Magnetic resonance imaging for prospective assessment of local recurrence of non-small cell lung cancer after stereotactic body radiation therapy

Evaluation of chest CT-scans following lung stereotactic ablative radiotherapy: Challenges and new insights

Stereotactic ablative radiotherapy (SABR) is increasingly used for the treatment of early-stage non-small cell lung cancer (ES-NSCLC) and for pulmonary metastases. In patients with ES-NSCLC, SABR is highly successful with reported 5-year local control rates of approximately 90%. However, the assessment of local control following lung SABR can be challenging as radiological changes arising from radiation-induced lung injury (RILI) can be observed in up to 90% of patients. These so-called 'benign' radiological changes evolve with time and are often asymptomatic. Several radiological and metabolic features have been explored to help distinguish RILI from local recurrences (LR). These include the Response Evaluation Criteria for Solid Tumors (RECIST), high-risk features (HRF's) and maximum standardized uptake value (SUVmax) on FDG-PET-CT. However, use of some of these approaches have poor predictive values and low specificity for recurrence. A proposed new workflow for the evaluation of post-lung SABR radiological changes will be reviewed which uses the presence of so-called 'actionable radiological features' to trigger changes to imaging schedules and identifies the need for a multidisciplinary board review. Furthermore, this critical review of post-lung SABR imaging will highlight current challenges, new insights, and unknowns in this field.

Influence of abdominal fat distribution and inflammatory status on post-operative prognosis in non-small cell lung cancer patients: a retrospective cohort study

PURPOSE

To evaluate the influence of visceral fat area (VFA), subcutaneous fat area (SFA), the systemic immune-inflammation index (SII) and total inflammation-based systemic index (AISI) on the postoperative prognosis of non-small cell lung cancers (NSCLC) patients.

METHODS

266 NSCLC patients received surgery from two academic medical centers were included. To assess the effect of abdominal fat measured by computed tomography (CT) imaging and inflammatory indicators on patients' overall survival (OS) and progression-free survival (PFS), Kaplan-Meier survival analysis and Cox proportional hazards models were used.

RESULTS

Kaplan-Meier analysis showed the OS and PFS of patients in high-VFA group was better than low-VFA group (p < 0.05). AISI and SII were shown to be risk factors for OS and PFS (p < 0.05) after additional adjustment for BMI (Cox regression model II). After further adjustment for VFA (Cox regression model III), low-SFA group had longer OS (p < 0.05). Among the four subgroups based on VFA (high/low) and SFA (high/low) (p < 0.05), the high-VFA & low-SFA group had the longest median OS (108 months; 95% CI 74-117 months) and PFS (85 months; 95% CI 65-117 months), as well as the lowest SII and AISI (p < 0.05). Low-SFA was a protective factor for OS with different VFA stratification (p < 0.05).

CONCLUSION

VFA, SFA, SII and AISI may be employed as significant prognostic markers of postoperative survival in NSCLC patients. Moreover, excessive SFA levels may encourage systemic inflammation decreasing the protective impact of VFA, which may help to provide targeted nutritional support and interventions for postoperative NSCLC patients with poor prognosis.

Fusion of laser-induced breakdown spectroscopy technology and deep learning: a new method to identify malignant and benign lung tumors with high accuracy

Precisely distinguishing between malignant and benign lung tumors is pivotal for suggesting therapeutic strategies and enhancing prognosis, yet this differentiation remains a daunting task. The growth rates, metastatic potentials, and prognoses of benign and malignant tumors differ significantly. Developing specialized treatment protocols tailored to various tumor types is essential for enhancing patient survival outcomes. Employing laser-induced breakdown spectroscopy (LIBS) in conjunction with a deep learning methodology, we attained a high-precision differential diagnosis of malignant and benign lung tumors. First, LIBS spectra of malignant tumors, benign tumors, and normal tissues were collected. The spectra were preprocessed and Z score normalized. Then, the intensities of the Mg II 279.6, Mg I 285.2, Ca II 393.4, Cu II 518.3, and Na I 589.6 nm lines were analyzed in the spectra of the three tissues. The analytical results show that the elemental lines have different contents in the three tissues and can be used as a basis for distinguishing between the three tissues. Finally, the RF-1D ResNet model was constructed by combining the feature importance assessment method of random forest (RF) and one-dimensional residual network (1D ResNet). The classification accuracy, precision, sensitivity, and specificity of the RF-1D ResNet model were 91.1%, 91.6%, 91.3%, and 91.3%, respectively. And the model demonstrates superior performance with an area under the curve (AUC) value of 0.99. The above results show that combining LIBS with deep learning is an effective way to diagnose malignant and benign tumors.