[Quantitative Imaging Assessment of Tumor Response to Chemoradiation in Lung Cancer]

Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences

The aim of this study was to develop a medical imaging and comprehensive stacked learning-based method for predicting high- and low-risk thymoma. A total of 126 patients with thymomas and 5 patients with thymic carcinoma treated at our institution, including 65 low-risk patients and 66 high-risk patients, were retrospectively recruited. Among them, 78 patients composed the training cohort, while the remaining 53 patients formed the validation cohort. We extracted 1702 features each from the patients' arterial-, venous-, and plain-phase images. Pairwise subtraction of these features yielded 1702 arterial-venous, arterial-plain, and venous-plain difference features each. The Mann‒Whitney U test and least absolute shrinkage and selection operator (LASSO) and SelectKBest methods were employed to select the best features from the training set. Six models were built with a stacked learning algorithm. By applying stacked ensemble learning, three machine learning algorithms (XGBoost, multilayer perceptron (MLP), and random forest) were combined by XGBoost to produce the the six basic imaging models. Then, the XGBoost algorithm was applied to the six basic imaging models to construct a combined radiomic model. Finally, the radiomic model was combined with clinical information to create a nomogram that could easily be used in clinical practice to predict the thymoma risk category. The areas under the curve (AUCs) of the combined radiomic model in the training and validation cohorts were 0.999 (95% CI 0.988-1.000) and 0.967 (95% CI 0.916-1.000), respectively, while those of the nomogram were 0.999 (95% CI 0.996-1.000) and 0.983 (95% CI 0.990-1.000). This study describes the application of CT-based radiomics in thymoma patients and proposes a nomogram for predicting the risk category for this disease, which could be advantageous for clinical decision-making for affected patients.

Impact of Computer-Aided CT and PET Analysis on Non-invasive T Staging in Patients with Lung Cancer and Atelectasis

PURPOSE

Tumor delineation within an atelectasis in lung cancer patients is not always accurate. When T staging is done by integrated 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG)-positron emission tomography (PET)/X-ray computer tomography (CT), tumors of neuroendocrine differentiation and slowly growing tumors can present with reduced FDG uptake, thus aggravating an exact T staging. In order to further exhaust information derived from [¹⁸F]FDG-PET/CT, we evaluated the impact of CT density and maximum standardized uptake value (SUVmax) for the classification of different tumor subtypes within a surrounding atelectasis, as well as possible cutoff values for the differentiation between the primary tumor and atelectatic lung tissue.

PROCEDURES

Seventy-two patients with histologically proven lung cancer and adjacent atelectasis were investigated. Non-contrast-enhanced [¹⁸F]FDG-PET/CT was performed within 2 weeks before surgery/biopsy. Boundaries of the primary within the atelectasis were determined visually on the basis of [¹⁸F]FDG uptake; CT density was quantified manually within each primary and each atelectasis.

RESULTS

CT density of the primary (36.4 Hounsfield units (HU) ± 6.2) was significantly higher compared to that of atelectatic lung (24.3 HU ± 8.3; p < 0.01), irrespective of the histological subtype. The discrimination between different malignant tumors using density analysis failed. SUVmax was increased in squamous cell carcinomas compared to adenocarcinomas. Irrespective of the malignant subtype, a possible cutoff value of 24 HU may help to exclude the presence of a primary in lesions below 24 HU, whereas a density above a threshold of 40 HU can help to exclude atelectatic lung.

CONCLUSION

Density measurements in patients with lung cancer and surrounding atelectasis may help to delineate the primary tumor, irrespective of the specific lung cancer subtype. This could improve T staging and radiation treatment planning (RTP) without additional application of a contrast agent in CT, or an additional magnetic resonance imaging (MRI), even in cases of lung tumors of neuroendocrine differentiation or in slowly growing tumors with less avidity to [¹⁸F]FDG.