors had clear edges, different internal patterns, and were denser than the nearby preserved soft tissues. In two cases where soft tissue tumors were inside the abdomen, the original organs were not preserved. In summary, malignant tumors, including those in soft tissues, can be detected using CT scans of ancient Egyptian mummies. This discovery about how these tumors appear and how often they occur provides new information and a fresh way to study cancer in ancient populations.

Fig 1

Case 16, probable skeletal metastases and large intra-abdominal soft tissue mass. (A) Axial multiplanar (MPR) reconstruction of the skull illustrating multiple predominantly small osteolytic lesions of the cranial vault involving the outer and inner table as well as the diploe. (B) Sagittal MPR of the cervical and thoracic spine demonstrating multiple osteolytic lesions in the vertebral bodies, spinous processes and the sternum. The second thoracic vertebra reveals a healed burst fracture with collapse, the first and third thoracic vertebrae show infraction of the adjacent endplates. (C) Sagittal MPR of the lumbar spine and the sacrum showing multiple osteolytic lesions. Note the cachectic body and the textiles that overly the lumbar spine and protrude into the relatively empty pelvic cavity (asterisk). (D) Axial MPR of the upper abdomen illustrates a large soft tissue mass that expands from the midline towards the left dorsolateral part of the abdomen (arrows). In the midline, the mass is relatively homogeneous, in the lateral part, it shows different components/layers with stratified, loosened structure. The mass appears to remodel the pancreas body and tail. (E) Axial MPR of the middle abdomen demonstrating the lower part of the mass with different components/layers (arrows). (F) Coronal MPR of the upper abdomen illustrating the soft tissues mass with irregular contour of the upper margin (arrows). Note preservation of shrunken lungs bilaterally. Unfortunately, the CT scan was sectioned at the level of the soft tissue mass. (G) Coronal MPR of the middle and lower abdomen showing the mass with a horizontal postmortem split due to desiccation (arrow). There is increased adjacent soft tissue around the lower margin (asterisk).

Lung nodules—small spots that appear on lung scans—are often biopsied to check for cancer. However, many of these nodules turn out to be harmless. The Lung Cancer Prediction (LCP) score is a deep learning tool that analyzes CT scans and has been shown to work well in identifying whether a nodule might be cancerous when it’s found by chance. But it hasn’t yet been tested in situations where doctors have already recommended a biopsy.

In this study, researchers looked at lung nodules that had already been biopsied at a large medical center. They used the Mayo Clinic’s traditional prediction model to estimate how likely each nodule was to be cancerous, dividing them into low, medium, or high risk using guidelines from the British Thoracic Society. Then, they compared how well three different models could predict cancer: the Mayo model, the LCP radiomic model, and a newÌýintegrated modelÌýthat combined the LCP score with key clinical details like the patient’s age, whether the nodule had spiky edges (spiculation), and whether it was located in the upper part of the lung.

The study included 321 nodules total—196 cancerous and 125 benign (non-cancerous). The Mayo model had an accuracy score (AUC) of 0.69, the LCP model had a similar score of 0.67, but theÌýintegrated modelÌýperformed best with an AUC of 0.75. It also had a better F1 score, which balances how well the model correctly identifies cancer and avoids false alarms. Importantly, the integrated model correctly reclassified 8 benign nodules from medium to low risk, meaning those patients might have avoided a biopsy—andÌýno cancer cases were mistakenly downgraded.

In summary, combining the LCP deep learning score with a few key patient details improved the ability to predict whether lung nodules were cancerous. This approach may help reduce the number of people who undergo unnecessary, invasive lung biopsies.

Figure 1

Receiver operating characteristic (ROC) curves for all models. AUC, area under the receiver operating characteristic curve; LCP, Lung Cancer Prediction score; Mayo, Mayo model; Mayo Select, Mayo model excluding all radiographic variables; ROC, receiver operating characteristic.