Renal Cell Carcinoma With Pulmonary Metastasis and Metachronous Non-Small Cell Lung Cancer

Pulmonary lesion after surgery for renal cancer: progression or new primary?

PURPOSE

To investigate clinical and radiological differences between kidney metastases to the lung (RCCM +) and metachronous lung cancer (LC) detected during follow-up in patients surgically treated for Renal Cell Carcinoma (RCC).

METHODS

cM0 surgically-treated RCC who harbored a pulmonary mass during follow-up were retrospectively scrutinized. Univariate logistic regression assessed predictive features for differentiating between LC and RCCM + . Multivariable analyses (MVA) were fitted to predict factors that could influence time between detection and histological diagnosis of the pulmonary mass, and how this interval could impact on survivals.

RESULTS

87% had RCCM + and 13% had LC. LC were more likely to have smoking history (75% vs. 29%, p < 0.001) and less aggressive RCC features (cT1-2: 94% vs. 65%, p = 0.01; pT1-2: 88% vs. 41%, p = 0.02; G1-2: 88% vs. 37%, p < 0.001). The median interval between RCC surgery and lung mass detection was longer between LC (55 months [32.8-107.2] vs. 20 months [9.0-45.0], p = 0.01). RCCM + had a higher likelihood of multiple (3[1-4] vs. 1[1-1], p < 0.001) and bilateral (51% vs. 6%, p = 0.002) pulmonary nodules, whereas LC usually presented with a solitary pulmonary nodule, less than 20 mm. Univariate analyses revealed that smoking history (OR:0.79; 95% CI 0.70-0.89; p < 0.001) and interval between RCC surgery and lung mass detection (OR:0.99; 95% CI 0.97-1.00; p = 0.002) predicted a higher risk of LC. Conversely, size (OR:1.02; 95% CI 1.01-1.04; p = 0.003), clinical stage (OR:1.14; 95% CI 1.06-1.23; p < 0.001), pathological stage (OR:1.14; 95% CI 1.07-1.22; p < 0.001), grade (OR:1.15; 95% CI 1.07-1.23; p < 0.001), presence of necrosis (OR:1.17; 95% CI 1.04-1.32; p = 0.01), and lymphovascular invasion (OR:1.18; 95% CI 1.01-1.37; p = 0.03) of primary RCC predicted a higher risk of RCCM + . Furthermore, number (OR:1.08; 95% CI 1.04-1.12; p < 0.001) and bilaterality (OR:1.23; 95% CI 1.09-1.38; p < 0.001) of pulmonary lesions predicted a higher risk of RCCM + . Survival analysis showed a median second PFS of 10.9 years (95% CI 3.3-not reached) for LC and a 3.8 years (95% CI 3.2-8.4) for RCCM + . The median OS time was 6.5 years (95% CI 4.4-not reached) for LC and 6 years (95% CI 4.3-11.6) for RCCM + .

CONCLUSIONS

Smoking history, primary grade and stage of RCC, interval between RCC surgery and lung mass detection, and number of pulmonary lesions appear to be the most valuable predictors for differentiating new primary lung cancer from RCC progression.

A clinical prediction model for predicting the risk of liver metastasis from renal cell carcinoma based on machine learning

Background

Renal cell carcinoma (RCC) is a highly metastatic urological cancer. RCC with liver metastasis (LM) carries a dismal prognosis. The objective of this study is to develop a machine learning (ML) model that predicts the risk of RCC with LM, which is used to assist clinical treatment.

Methods

The retrospective study data of 42,547 patients with RCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. ML includes algorithmic methods and is a fast-rising field that has been widely used in the biomedical field. Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), random forest (RF), decision tree (DT), and naive Bayesian model [Naive Bayes Classifier (NBC)] were applied to develop prediction models to predict the risk of RCC with LM. The six models were 10-fold cross-validated, and the best-performing model was selected based on the area under the curve (AUC) value. A web online calculator was constructed based on the best ML model.

Results

Bone metastasis, lung metastasis, grade, T stage, N stage, and tumor size were independent risk factors for the development of RCC with LM by multivariate regression analysis. In addition, the correlation of the relative proportions of the six clinical variables was shown by a heat map. In the prediction models of RCC with LM, the mean AUC of the XGB model among the six ML algorithms was 0.947. Based on the XGB model, the web calculator (https://share.streamlit.io/liuwencai4/renal_liver/main/renal_liver.py) was developed to evaluate the risk of RCC with LM.

Conclusions

This XGB model has the best predictive effect on RCC with LM. The web calculator constructed based on the XGB model has great potential for clinicians to make clinical decisions and improve the prognosis of RCC patients with LM.

Tumor-to-Tumor Metastasis: Lung Adenocarcinoma as a Recipient of Metastasis from Renal Cell Carcinoma: A Case Report

BACKGROUND The occurrence of metastasis from one neoplasm to another is known as tumor-to-tumor metastasis (TTM). It is a rare phenomenon in the natural history of any neoplasm, with approximately 100 cases reported in the literature to date. The lungs are the most frequent metastatic tumor donors and kidney cancer is the most common recipient. However, the opposite phenomenon (lung adenocarcinoma as a recipient of metastasis from renal carcinoma) has not been previously reported in the literature. CASE REPORT We present the case of a man with a history of multiple neoplasms. He had a diffuse large B-cell lymphoma in 2006, a left papillary renal cell carcinoma (RCC) type 2 in 2006, and an acinar adenocarcinoma of the prostate in 2011. A follow-up computed tomography scan in July 2019 showed a suspicious lung nodule on the left upper lobe and a retroperitoneal hypermetabolic mass on the positron emission tomography scan. The lung nodule and retroperitoneal mass biopsies were consistent with a primary lung adenocarcinoma with a lepidic pattern and a metastatic RCC, respectively. In January 2020, he underwent a thoracoscopic left upper lobectomy and a mediastinal lymph node dissection. Histopathological evaluation revealed a 2-cm nodule composed of a lung adenocarcinoma with an intratumoral metastasis from a papillary RCC. To date, the patient has stable renal neoplastic metastatic disease and no locoregional recurrences of the lung adenocarcinoma. CONCLUSIONS Metastasis from one primary tumor to another primary tumor is an extremely unusual event. We report one of the first cases of an RCC metastasis to a primary lung adenocarcinoma.

Cavitating pulmonary metastases from a renal cell carcinoma

Cavitary lung lesions are quite common findings on chest imaging and often pose a diagnostic challenge to the clinicians. We describe a case of a 75-year-old male who presented to the emergency room with hemoptysis. Computed tomography of the chest demonstrated multiple cavitary pulmonary nodules with peripheral groundglass opacities. Bronchoscopy did not reveal any active bleeding source, and washings were negative for malignancy and infectious cause. Computed Tomography guided biopsy of the left lung nodule showed metastatic carcinoma consistent with papillary renal cell carcinoma. This case highlights the unusual presentation of metastatic renal cell carcinoma.

Imaging and Screening of Kidney Cancer

Renal cell carcinoma (RCC) exhibits a diverse and heterogeneous disease spectrum, but insight into its molecular biology has provided an improved understanding of potential risk factors, oncologic behavior, and imaging features. Computed tomography (CT) and MR imaging may allow the identification and preoperative subtyping of RCC and assessment of a response to various therapies. Active surveillance is a viable management option in some patients and has provided further insight into the natural history of RCC, including the favorable prognosis of cystic neoplasms. This article reviews CT and MR imaging in RCC and the role of screening in selected high-risk populations.